Databricks Data Science And Engineering Workspace

  databricks data science and engineering workspace: Mastering Data Engineering and Analytics with Databricks Manoj Kumar, 2024-09-30 TAGLINE Master Databricks to Transform Data into Strategic Insights for Tomorrow’s Business Challenges KEY FEATURES ● Combines theory with practical steps to master Databricks, Delta Lake, and MLflow. ● Real-world examples from FMCG and CPG sectors demonstrate Databricks in action. ● Covers real-time data processing, ML integration, and CI/CD for scalable pipelines. ● Offers proven strategies to optimize workflows and avoid common pitfalls. DESCRIPTION In today’s data-driven world, mastering data engineering is crucial for driving innovation and delivering real business impact. Databricks is one of the most powerful platforms which unifies data, analytics and AI requirements of numerous organizations worldwide. Mastering Data Engineering and Analytics with Databricks goes beyond the basics, offering a hands-on, practical approach tailored for professionals eager to excel in the evolving landscape of data engineering and analytics. This book uniquely blends foundational knowledge with advanced applications, equipping readers with the expertise to build, optimize, and scale data pipelines that meet real-world business needs. With a focus on actionable learning, it delves into complex workflows, including real-time data processing, advanced optimization with Delta Lake, and seamless ML integration with MLflow—skills critical for today’s data professionals. Drawing from real-world case studies in FMCG and CPG industries, this book not only teaches you how to implement Databricks solutions but also provides strategic insights into tackling industry-specific challenges. From setting up your environment to deploying CI/CD pipelines, you'll gain a competitive edge by mastering techniques that are directly applicable to your organization’s data strategy. By the end, you’ll not just understand Databricks—you’ll command it, positioning yourself as a leader in the data engineering space. WHAT WILL YOU LEARN ● Design and implement scalable, high-performance data pipelines using Databricks for various business use cases. ● Optimize query performance and efficiently manage cloud resources for cost-effective data processing. ● Seamlessly integrate machine learning models into your data engineering workflows for smarter automation. ● Build and deploy real-time data processing solutions for timely and actionable insights. ● Develop reliable and fault-tolerant Delta Lake architectures to support efficient data lakes at scale. WHO IS THIS BOOK FOR? This book is designed for data engineering students, aspiring data engineers, experienced data professionals, cloud data architects, data scientists and analysts looking to expand their skill sets, as well as IT managers seeking to master data engineering and analytics with Databricks. A basic understanding of data engineering concepts, familiarity with data analytics, and some experience with cloud computing or programming languages such as Python or SQL will help readers fully benefit from the book’s content. TABLE OF CONTENTS SECTION 1 1. Introducing Data Engineering with Databricks 2. Setting Up a Databricks Environment for Data Engineering 3. Working with Databricks Utilities and Clusters SECTION 2 4. Extracting and Loading Data Using Databricks 5. Transforming Data with Databricks 6. Handling Streaming Data with Databricks 7. Creating Delta Live Tables 8. Data Partitioning and Shuffling 9. Performance Tuning and Best Practices 10. Workflow Management 11. Databricks SQL Warehouse 12. Data Storage and Unity Catalog 13. Monitoring Databricks Clusters and Jobs 14. Production Deployment Strategies 15. Maintaining Data Pipelines in Production 16. Managing Data Security and Governance 17. Real-World Data Engineering Use Cases with Databricks 18. AI and ML Essentials 19. Integrating Databricks with External Tools Index
  databricks data science and engineering workspace: Ultimate Data Engineering with Databricks Mayank Malhotra, 2024-02-14 Navigating Databricks with Ease for Unparalleled Data Engineering Insights. KEY FEATURES ● Navigate Databricks with a seamless progression from fundamental principles to advanced engineering techniques. ● Gain hands-on experience with real-world examples, ensuring immediate relevance and practicality. ● Discover expert insights and best practices for refining your data engineering skills and achieving superior results with Databricks. DESCRIPTION Ultimate Data Engineering with Databricks is a comprehensive handbook meticulously designed for professionals aiming to enhance their data engineering skills through Databricks. Bridging the gap between foundational and advanced knowledge, this book employs a step-by-step approach with detailed explanations suitable for beginners and experienced practitioners alike. Focused on practical applications, the book employs real-world examples and scenarios to teach how to construct, optimize, and maintain robust data pipelines. Emphasizing immediate applicability, it equips readers to address real data challenges using Databricks effectively. The goal is not just understanding Databricks but mastering it to offer tangible solutions. Beyond technical skills, the book imparts best practices and expert tips derived from industry experience, aiding readers in avoiding common pitfalls and adopting strategies for optimal data engineering solutions. This book will help you develop the skills needed to make impactful contributions to organizations, enhancing your value as data engineering professionals in today's competitive job market. WHAT WILL YOU LEARN ● Acquire proficiency in Databricks fundamentals, enabling the construction of efficient data pipelines. ● Design and implement high-performance data solutions for scalability. ● Apply essential best practices for ensuring data integrity in pipelines. ● Explore advanced Databricks features for tackling complex data tasks. ● Learn to optimize data pipelines for streamlined workflows. WHO IS THIS BOOK FOR? This book caters to a diverse audience, including data engineers, data architects, BI analysts, data scientists and technology enthusiasts. Suitable for both professionals and students, the book appeals to those eager to master Databricks and stay at the forefront of data engineering trends. A basic understanding of data engineering concepts and familiarity with cloud computing will enhance the learning experience. TABLE OF CONTENTS 1. Fundamentals of Data Engineering 2. Mastering Delta Tables in Databricks 3. Data Ingestion and Extraction 4. Data Transformation and ETL Processes 5. Data Quality and Validation 6. Data Modeling and Storage 7. Data Orchestration and Workflow Management 8. Performance Tuning and Optimization 9. Scalability and Deployment Considerations 10. Data Security and Governance Last Words Index
  databricks data science and engineering workspace: Mastering Databricks Lakehouse Platform Sagar Lad, Anjani Kumar, 2022-07-11 Enable data and AI workloads with absolute security and scalability KEY FEATURES ● Detailed, step-by-step instructions for every data professional starting a career with data engineering. ● Access to DevOps, Machine Learning, and Analytics wirthin a single unified platform. ● Includes design considerations and security best practices for efficient utilization of Databricks platform. DESCRIPTION Starting with the fundamentals of the databricks lakehouse platform, the book teaches readers on administering various data operations, including Machine Learning, DevOps, Data Warehousing, and BI on the single platform. The subsequent chapters discuss working around data pipelines utilizing the databricks lakehouse platform with data processing and audit quality framework. The book teaches to leverage the Databricks Lakehouse platform to develop delta live tables, streamline ETL/ELT operations, and administer data sharing and orchestration. The book explores how to schedule and manage jobs through the Databricks notebook UI and the Jobs API. The book discusses how to implement DevOps methods on the Databricks Lakehouse platform for data and AI workloads. The book helps readers prepare and process data and standardizes the entire ML lifecycle, right from experimentation to production. The book doesn't just stop here; instead, it teaches how to directly query data lake with your favourite BI tools like Power BI, Tableau, or Qlik. Some of the best industry practices on building data engineering solutions are also demonstrated towards the end of the book. WHAT YOU WILL LEARN ● Acquire capabilities to administer end-to-end Databricks Lakehouse Platform. ● Utilize Flow to deploy and monitor machine learning solutions. ● Gain practical experience with SQL Analytics and connect Tableau, Power BI, and Qlik. ● Configure clusters and automate CI/CD deployment. ● Learn how to use Airflow, Data Factory, Delta Live Tables, Databricks notebook UI, and the Jobs API. WHO THIS BOOK IS FOR This book is for every data professional, including data engineers, ETL developers, DB administrators, Data Scientists, SQL Developers, and BI specialists. You don't need any prior expertise with this platform because the book covers all the basics. TABLE OF CONTENTS 1. Getting started with Databricks Platform 2. Management of Databricks Platform 3. Spark, Databricks, and Building a Data Quality Framework 4. Data Sharing and Orchestration with Databricks 5. Simplified ETL with Delta Live Tables 6. SCD Type 2 Implementation with Delta Lake 7. Machine Learning Model Management with Databricks 8. Continuous Integration and Delivery with Databricks 9. Visualization with Databricks 10. Best Security and Compliance Practices of Databricks
  databricks data science and engineering workspace: Distributed Data Systems with Azure Databricks Alan Bernardo Palacio, 2021-05-25 Quickly build and deploy massive data pipelines and improve productivity using Azure Databricks Key FeaturesGet to grips with the distributed training and deployment of machine learning and deep learning modelsLearn how ETLs are integrated with Azure Data Factory and Delta LakeExplore deep learning and machine learning models in a distributed computing infrastructureBook Description Microsoft Azure Databricks helps you to harness the power of distributed computing and apply it to create robust data pipelines, along with training and deploying machine learning and deep learning models. Databricks' advanced features enable developers to process, transform, and explore data. Distributed Data Systems with Azure Databricks will help you to put your knowledge of Databricks to work to create big data pipelines. The book provides a hands-on approach to implementing Azure Databricks and its associated methodologies that will make you productive in no time. Complete with detailed explanations of essential concepts, practical examples, and self-assessment questions, you’ll begin with a quick introduction to Databricks core functionalities, before performing distributed model training and inference using TensorFlow and Spark MLlib. As you advance, you’ll explore MLflow Model Serving on Azure Databricks and implement distributed training pipelines using HorovodRunner in Databricks. Finally, you’ll discover how to transform, use, and obtain insights from massive amounts of data to train predictive models and create entire fully working data pipelines. By the end of this MS Azure book, you’ll have gained a solid understanding of how to work with Databricks to create and manage an entire big data pipeline. What you will learnCreate ETLs for big data in Azure DatabricksTrain, manage, and deploy machine learning and deep learning modelsIntegrate Databricks with Azure Data Factory for extract, transform, load (ETL) pipeline creationDiscover how to use Horovod for distributed deep learningFind out how to use Delta Engine to query and process data from Delta LakeUnderstand how to use Data Factory in combination with DatabricksUse Structured Streaming in a production-like environmentWho this book is for This book is for software engineers, machine learning engineers, data scientists, and data engineers who are new to Azure Databricks and want to build high-quality data pipelines without worrying about infrastructure. Knowledge of Azure Databricks basics is required to learn the concepts covered in this book more effectively. A basic understanding of machine learning concepts and beginner-level Python programming knowledge is also recommended.
  databricks data science and engineering workspace: Exam Prep AZ-305 Lalit Rawat, 2024-07-24 DESCRIPTION “Exam Prep AZ-305: Designing Microsoft Azure Infrastructure Solutions” book is a comprehensive guide for IT professionals preparing for the Microsoft Azure AZ-305 certification exam. This book offers detailed insights into designing scalable, secure, and resilient infrastructure solutions on the Azure platform, aligning with the latest exam objectives. It covers critical topics such as designing governance, security, storage, and networking solutions, ensuring readers have the necessary knowledge to architect effective Azure solutions. Through a blend of theoretical concepts and practical exercises, this guide equips readers with the skills needed to apply Azure best practices in real-world scenarios. Each chapter covers specific areas of infrastructure design, providing step-by-step instructions, expert tips, and real-life examples to illustrate complex concepts. This practical approach not only helps in mastering the exam content but also enhances the reader's ability to solve real-world challenges in their job roles. It not only prepares you for certification but also empowers you to design and implement robust Azure infrastructure solutions, thereby enhancing your capabilities and career prospects in the evolving field of cloud technology. KEY FEATURES ● Expertise in Azure networking, storage, compute, identity management, monitoring, security, hybrid cloud solutions, and disaster recovery. ● Learn to design and implement robust Azure infrastructure solutions. ● Prepare for the AZ-305 Azure Infrastructure Architect certification exam. ● Utilize up-to-date Microsoft AZ-305 curriculum. WHAT YOU WILL LEARN ● Master Azure governance principles. ● Design secure authentication and authorization solutions. ● Architect scalable compute solutions on Azure. ● Implement effective data storage and integration strategies. ● Design robust backup and disaster recovery solutions. ● Learn key migration strategies for transitioning to Azure. WHO THIS BOOK IS FOR Whether you are an aspiring cloud architect, a seasoned IT professional, or someone looking to advance their career in cloud computing, this book serves as an essential resource. TABLE OF CONTENTS 1. Designing Governance 2. Designing Authentication and Authorization Solutions 3. Designing a Solution Monitor of Azure Resources 4. Designing an Azure Compute Solution 5. Designing a Data Storage Solution for Non-relational Data 6. Designing Data Integration 7. Designing Data Storage Solutions for Relational Data 8. Designing Network Solutions 9. Designing a Solution for Backup and Disaster Recovery 10. Designing Migration 11. Azure Well-Architected Framework 12. Exam Preparation Guidelines and Assessment Questions 13. Azure Architect Exam Mock Test
  databricks data science and engineering workspace: Optimizing Databricks Workloads Anirudh Kala, Anshul Bhatnagar, Sarthak Sarbahi, 2021-12-24 Accelerate computations and make the most of your data effectively and efficiently on Databricks Key FeaturesUnderstand Spark optimizations for big data workloads and maximizing performanceBuild efficient big data engineering pipelines with Databricks and Delta LakeEfficiently manage Spark clusters for big data processingBook Description Databricks is an industry-leading, cloud-based platform for data analytics, data science, and data engineering supporting thousands of organizations across the world in their data journey. It is a fast, easy, and collaborative Apache Spark-based big data analytics platform for data science and data engineering in the cloud. In Optimizing Databricks Workloads, you will get started with a brief introduction to Azure Databricks and quickly begin to understand the important optimization techniques. The book covers how to select the optimal Spark cluster configuration for running big data processing and workloads in Databricks, some very useful optimization techniques for Spark DataFrames, best practices for optimizing Delta Lake, and techniques to optimize Spark jobs through Spark core. It contains an opportunity to learn about some of the real-world scenarios where optimizing workloads in Databricks has helped organizations increase performance and save costs across various domains. By the end of this book, you will be prepared with the necessary toolkit to speed up your Spark jobs and process your data more efficiently. What you will learnGet to grips with Spark fundamentals and the Databricks platformProcess big data using the Spark DataFrame API with Delta LakeAnalyze data using graph processing in DatabricksUse MLflow to manage machine learning life cycles in DatabricksFind out how to choose the right cluster configuration for your workloadsExplore file compaction and clustering methods to tune Delta tablesDiscover advanced optimization techniques to speed up Spark jobsWho this book is for This book is for data engineers, data scientists, and cloud architects who have working knowledge of Spark/Databricks and some basic understanding of data engineering principles. Readers will need to have a working knowledge of Python, and some experience of SQL in PySpark and Spark SQL is beneficial.
  databricks data science and engineering workspace: DATABRICKS SERVICE GUIDE Diego Rodrigues, 2024-10-16 Discover the power of data analysis and machine learning with the DATABRICKS SERVICES GUIDE: From Fundamentals to Practical Applications. This book is an essential reference for data engineers, data scientists, and developers seeking to master the Databricks platform, one of the most advanced solutions for big data and artificial intelligence. Written by Diego Rodrigues, an internationally recognized author with vast experience in technology, this guide offers a comprehensive view of the main services of Databricks. From initial setup to advanced solutions implementation, each chapter is designed to provide clear and detailed instructions, enabling you to immediately apply the knowledge acquired in your projects. The DATABRICKS SERVICES GUIDE covers fundamental topics such as Databricks Workspace, Delta Lake, Data Engineering, Machine Learning, and much more. This book is ideal for both beginners who seek a solid foundation and experienced professionals who want to deepen their skills and explore the advanced capabilities of Databricks. This guide has been designed to be a practical and accessible tool, facilitating the understanding of concepts and the application of best practices in production environments. With practical examples and a structured approach, you will be ready to face technological challenges and implement scalable and secure solutions with Databricks. Tags: Databricks big data machine learning engineering Delta Lake processing analysis Apache Spark notebooks clusters integration pipelines automation cloud storage security data compliance GDPR lgpd engineering transformation SQL real-time API data governance data orchestration data integration Power BI Tableau CI/CD cluster management performance monitoring logs data optimization WAF Databricks File System DBFS cloud computing data science Python Scala R artificial intelligence machine learning workflow scalability efficiency encryption automation DevOps S3 Lambda Glue Kafka Kubernetes Hadoop continuous integration continuous delivery security compliance AWS Microsoft Azure Google IBM Alibaba Diego Rodrigues
  databricks data science and engineering workspace: Azure Databricks Cookbook Phani Raj, Vinod Jaiswal, 2021-09-17 Get to grips with building and productionizing end-to-end big data solutions in Azure and learn best practices for working with large datasets Key FeaturesIntegrate with Azure Synapse Analytics, Cosmos DB, and Azure HDInsight Kafka Cluster to scale and analyze your projects and build pipelinesUse Databricks SQL to run ad hoc queries on your data lake and create dashboardsProductionize a solution using CI/CD for deploying notebooks and Azure Databricks Service to various environmentsBook Description Azure Databricks is a unified collaborative platform for performing scalable analytics in an interactive environment. The Azure Databricks Cookbook provides recipes to get hands-on with the analytics process, including ingesting data from various batch and streaming sources and building a modern data warehouse. The book starts by teaching you how to create an Azure Databricks instance within the Azure portal, Azure CLI, and ARM templates. You'll work through clusters in Databricks and explore recipes for ingesting data from sources, including files, databases, and streaming sources such as Apache Kafka and EventHub. The book will help you explore all the features supported by Azure Databricks for building powerful end-to-end data pipelines. You'll also find out how to build a modern data warehouse by using Delta tables and Azure Synapse Analytics. Later, you'll learn how to write ad hoc queries and extract meaningful insights from the data lake by creating visualizations and dashboards with Databricks SQL. Finally, you'll deploy and productionize a data pipeline as well as deploy notebooks and Azure Databricks service using continuous integration and continuous delivery (CI/CD). By the end of this Azure book, you'll be able to use Azure Databricks to streamline different processes involved in building data-driven apps. What you will learnRead and write data from and to various Azure resources and file formatsBuild a modern data warehouse with Delta Tables and Azure Synapse AnalyticsExplore jobs, stages, and tasks and see how Spark lazy evaluation worksHandle concurrent transactions and learn performance optimization in Delta tablesLearn Databricks SQL and create real-time dashboards in Databricks SQLIntegrate Azure DevOps for version control, deploying, and productionizing solutions with CI/CD pipelinesDiscover how to use RBAC and ACLs to restrict data accessBuild end-to-end data processing pipeline for near real-time data analyticsWho this book is for This recipe-based book is for data scientists, data engineers, big data professionals, and machine learning engineers who want to perform data analytics on their applications. Prior experience of working with Apache Spark and Azure is necessary to get the most out of this book.
  databricks data science and engineering workspace: Azure Cookbook Reza Salehi, 2022-10-10 How do you deal with the problems you face when using Azure? This practical guide provides over 75 recipes to help you to work with common Azure issues in everyday scenarios. That includes key tasks like setting up permissions for a storage account, working with Cosmos DB APIs, managing Azure role-based access control, governing your Azure subscriptions using Azure Policy, and much more. Author Reza Salehi has assembled real-world recipes that enable you to grasp key Azure services and concepts quickly. Each recipe includes CLI scripts that you can execute in your own Azure account. Recipes also explain the approach and provide meaningful context. The solutions in this cookbook will take you beyond theory and help you understand Azure services in practice. You'll find recipes that let you: Store data in an Azure storage account or in a data lake Work with relational and nonrelational databases in Azure Manage role-based access control (RBAC) for Azure resources Safeguard secrets in Azure Key Vault Govern your Azure subscription using Azure Policy Use CLI code to construct your application or fix a particular problem
  databricks data science and engineering workspace: Hands-On Azure Data Platform Sagar Lad, Abhishek Mishra, Ashirwad Satapathi, 2022-02-10 Plan, build, deploy, and monitor data solutions on Azure KEY FEATURES ● Work with PostgreSQL, MySQL, and CosmosDB databases on Microsoft Azure. ● Work with whole data architecture, leverage Azure Storage, Azure Synapse, and Azure Data Lake. ● Data integration strategies with Azure Data Factory and Data Bricks. DESCRIPTION 'Hands-On Azure Data Platform' helps readers get a fundamental understanding of the Database, Data Warehouse, and Data Lake and their management on the Azure Data Platform. The book describes how to work efficiently with Relational and Non-Relational Databases, Azure Synapse Analytics, and Azure Data Lake. The readers will use Azure Databricks and Azure Data Factory to experience data processing and transformation. The book delves deeply into topics like continuous integration, continuous delivery, and the use of Azure DevOps. The book focuses on the integration of Azure DevOps with CI/CD pipelines for data ops solutions. The book teaches readers how to migrate data from an on-premises system or another cloud service provider to Azure. After reading the book, readers will develop end-to-end data solutions using the Azure data platform. Additionally, data engineers and ETL developers can streamline their ETL operations using various efficient Azure services. WHAT YOU WILL LEARN ● In-depth knowledge of the principles of the data warehouse and the data lake. ● Acquaint yourself with Azure Storage Files, Blobs, and Queues. ● Create relational databases on the Azure platform using SQL, PostgreSQL, and MySQL. ● With Cosmos DB, you can create extremely scalable databases and data warehouses. ● Utilize Azure Databricks and Data Factory to develop data integration solutions. WHO THIS BOOK IS FOR This book is designed for big data engineers, data architects, and cloud engineers who want to understand how to use the Azure Data Platform to build enterprise-grade solutions. Learning about databases and the Azure Data Platform would be helpful but not necessary. TABLE OF CONTENTS 1. Getting Started with the Azure Data Platform 2. Working with Relational Databases on Azure 3. Working with Azure Synapse Analytics 4. Working with Azure Data Lake 5. Working with Azure Cosmos DB 6. Working with Azure Databricks 7. Working with Azure Data Factory 8. DevOps with the Azure Data Platform 9. Planning and Migrating On-Premises Azure Workloads to the Azure Data platform 10. Design and Implement Data Solutions on Azure
  databricks data science and engineering workspace: Learning Spark Jules S. Damji, Brooke Wenig, Tathagata Das, Denny Lee, 2020-07-16 Data is bigger, arrives faster, and comes in a variety of formats—and it all needs to be processed at scale for analytics or machine learning. But how can you process such varied workloads efficiently? Enter Apache Spark. Updated to include Spark 3.0, this second edition shows data engineers and data scientists why structure and unification in Spark matters. Specifically, this book explains how to perform simple and complex data analytics and employ machine learning algorithms. Through step-by-step walk-throughs, code snippets, and notebooks, you’ll be able to: Learn Python, SQL, Scala, or Java high-level Structured APIs Understand Spark operations and SQL Engine Inspect, tune, and debug Spark operations with Spark configurations and Spark UI Connect to data sources: JSON, Parquet, CSV, Avro, ORC, Hive, S3, or Kafka Perform analytics on batch and streaming data using Structured Streaming Build reliable data pipelines with open source Delta Lake and Spark Develop machine learning pipelines with MLlib and productionize models using MLflow
  databricks data science and engineering workspace: Building the Data Lakehouse Bill Inmon, Ranjeet Srivastava, Mary Levins, 2021-10 The data lakehouse is the next generation of the data warehouse and data lake, designed to meet today's complex and ever-changing analytics, machine learning, and data science requirements. Learn about the features and architecture of the data lakehouse, along with its powerful analytical infrastructure. Appreciate how the universal common connector blends structured, textual, analog, and IoT data. Maintain the lakehouse for future generations through Data Lakehouse Housekeeping and Data Future-proofing. Know how to incorporate the lakehouse into an existing data governance strategy. Incorporate data catalogs, data lineage tools, and open source software into your architecture to ensure your data scientists, analysts, and end users live happily ever after.
  databricks data science and engineering workspace: Data Engineering with Scala and Spark Eric Tome, Rupam Bhattacharjee, David Radford, 2024-01-31 Take your data engineering skills to the next level by learning how to utilize Scala and functional programming to create continuous and scheduled pipelines that ingest, transform, and aggregate data Key Features Transform data into a clean and trusted source of information for your organization using Scala Build streaming and batch-processing pipelines with step-by-step explanations Implement and orchestrate your pipelines by following CI/CD best practices and test-driven development (TDD) Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMost data engineers know that performance issues in a distributed computing environment can easily lead to issues impacting the overall efficiency and effectiveness of data engineering tasks. While Python remains a popular choice for data engineering due to its ease of use, Scala shines in scenarios where the performance of distributed data processing is paramount. This book will teach you how to leverage the Scala programming language on the Spark framework and use the latest cloud technologies to build continuous and triggered data pipelines. You’ll do this by setting up a data engineering environment for local development and scalable distributed cloud deployments using data engineering best practices, test-driven development, and CI/CD. You’ll also get to grips with DataFrame API, Dataset API, and Spark SQL API and its use. Data profiling and quality in Scala will also be covered, alongside techniques for orchestrating and performance tuning your end-to-end pipelines to deliver data to your end users. By the end of this book, you will be able to build streaming and batch data pipelines using Scala while following software engineering best practices.What you will learn Set up your development environment to build pipelines in Scala Get to grips with polymorphic functions, type parameterization, and Scala implicits Use Spark DataFrames, Datasets, and Spark SQL with Scala Read and write data to object stores Profile and clean your data using Deequ Performance tune your data pipelines using Scala Who this book is for This book is for data engineers who have experience in working with data and want to understand how to transform raw data into a clean, trusted, and valuable source of information for their organization using Scala and the latest cloud technologies.
  databricks data science and engineering workspace: MCA Microsoft Certified Associate Azure Data Engineer Study Guide Benjamin Perkins, 2023-08-02 Prepare for the Azure Data Engineering certification—and an exciting new career in analytics—with this must-have study aide In the MCA Microsoft Certified Associate Azure Data Engineer Study Guide: Exam DP-203, accomplished data engineer and tech educator Benjamin Perkins delivers a hands-on, practical guide to preparing for the challenging Azure Data Engineer certification and for a new career in an exciting and growing field of tech. In the book, you’ll explore all the objectives covered on the DP-203 exam while learning the job roles and responsibilities of a newly minted Azure data engineer. From integrating, transforming, and consolidating data from various structured and unstructured data systems into a structure that is suitable for building analytics solutions, you’ll get up to speed quickly and efficiently with Sybex’s easy-to-use study aids and tools. This Study Guide also offers: Career-ready advice for anyone hoping to ace their first data engineering job interview and excel in their first day in the field Indispensable tips and tricks to familiarize yourself with the DP-203 exam structure and help reduce test anxiety Complimentary access to Sybex’s expansive online study tools, accessible across multiple devices, and offering access to hundreds of bonus practice questions, electronic flashcards, and a searchable, digital glossary of key terms A one-of-a-kind study aid designed to help you get straight to the crucial material you need to succeed on the exam and on the job, the MCA Microsoft Certified Associate Azure Data Engineer Study Guide: Exam DP-203 belongs on the bookshelves of anyone hoping to increase their data analytics skills, advance their data engineering career with an in-demand certification, or hoping to make a career change into a popular new area of tech.
  databricks data science and engineering workspace: Microsoft Certified Exam guide - Azure Data Engineer Associate (DP-203) Cybellium Ltd, Unlock the Power of Data with Azure Data Engineering! Are you ready to become a Microsoft Azure Data Engineer Associate and harness the transformative potential of data in the cloud? Look no further than the Microsoft Certified Exam Guide - Azure Data Engineer Associate (DP-203). This comprehensive book is your ultimate companion on the journey to mastering Azure data engineering and acing the DP-203 exam. In today's data-driven world, organizations depend on the efficient management, processing, and analysis of data to make critical decisions and drive innovation. Microsoft Azure provides a cutting-edge platform for data engineers to design and implement data solutions, and the demand for skilled professionals in this field is soaring. Whether you're an experienced data engineer or just starting your journey, this book equips you with the knowledge and skills needed to excel in Azure data engineering. Inside this book, you will discover: ✔ Comprehensive Coverage: A deep dive into all the key concepts, tools, and best practices required for designing, building, and maintaining data solutions on Azure. ✔ Real-World Scenarios: Practical examples and case studies that illustrate how Azure is used to solve complex data challenges, making learning engaging and relevant. ✔ Exam-Ready Preparation: Thorough coverage of DP-203 exam objectives, complete with practice questions and expert tips to ensure you're well-prepared for exam day. ✔ Proven Expertise: Authored by Azure data engineering professionals who hold the certification and have hands-on experience in developing data solutions, offering you invaluable insights and practical guidance. Whether you aspire to advance your career, validate your expertise, or simply become a proficient Azure Data Engineer, Microsoft Certified Exam Guide - Azure Data Engineer Associate (DP-203) is your trusted companion on this journey. Don't miss this opportunity to become a sought-after data engineering expert in a competitive job market. © 2023 Cybellium Ltd. All rights reserved. www.cybellium.com
  databricks data science and engineering workspace: Azure Data Scientist Associate Certification Guide Andreas Botsikas, Michael Hlobil, 2021-12-03 Develop the skills you need to run machine learning workloads in Azure and pass the DP-100 exam with ease Key FeaturesCreate end-to-end machine learning training pipelines, with or without codeTrack experiment progress using the cloud-based MLflow-compatible process of Azure ML servicesOperationalize your machine learning models by creating batch and real-time endpointsBook Description The Azure Data Scientist Associate Certification Guide helps you acquire practical knowledge for machine learning experimentation on Azure. It covers everything you need to pass the DP-100 exam and become a certified Azure Data Scientist Associate. Starting with an introduction to data science, you'll learn the terminology that will be used throughout the book and then move on to the Azure Machine Learning (Azure ML) workspace. You'll discover the studio interface and manage various components, such as data stores and compute clusters. Next, the book focuses on no-code and low-code experimentation, and shows you how to use the Automated ML wizard to locate and deploy optimal models for your dataset. You'll also learn how to run end-to-end data science experiments using the designer provided in Azure ML Studio. You'll then explore the Azure ML Software Development Kit (SDK) for Python and advance to creating experiments and publishing models using code. The book also guides you in optimizing your model's hyperparameters using Hyperdrive before demonstrating how to use responsible AI tools to interpret and debug your models. Once you have a trained model, you'll learn to operationalize it for batch or real-time inferences and monitor it in production. By the end of this Azure certification study guide, you'll have gained the knowledge and the practical skills required to pass the DP-100 exam. What you will learnCreate a working environment for data science workloads on AzureRun data experiments using Azure Machine Learning servicesCreate training and inference pipelines using the designer or codeDiscover the best model for your dataset using Automated MLUse hyperparameter tuning to optimize trained modelsDeploy, use, and monitor models in productionInterpret the predictions of a trained modelWho this book is for This book is for developers who want to infuse their applications with AI capabilities and data scientists looking to scale their machine learning experiments in the Azure cloud. Basic knowledge of Python is needed to follow the code samples used in the book. Some experience in training machine learning models in Python using common frameworks like scikit-learn will help you understand the content more easily.
  databricks data science and engineering workspace: Business Intelligence with Databricks SQL Vihag Gupta, 2022-09-16 Master critical skills needed to deploy and use Databricks SQL and elevate your BI from the warehouse to the lakehouse with confidence Key FeaturesLearn about business intelligence on the lakehouse with features and functions of Databricks SQLMake the most of Databricks SQL by getting to grips with the enablers of its data warehousing capabilitiesA unique approach to teaching concepts and techniques with follow-along scenarios on real datasetsBook Description In this new era of data platform system design, data lakes and data warehouses are giving way to the lakehouse – a new type of data platform system that aims to unify all data analytics into a single platform. Databricks, with its Databricks SQL product suite, is the hottest lakehouse platform out there, harnessing the power of Apache Spark™, Delta Lake, and other innovations to enable data warehousing capabilities on the lakehouse with data lake economics. This book is a comprehensive hands-on guide that helps you explore all the advanced features, use cases, and technology components of Databricks SQL. You'll start with the lakehouse architecture fundamentals and understand how Databricks SQL fits into it. The book then shows you how to use the platform, from exploring data, executing queries, building reports, and using dashboards through to learning the administrative aspects of the lakehouse – data security, governance, and management of the computational power of the lakehouse. You'll also delve into the core technology enablers of Databricks SQL – Delta Lake and Photon. Finally, you'll get hands-on with advanced SQL commands for ingesting data and maintaining the lakehouse. By the end of this book, you'll have mastered Databricks SQL and be able to deploy and deliver fast, scalable business intelligence on the lakehouse. What you will learnUnderstand how Databricks SQL fits into the Databricks Lakehouse PlatformPerform everyday analytics with Databricks SQL Workbench and business intelligence toolsOrganize and catalog your data assetsProgram the data security model to protect and govern your dataTune SQL warehouses (computing clusters) for optimal query experienceTune the Delta Lake storage format for maximum query performanceDeliver extreme performance with the Photon query execution engineImplement advanced data ingestion patterns with Databricks SQLWho this book is for This book is for business intelligence practitioners, data warehouse administrators, and data engineers who are new to Databrick SQL and want to learn how to deliver high-quality insights unhindered by the scale of data or infrastructure. This book is also for anyone looking to study the advanced technologies that power Databricks SQL. Basic knowledge of data warehouses, SQL-based analytics, and ETL processes is recommended to effectively learn the concepts introduced in this book and appreciate the innovation behind the platform.
  databricks data science and engineering workspace: Practical Machine Learning on Databricks Debu Sinha, 2023-11-24 Take your machine learning skills to the next level by mastering databricks and building robust ML pipeline solutions for future ML innovations Key Features Learn to build robust ML pipeline solutions for databricks transition Master commonly available features like AutoML and MLflow Leverage data governance and model deployment using MLflow model registry Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionUnleash the potential of databricks for end-to-end machine learning with this comprehensive guide, tailored for experienced data scientists and developers transitioning from DIY or other cloud platforms. Building on a strong foundation in Python, Practical Machine Learning on Databricks serves as your roadmap from development to production, covering all intermediary steps using the databricks platform. You’ll start with an overview of machine learning applications, databricks platform features, and MLflow. Next, you’ll dive into data preparation, model selection, and training essentials and discover the power of databricks feature store for precomputing feature tables. You’ll also learn to kickstart your projects using databricks AutoML and automate retraining and deployment through databricks workflows. By the end of this book, you’ll have mastered MLflow for experiment tracking, collaboration, and advanced use cases like model interpretability and governance. The book is enriched with hands-on example code at every step. While primarily focused on generally available features, the book equips you to easily adapt to future innovations in machine learning, databricks, and MLflow.What you will learn Transition smoothly from DIY setups to databricks Master AutoML for quick ML experiment setup Automate model retraining and deployment Leverage databricks feature store for data prep Use MLflow for effective experiment tracking Gain practical insights for scalable ML solutions Find out how to handle model drifts in production environments Who this book is forThis book is for experienced data scientists, engineers, and developers proficient in Python, statistics, and ML lifecycle looking to transition to databricks from DIY clouds. Introductory Spark knowledge is a must to make the most out of this book, however, end-to-end ML workflows will be covered. If you aim to accelerate your machine learning workflows and deploy scalable, robust solutions, this book is an indispensable resource.
  databricks data science and engineering workspace: MLOps Engineering at Scale Carl Osipov, 2022-03-22 Dodge costly and time-consuming infrastructure tasks, and rapidly bring your machine learning models to production with MLOps and pre-built serverless tools! In MLOps Engineering at Scale you will learn: Extracting, transforming, and loading datasets Querying datasets with SQL Understanding automatic differentiation in PyTorch Deploying model training pipelines as a service endpoint Monitoring and managing your pipeline’s life cycle Measuring performance improvements MLOps Engineering at Scale shows you how to put machine learning into production efficiently by using pre-built services from AWS and other cloud vendors. You’ll learn how to rapidly create flexible and scalable machine learning systems without laboring over time-consuming operational tasks or taking on the costly overhead of physical hardware. Following a real-world use case for calculating taxi fares, you will engineer an MLOps pipeline for a PyTorch model using AWS server-less capabilities. About the technology A production-ready machine learning system includes efficient data pipelines, integrated monitoring, and means to scale up and down based on demand. Using cloud-based services to implement ML infrastructure reduces development time and lowers hosting costs. Serverless MLOps eliminates the need to build and maintain custom infrastructure, so you can concentrate on your data, models, and algorithms. About the book MLOps Engineering at Scale teaches you how to implement efficient machine learning systems using pre-built services from AWS and other cloud vendors. This easy-to-follow book guides you step-by-step as you set up your serverless ML infrastructure, even if you’ve never used a cloud platform before. You’ll also explore tools like PyTorch Lightning, Optuna, and MLFlow that make it easy to build pipelines and scale your deep learning models in production. What's inside Reduce or eliminate ML infrastructure management Learn state-of-the-art MLOps tools like PyTorch Lightning and MLFlow Deploy training pipelines as a service endpoint Monitor and manage your pipeline’s life cycle Measure performance improvements About the reader Readers need to know Python, SQL, and the basics of machine learning. No cloud experience required. About the author Carl Osipov implemented his first neural net in 2000 and has worked on deep learning and machine learning at Google and IBM. Table of Contents PART 1 - MASTERING THE DATA SET 1 Introduction to serverless machine learning 2 Getting started with the data set 3 Exploring and preparing the data set 4 More exploratory data analysis and data preparation PART 2 - PYTORCH FOR SERVERLESS MACHINE LEARNING 5 Introducing PyTorch: Tensor basics 6 Core PyTorch: Autograd, optimizers, and utilities 7 Serverless machine learning at scale 8 Scaling out with distributed training PART 3 - SERVERLESS MACHINE LEARNING PIPELINE 9 Feature selection 10 Adopting PyTorch Lightning 11 Hyperparameter optimization 12 Machine learning pipeline
  databricks data science and engineering workspace: Beginning Apache Spark Using Azure Databricks Robert Ilijason, 2020-06-11 Analyze vast amounts of data in record time using Apache Spark with Databricks in the Cloud. Learn the fundamentals, and more, of running analytics on large clusters in Azure and AWS, using Apache Spark with Databricks on top. Discover how to squeeze the most value out of your data at a mere fraction of what classical analytics solutions cost, while at the same time getting the results you need, incrementally faster. This book explains how the confluence of these pivotal technologies gives you enormous power, and cheaply, when it comes to huge datasets. You will begin by learning how cloud infrastructure makes it possible to scale your code to large amounts of processing units, without having to pay for the machinery in advance. From there you will learn how Apache Spark, an open source framework, can enable all those CPUs for data analytics use. Finally, you will see how services such as Databricks provide the power of Apache Spark, without you having to know anything about configuring hardware or software. By removing the need for expensive experts and hardware, your resources can instead be allocated to actually finding business value in the data. This book guides you through some advanced topics such as analytics in the cloud, data lakes, data ingestion, architecture, machine learning, and tools, including Apache Spark, Apache Hadoop, Apache Hive, Python, and SQL. Valuable exercises help reinforce what you have learned. What You Will Learn Discover the value of big data analytics that leverage the power of the cloudGet started with Databricks using SQL and Python in either Microsoft Azure or AWSUnderstand the underlying technology, and how the cloud and Apache Spark fit into the bigger picture See how these tools are used in the real world Run basic analytics, including machine learning, on billions of rows at a fraction of a cost or free Who This Book Is For Data engineers, data scientists, and cloud architects who want or need to run advanced analytics in the cloud. It is assumed that the reader has data experience, but perhaps minimal exposure to Apache Spark and Azure Databricks. The book is also recommended for people who want to get started in the analytics field, as it provides a strong foundation.
  databricks data science and engineering workspace: Data Engineering with Databricks Cookbook Pulkit Chadha, 2024-05-31 Work through 70 recipes for implementing reliable data pipelines with Apache Spark, optimally store and process structured and unstructured data in Delta Lake, and use Databricks to orchestrate and govern your data Key Features Learn data ingestion, data transformation, and data management techniques using Apache Spark and Delta Lake Gain practical guidance on using Delta Lake tables and orchestrating data pipelines Implement reliable DataOps and DevOps practices, and enforce data governance policies on Databricks Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWritten by a Senior Solutions Architect at Databricks, Data Engineering with Databricks Cookbook will show you how to effectively use Apache Spark, Delta Lake, and Databricks for data engineering, starting with comprehensive introduction to data ingestion and loading with Apache Spark. What makes this book unique is its recipe-based approach, which will help you put your knowledge to use straight away and tackle common problems. You’ll be introduced to various data manipulation and data transformation solutions that can be applied to data, find out how to manage and optimize Delta tables, and get to grips with ingesting and processing streaming data. The book will also show you how to improve the performance problems of Apache Spark apps and Delta Lake. Advanced recipes later in the book will teach you how to use Databricks to implement DataOps and DevOps practices, as well as how to orchestrate and schedule data pipelines using Databricks Workflows. You’ll also go through the full process of setup and configuration of the Unity Catalog for data governance. By the end of this book, you’ll be well-versed in building reliable and scalable data pipelines using modern data engineering technologies.What you will learn Perform data loading, ingestion, and processing with Apache Spark Discover data transformation techniques and custom user-defined functions (UDFs) in Apache Spark Manage and optimize Delta tables with Apache Spark and Delta Lake APIs Use Spark Structured Streaming for real-time data processing Optimize Apache Spark application and Delta table query performance Implement DataOps and DevOps practices on Databricks Orchestrate data pipelines with Delta Live Tables and Databricks Workflows Implement data governance policies with Unity Catalog Who this book is for This book is for data engineers, data scientists, and data practitioners who want to learn how to build efficient and scalable data pipelines using Apache Spark, Delta Lake, and Databricks. To get the most out of this book, you should have basic knowledge of data architecture, SQL, and Python programming.
  databricks data science and engineering workspace: Learning Spark Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia, 2015-01-28 Data in all domains is getting bigger. How can you work with it efficiently? Recently updated for Spark 1.3, this book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. With Spark, you can tackle big datasets quickly through simple APIs in Python, Java, and Scala. This edition includes new information on Spark SQL, Spark Streaming, setup, and Maven coordinates. Written by the developers of Spark, this book will have data scientists and engineers up and running in no time. You’ll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning. Quickly dive into Spark capabilities such as distributed datasets, in-memory caching, and the interactive shell Leverage Spark’s powerful built-in libraries, including Spark SQL, Spark Streaming, and MLlib Use one programming paradigm instead of mixing and matching tools like Hive, Hadoop, Mahout, and Storm Learn how to deploy interactive, batch, and streaming applications Connect to data sources including HDFS, Hive, JSON, and S3 Master advanced topics like data partitioning and shared variables
  databricks data science and engineering workspace: Deep Learning with Azure Mathew Salvaris, Danielle Dean, Wee Hyong Tok, 2018-08-24 Get up-to-speed with Microsoft's AI Platform. Learn to innovate and accelerate with open and powerful tools and services that bring artificial intelligence to every data scientist and developer. Artificial Intelligence (AI) is the new normal. Innovations in deep learning algorithms and hardware are happening at a rapid pace. It is no longer a question of should I build AI into my business, but more about where do I begin and how do I get started with AI? Written by expert data scientists at Microsoft, Deep Learning with the Microsoft AI Platform helps you with the how-to of doing deep learning on Azure and leveraging deep learning to create innovative and intelligent solutions. Benefit from guidance on where to begin your AI adventure, and learn how the cloud provides you with all the tools, infrastructure, and services you need to do AI. What You'll Learn Become familiar with the tools, infrastructure, and services available for deep learning on Microsoft Azure such as Azure Machine Learning services and Batch AI Use pre-built AI capabilities (Computer Vision, OCR, gender, emotion, landmark detection, and more) Understand the common deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs) with sample code and understand how the field is evolving Discover the options for training and operationalizing deep learning models on Azure Who This Book Is For Professional data scientists who are interested in learning more about deep learning and how to use the Microsoft AI platform. Some experience with Python is helpful.
  databricks data science and engineering workspace: Cloud Data Architectures Demystified Ashok Boddeda, 2023-09-27 Learn using Cloud data technologies for improving data analytics and decision-making capabilities for your organization KEY FEATURES ● Get familiar with the fundamentals of data architecture and Cloud computing. ● Design and deploy enterprise data architectures on the Cloud. ● Learn how to leverage AI/ML to gain insights from data. DESCRIPTION Cloud data architectures are a valuable tool for organizations that want to use data to make better decisions. By understanding the different components of Cloud data architectures and the benefits they offer, organizations can select the right architecture for their needs. This book is a holistic guide for using Cloud data technologies to ingest, transform, and analyze data. It covers the entire data lifecycle, from collecting data to transforming it into actionable insights. The readers will get a comprehensive overview of Cloud data technologies and AI/ML algorithms. The readers will learn how to use these technologies and algorithms to improve decision-making, optimize operations, and identify new opportunities. By the end of the book, you will have a comprehensive understanding of loud data architectures and the confidence to implement effective solutions that drive business success. WHAT YOU WILL LEARN ● Learn the fundamental principles of data architecture. ● Understand the working of different cloud ecosystems such as AWS, Azure & GCP. ● Explore different Snowflake data services. ● Learn how to implement data governance policies and procedures. ● Use artificial intelligence (AI) and machine learning (ML) to gain insights from data. WHO THIS BOOK IS FOR This book is for executives, IT professionals, and data enthusiasts who want to learn more about Cloud data architectures. It does not require any prior experience, but a basic understanding of data concepts and technology landscapes will be helpful. TABLE OF CONTENTS 1. Data Architectures and Patterns 2. Enterprise Data Architectures 3. Cloud Fundamentals 4. Azure Data Eco-system 5. AWS Data Services 6. Google Data Services 7. Snowflake Data Eco-system 8. Data Governance 9. Data Intelligence: AI-ML Modeling and Services
  databricks data science and engineering workspace: Mastering Large Language Models with Python Raj Arun R, 2024-04-12 A Comprehensive Guide to Leverage Generative AI in the Modern Enterprise KEY FEATURES ● Gain a comprehensive understanding of LLMs within the framework of Generative AI, from foundational concepts to advanced applications. ● Dive into practical exercises and real-world applications, accompanied by detailed code walkthroughs in Python. ● Explore LLMOps with a dedicated focus on ensuring trustworthy AI and best practices for deploying, managing, and maintaining LLMs in enterprise settings. ● Prioritize the ethical and responsible use of LLMs, with an emphasis on building models that adhere to principles of fairness, transparency, and accountability, fostering trust in AI technologies. DESCRIPTION “Mastering Large Language Models with Python” is an indispensable resource that offers a comprehensive exploration of Large Language Models (LLMs), providing the essential knowledge to leverage these transformative AI models effectively. From unraveling the intricacies of LLM architecture to practical applications like code generation and AI-driven recommendation systems, readers will gain valuable insights into implementing LLMs in diverse projects. Covering both open-source and proprietary LLMs, the book delves into foundational concepts and advanced techniques, empowering professionals to harness the full potential of these models. Detailed discussions on quantization techniques for efficient deployment, operational strategies with LLMOps, and ethical considerations ensure a well-rounded understanding of LLM implementation. Through real-world case studies, code snippets, and practical examples, readers will navigate the complexities of LLMs with confidence, paving the way for innovative solutions and organizational growth. Whether you seek to deepen your understanding, drive impactful applications, or lead AI-driven initiatives, this book equips you with the tools and insights needed to excel in the dynamic landscape of artificial intelligence. WHAT WILL YOU LEARN ● In-depth study of LLM architecture and its versatile applications across industries. ● Harness open-source and proprietary LLMs to craft innovative solutions. ● Implement LLM APIs for a wide range of tasks spanning natural language processing, audio analysis, and visual recognition. ● Optimize LLM deployment through techniques such as quantization and operational strategies like LLMOps, ensuring efficient and scalable model usage. ● Master prompt engineering techniques to fine-tune LLM outputs, enhancing quality and relevance for diverse use cases. ● Navigate the complex landscape of ethical AI development, prioritizing responsible practices to drive impactful technology adoption and advancement. WHO IS THIS BOOK FOR? This book is tailored for software engineers, data scientists, AI researchers, and technology leaders with a foundational understanding of machine learning concepts and programming. It's ideal for those looking to deepen their knowledge of Large Language Models and their practical applications in the field of AI. If you aim to explore LLMs extensively for implementing inventive solutions or spearheading AI-driven projects, this book is tailored to your needs. TABLE OF CONTENTS 1. The Basics of Large Language Models and Their Applications 2. Demystifying Open-Source Large Language Models 3. Closed-Source Large Language Models 4. LLM APIs for Various Large Language Model Tasks 5. Integrating Cohere API in Google Sheets 6. Dynamic Movie Recommendation Engine Using LLMs 7. Document-and Web-based QA Bots with Large Language Models 8. LLM Quantization Techniques and Implementation 9. Fine-tuning and Evaluation of LLMs 10. Recipes for Fine-Tuning and Evaluating LLMs 11. LLMOps - Operationalizing LLMs at Scale 12. Implementing LLMOps in Practice Using MLflow on Databricks 13. Mastering the Art of Prompt Engineering 14. Prompt Engineering Essentials and Design Patterns 15. Ethical Considerations and Regulatory Frameworks for LLMs 16. Towards Trustworthy Generative AI (A Novel Framework Inspired by Symbolic Reasoning) Index
  databricks data science and engineering workspace: Data Engineering Best Practices Richard J. Schiller, David Larochelle, 2024-10-11 Explore modern data engineering techniques and best practices to build scalable, efficient, and future-proof data processing systems across cloud platforms Key Features Architect and engineer optimized data solutions in the cloud with best practices for performance and cost-effectiveness Explore design patterns and use cases to balance roles, technology choices, and processes for a future-proof design Learn from experts to avoid common pitfalls in data engineering projects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionRevolutionize your approach to data processing in the fast-paced business landscape with this essential guide to data engineering. Discover the power of scalable, efficient, and secure data solutions through expert guidance on data engineering principles and techniques. Written by two industry experts with over 60 years of combined experience, it offers deep insights into best practices, architecture, agile processes, and cloud-based pipelines. You’ll start by defining the challenges data engineers face and understand how this agile and future-proof comprehensive data solution architecture addresses them. As you explore the extensive toolkit, mastering the capabilities of various instruments, you’ll gain the knowledge needed for independent research. Covering everything you need, right from data engineering fundamentals, the guide uses real-world examples to illustrate potential solutions. It elevates your skills to architect scalable data systems, implement agile development processes, and design cloud-based data pipelines. The book further equips you with the knowledge to harness serverless computing and microservices to build resilient data applications. By the end, you'll be armed with the expertise to design and deliver high-performance data engineering solutions that are not only robust, efficient, and secure but also future-ready.What you will learn Architect scalable data solutions within a well-architected framework Implement agile software development processes tailored to your organization's needs Design cloud-based data pipelines for analytics, machine learning, and AI-ready data products Optimize data engineering capabilities to ensure performance and long-term business value Apply best practices for data security, privacy, and compliance Harness serverless computing and microservices to build resilient, scalable, and trustworthy data pipelines Who this book is for If you are a data engineer, ETL developer, or big data engineer who wants to master the principles and techniques of data engineering, this book is for you. A basic understanding of data engineering concepts, ETL processes, and big data technologies is expected. This book is also for professionals who want to explore advanced data engineering practices, including scalable data solutions, agile software development, and cloud-based data processing pipelines.
  databricks data science and engineering workspace: Cloud Data Science: Harnessing Azure Machine Learning with Python Peter Jones, 2024-10-15 Unlock the full potential of your data with Cloud Data Science: Harnessing Azure Machine Learning with Python. This comprehensive guide equips you with the knowledge and skills to leverage the power of Azure Machine Learning and the versatility of Python to innovate and streamline your machine learning workflows. From setting up your Azure Machine Learning workspace to deploying sophisticated models, this book covers essential techniques and advanced methodologies in a clear, practical format. Dive into core topics such as data management, automated machine learning workflows, model optimization, and real-time monitoring to ensure your projects are scalable, efficient, and effective. Whether you're a data scientist, machine learning engineer, or a professional seeking to enhance your understanding of cloud-based machine learning, this book offers invaluable insights and hands-on examples to help you transform vast amounts of data into actionable insights. Explore real-world case studies across various industries, learn to overcome common challenges, and discover best practices for implementing machine learning projects successfully. Cloud Data Science: Harnessing Azure Machine Learning with Python is your gateway to mastering data science in the cloud and advancing your professional capabilities in the future of technology.
  databricks data science and engineering workspace: Databricks ML in Action Stephanie Rivera, Anastasia Prokaieva, Amanda Baker, Hayley Horn, 2024-05-17 Get to grips with autogenerating code, deploying ML algorithms, and leveraging various ML lifecycle features on the Databricks Platform, guided by best practices and reusable code for you to try, alter, and build on Key Features Build machine learning solutions faster than peers only using documentation Enhance or refine your expertise with tribal knowledge and concise explanations Follow along with code projects provided in GitHub to accelerate your projects Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDiscover what makes the Databricks Data Intelligence Platform the go-to choice for top-tier machine learning solutions. Written by a team of industry experts at Databricks with decades of combined experience in big data, machine learning, and data science, Databricks ML in Action presents cloud-agnostic, end-to-end examples with hands-on illustrations of executing data science, machine learning, and generative AI projects on the Databricks Platform. You’ll develop expertise in Databricks' managed MLflow, Vector Search, AutoML, Unity Catalog, and Model Serving as you learn to apply them practically in everyday workflows. This Databricks book not only offers detailed code explanations but also facilitates seamless code importation for practical use. You’ll discover how to leverage the open-source Databricks platform to enhance learning, boost skills, and elevate productivity with supplemental resources. By the end of this book, you'll have mastered the use of Databricks for data science, machine learning, and generative AI, enabling you to deliver outstanding data products.What you will learn Set up a workspace for a data team planning to perform data science Monitor data quality and detect drift Use autogenerated code for ML modeling and data exploration Operationalize ML with feature engineering client, AutoML, VectorSearch, Delta Live Tables, AutoLoader, and Workflows Integrate open-source and third-party applications, such as OpenAI's ChatGPT, into your AI projects Communicate insights through Databricks SQL dashboards and Delta Sharing Explore data and models through the Databricks marketplace Who this book is for This book is for machine learning engineers, data scientists, and technical managers seeking hands-on expertise in implementing and leveraging the Databricks Data Intelligence Platform and its Lakehouse architecture to create data products.
  databricks data science and engineering workspace: Azure Data Engineering Cookbook Ahmad Osama, 2021-04-05 Over 90 recipes to help you orchestrate modern ETL/ELT workflows and perform analytics using Azure services more easily Key FeaturesBuild highly efficient ETL pipelines using the Microsoft Azure Data servicesCreate and execute real-time processing solutions using Azure Databricks, Azure Stream Analytics, and Azure Data ExplorerDesign and execute batch processing solutions using Azure Data FactoryBook Description Data engineering is one of the faster growing job areas as Data Engineers are the ones who ensure that the data is extracted, provisioned and the data is of the highest quality for data analysis. This book uses various Azure services to implement and maintain infrastructure to extract data from multiple sources, and then transform and load it for data analysis. It takes you through different techniques for performing big data engineering using Microsoft Azure Data services. It begins by showing you how Azure Blob storage can be used for storing large amounts of unstructured data and how to use it for orchestrating a data workflow. You'll then work with different Cosmos DB APIs and Azure SQL Database. Moving on, you'll discover how to provision an Azure Synapse database and find out how to ingest and analyze data in Azure Synapse. As you advance, you'll cover the design and implementation of batch processing solutions using Azure Data Factory, and understand how to manage, maintain, and secure Azure Data Factory pipelines. You'll also design and implement batch processing solutions using Azure Databricks and then manage and secure Azure Databricks clusters and jobs. In the concluding chapters, you'll learn how to process streaming data using Azure Stream Analytics and Data Explorer. By the end of this Azure book, you'll have gained the knowledge you need to be able to orchestrate batch and real-time ETL workflows in Microsoft Azure. What you will learnUse Azure Blob storage for storing large amounts of unstructured dataPerform CRUD operations on the Cosmos Table APIImplement elastic pools and business continuity with Azure SQL DatabaseIngest and analyze data using Azure Synapse AnalyticsDevelop Data Factory data flows to extract data from multiple sourcesManage, maintain, and secure Azure Data Factory pipelinesProcess streaming data using Azure Stream Analytics and Data ExplorerWho this book is for This book is for Data Engineers, Database administrators, Database developers, and extract, load, transform (ETL) developers looking to build expertise in Azure Data engineering using a recipe-based approach. Technical architects and database architects with experience in designing data or ETL applications either on-premise or on any other cloud vendor who wants to learn Azure Data engineering concepts will also find this book useful. Prior knowledge of Azure fundamentals and data engineering concepts is needed.
  databricks data science and engineering workspace: Practical DataOps Harvinder Atwal, 2019-12-09 Gain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making. Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data producers, data consumers, and the rest of the organization through the adoption of lean thinking and agile software development principles. This book helps you to improve the speed and accuracy of analytical application development through data management and DevOps practices that securely expand data access, and rapidly increase the number of reproducible data products through automation, testing, and integration. The book also shows how to collect feedback and monitor performance to manage and continuously improve your processes and output. What You Will LearnDevelop a data strategy for your organization to help it reach its long-term goals Recognize and eliminate barriers to delivering data to users at scale Work on the right things for the right stakeholders through agile collaboration Create trust in data via rigorous testing and effective data management Build a culture of learning and continuous improvement through monitoring deployments and measuring outcomes Create cross-functional self-organizing teams focused on goals not reporting lines Build robust, trustworthy, data pipelines in support of AI, machine learning, and other analytical data products Who This Book Is For Data science and advanced analytics experts, CIOs, CDOs (chief data officers), chief analytics officers, business analysts, business team leaders, and IT professionals (data engineers, developers, architects, and DBAs) supporting data teams who want to dramatically increase the value their organization derives from data. The book is ideal for data professionals who want to overcome challenges of long delivery time, poor data quality, high maintenance costs, and scaling difficulties in getting data science output and machine learning into customer-facing production.
  databricks data science and engineering workspace: Microsoft Certified: AI-900: Microsoft Azure AI Fundamentals Cybellium, 2024-09-01 Welcome to the forefront of knowledge with Cybellium, your trusted partner in mastering the cutting-edge fields of IT, Artificial Intelligence, Cyber Security, Business, Economics and Science. Designed for professionals, students, and enthusiasts alike, our comprehensive books empower you to stay ahead in a rapidly evolving digital world. * Expert Insights: Our books provide deep, actionable insights that bridge the gap between theory and practical application. * Up-to-Date Content: Stay current with the latest advancements, trends, and best practices in IT, Al, Cybersecurity, Business, Economics and Science. Each guide is regularly updated to reflect the newest developments and challenges. * Comprehensive Coverage: Whether you're a beginner or an advanced learner, Cybellium books cover a wide range of topics, from foundational principles to specialized knowledge, tailored to your level of expertise. Become part of a global network of learners and professionals who trust Cybellium to guide their educational journey. www.cybellium.com
  databricks data science and engineering workspace: Databricks Lakehouse Platform Cookbook Dr. Alan L. Dennis, 2023-12-18 Analyze, Architect, and Innovate with Databricks Lakehouse KEY FEATURES ● Create a Lakehouse using Databricks, including ingestion from source to Bronze. ● Refinement of Bronze items to business-ready Silver items using incremental methods. ● Construct Gold items to service the needs of various business requirements. DESCRIPTION The Databricks Lakehouse is groundbreaking technology that simplifies data storage, processing, and analysis. This cookbook offers a clear and practical guide to building and optimizing your Lakehouse to make data-driven decisions and drive impactful results. This definitive guide walks you through the entire Lakehouse journey, from setting up your environment, and connecting to storage, to creating Delta tables, building data models, and ingesting and transforming data. We start off by discussing how to ingest data to Bronze, then refine it to produce Silver. Next, we discuss how to create Gold tables and various data modeling techniques often performed in the Gold layer. You will learn how to leverage Spark SQL and PySpark for efficient data manipulation, apply Delta Live Tables for real-time data processing, and implement Machine Learning and Data Science workflows with MLflow, Feature Store, and AutoML. The book also delves into advanced topics like graph analysis, data governance, and visualization, equipping you with the necessary knowledge to solve complex data challenges. By the end of this cookbook, you will be a confident Lakehouse expert, capable of designing, building, and managing robust data-driven solutions. WHAT YOU WILL LEARN ● Design and build a robust Databricks Lakehouse environment. ● Create and manage Delta tables with advanced transformations. ● Analyze and transform data using SQL and Python. ● Build and deploy machine learning models for actionable insights. ● Implement best practices for data governance and security. WHO THIS BOOK IS FOR This book is meant for Data Engineers, Data Analysts, Data Scientists, Business intelligence professionals, and Architects who want to go to the next level of Data Engineering using the Databricks platform to construct Lakehouses. TABLE OF CONTENTS 1. Introduction to Databricks Lakehouse 2. Setting Up a Databricks Workspace 3. Connecting to Storage 4. Creating Delta Tables 5. Data Profiling and Modeling in the Lakehouse 6. Extracting from Source and Loading to Bronze 7. Transforming to Create Silver 8. Transforming to Create Gold for Business Purposes 9. Machine Learning and Data Science 10. SQL Analysis 11. Graph Analysis 12. Visualizations 13. Governance 14. Operations 15. Tips, Tricks, Troubleshooting, and Best Practices
  databricks data science and engineering workspace: Azure Data Engineer Associate Certification Guide Giacinto Palmieri, Surendra Mettapalli, Newton Alex, 2024-05-23 Achieve Azure Data Engineer Associate certification success with this DP-203 exam guide Purchase of this book unlocks access to web-based exam prep resources including mock exams, flashcards, and exam tips, and the eBook PDF Key Features Prepare for the DP-203 exam with expert insights, real-world examples, and practice resources Gain up-to-date skills to thrive in the dynamic world of cloud data engineering Build secure and sustainable data solutions using Azure services Book DescriptionOne of the top global cloud providers, Azure offers extensive data hosting and processing services, driving widespread cloud adoption and creating a high demand for skilled data engineers. The Azure Data Engineer Associate (DP-203) certification is a vital credential, demonstrating your proficiency as an Azure data engineer to prospective employers. This comprehensive exam guide is designed for both beginners and seasoned professionals, aligned with the latest DP-203 certification exam, to help you pass the exam on your first try. The book provides a foundational understanding of IaaS, PaaS, and SaaS, starting with core concepts like virtual machines (VMs), VNETS, and App Services and progressing to advanced topics such as data storage, processing, and security. What sets this exam guide apart is its hands-on approach, seamlessly integrating theory with practice through real-world examples, practical exercises, and insights into Azure's evolving ecosystem. Additionally, you'll unlock lifetime access to supplementary practice material on an online platform, including mock exams, interactive flashcards, and exam tips, ensuring a comprehensive exam prep experience. By the end of this book, you’ll not only be ready to excel in the DP-203 exam, but also be equipped to tackle complex challenges as an Azure data engineer.What you will learn Design and implement data lake solutions with batch and stream pipelines Secure data with masking, encryption, RBAC, and ACLs Perform standard extract, transform, and load (ETL) and analytics operations Implement different table geometries in Azure Synapse Analytics Write Spark code, design ADF pipelines, and handle batch and stream data Use Azure Databricks or Synapse Spark for data processing using Notebooks Leverage Synapse Analytics and Purview for comprehensive data exploration Confidently manage VMs, VNETS, App Services, and more Who this book is for This book is for data engineers who want to take the Azure Data Engineer Associate (DP-203) exam and delve deep into the Azure cloud stack. Engineers and product managers new to Azure or preparing for interviews with companies working on Azure technologies will find invaluable hands-on experience with Azure data technologies through this book. A basic understanding of cloud technologies, ETL, and databases will assist with understanding the concepts covered.
  databricks data science and engineering workspace: Azure Data Engineer Associate Certification Guide Newton Alex, 2022-02-28 Become well-versed with data engineering concepts and exam objectives to achieve Azure Data Engineer Associate certification Key Features Understand and apply data engineering concepts to real-world problems and prepare for the DP-203 certification exam Explore the various Azure services for building end-to-end data solutions Gain a solid understanding of building secure and sustainable data solutions using Azure services Book DescriptionAzure is one of the leading cloud providers in the world, providing numerous services for data hosting and data processing. Most of the companies today are either cloud-native or are migrating to the cloud much faster than ever. This has led to an explosion of data engineering jobs, with aspiring and experienced data engineers trying to outshine each other. Gaining the DP-203: Azure Data Engineer Associate certification is a sure-fire way of showing future employers that you have what it takes to become an Azure Data Engineer. This book will help you prepare for the DP-203 examination in a structured way, covering all the topics specified in the syllabus with detailed explanations and exam tips. The book starts by covering the fundamentals of Azure, and then takes the example of a hypothetical company and walks you through the various stages of building data engineering solutions. Throughout the chapters, you'll learn about the various Azure components involved in building the data systems and will explore them using a wide range of real-world use cases. Finally, you’ll work on sample questions and answers to familiarize yourself with the pattern of the exam. By the end of this Azure book, you'll have gained the confidence you need to pass the DP-203 exam with ease and land your dream job in data engineering.What you will learn Gain intermediate-level knowledge of Azure the data infrastructure Design and implement data lake solutions with batch and stream pipelines Identify the partition strategies available in Azure storage technologies Implement different table geometries in Azure Synapse Analytics Use the transformations available in T-SQL, Spark, and Azure Data Factory Use Azure Databricks or Synapse Spark to process data using Notebooks Design security using RBAC, ACL, encryption, data masking, and more Monitor and optimize data pipelines with debugging tips Who this book is for This book is for data engineers who want to take the DP-203: Azure Data Engineer Associate exam and are looking to gain in-depth knowledge of the Azure cloud stack. The book will also help engineers and product managers who are new to Azure or interviewing with companies working on Azure technologies, to get hands-on experience of Azure data technologies. A basic understanding of cloud technologies, extract, transform, and load (ETL), and databases will help you get the most out of this book.
  databricks data science and engineering workspace: Spark: The Definitive Guide Bill Chambers, Matei Zaharia, 2018-02-08 Learn how to use, deploy, and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. With an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals. Youâ??ll explore the basic operations and common functions of Sparkâ??s structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Sparkâ??s scalable machine-learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasetsâ??Sparkâ??s core APIsâ??through worked examples Dive into Sparkâ??s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Sparkâ??s stream-processing engine Learn how you can apply MLlib to a variety of problems, including classification or recommendation
  databricks data science and engineering workspace: Mastering Azure Machine Learning Kaijisse Waaijer, Christoph Körner, 2020-04-30 This book will help you learn how to build a scalable end-to-end machine learning pipeline in Azure from experimentation and training to optimization and deployment. By the end of this book, you will learn to build complex distributed systems and scalable cloud infrastructure using powerful machine learning algorithms to compute insights.
  databricks data science and engineering workspace: Data Lake Analytics on Microsoft Azure Harsh Chawla, Pankaj Khattar, 2020-11-15 Get a 360-degree view of how the journey of data analytics solutions has evolved from monolithic data stores and enterprise data warehouses to data lakes and modern data warehouses. You will This book includes comprehensive coverage of how: To architect data lake analytics solutions by choosing suitable technologies available on Microsoft Azure The advent of microservices applications covering ecommerce or modern solutions built on IoT and how real-time streaming data has completely disrupted this ecosystem These data analytics solutions have been transformed from solely understanding the trends from historical data to building predictions by infusing machine learning technologies into the solutions Data platform professionals who have been working on relational data stores, non-relational data stores, and big data technologies will find the content in this book useful. The book also can help you start your journey into the data engineer world as it provides an overview of advanced data analytics and touches on data science concepts and various artificial intelligence and machine learning technologies available on Microsoft Azure. What Will You Learn You will understand the: Concepts of data lake analytics, the modern data warehouse, and advanced data analytics Architecture patterns of the modern data warehouse and advanced data analytics solutions Phases—such as Data Ingestion, Store, Prep and Train, and Model and Serve—of data analytics solutions and technology choices available on Azure under each phase In-depth coverage of real-time and batch mode data analytics solutions architecture Various managed services available on Azure such as Synapse analytics, event hubs, Stream analytics, CosmosDB, and managed Hadoop services such as Databricks and HDInsight Who This Book Is For Data platform professionals, database architects, engineers, and solution architects
  databricks data science and engineering workspace: Computer Networks and Inventive Communication Technologies S. Smys, Pavel Lafata, Ram Palanisamy, Khaled A. Kamel, 2022-10-13 This book is a collection of peer-reviewed best selected research papers presented at 5th International Conference on Computer Networks and Inventive Communication Technologies (ICCNCT 2022). The book covers new results in theory, methodology, and applications of computer networks and data communications. It includes original papers on computer networks, network protocols and wireless networks, data communication technologies, and network security. The proceedings of this conference is a valuable resource, dealing with both the important core and the specialized issues in the areas of next generation wireless network design, control, and management, as well as in the areas of protection, assurance, and trust in information security practice. It is a reference for researchers, instructors, students, scientists, engineers, managers, and industry practitioners for advance work in the area.
  databricks data science and engineering workspace: Data Engineering with Python Paul Crickard, 2020-10-23 Build, monitor, and manage real-time data pipelines to create data engineering infrastructure efficiently using open-source Apache projects Key Features Become well-versed in data architectures, data preparation, and data optimization skills with the help of practical examples Design data models and learn how to extract, transform, and load (ETL) data using Python Schedule, automate, and monitor complex data pipelines in production Book DescriptionData engineering provides the foundation for data science and analytics, and forms an important part of all businesses. This book will help you to explore various tools and methods that are used for understanding the data engineering process using Python. The book will show you how to tackle challenges commonly faced in different aspects of data engineering. You’ll start with an introduction to the basics of data engineering, along with the technologies and frameworks required to build data pipelines to work with large datasets. You’ll learn how to transform and clean data and perform analytics to get the most out of your data. As you advance, you'll discover how to work with big data of varying complexity and production databases, and build data pipelines. Using real-world examples, you’ll build architectures on which you’ll learn how to deploy data pipelines. By the end of this Python book, you’ll have gained a clear understanding of data modeling techniques, and will be able to confidently build data engineering pipelines for tracking data, running quality checks, and making necessary changes in production.What you will learn Understand how data engineering supports data science workflows Discover how to extract data from files and databases and then clean, transform, and enrich it Configure processors for handling different file formats as well as both relational and NoSQL databases Find out how to implement a data pipeline and dashboard to visualize results Use staging and validation to check data before landing in the warehouse Build real-time pipelines with staging areas that perform validation and handle failures Get to grips with deploying pipelines in the production environment Who this book is for This book is for data analysts, ETL developers, and anyone looking to get started with or transition to the field of data engineering or refresh their knowledge of data engineering using Python. This book will also be useful for students planning to build a career in data engineering or IT professionals preparing for a transition. No previous knowledge of data engineering is required.
  databricks data science and engineering workspace: Machine Learning with Microsoft Technologies Leila Etaati, 2019-06-12 Know how to do machine learning with Microsoft technologies. This book teaches you to do predictive, descriptive, and prescriptive analyses with Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, HD Insight, and more. The ability to analyze massive amounts of real-time data and predict future behavior of an organization is critical to its long-term success. Data science, and more specifically machine learning (ML), is today’s game changer and should be a key building block in every company’s strategy. Managing a machine learning process from business understanding, data acquisition and cleaning, modeling, and deployment in each tool is a valuable skill set. Machine Learning with Microsoft Technologies is a demo-driven book that explains how to do machine learning with Microsoft technologies. You will gain valuable insight into designing the best architecture for development, sharing, and deploying a machine learning solution. This book simplifies the process of choosing the right architecture and tools for doing machine learning based on your specific infrastructure needs and requirements. Detailed content is provided on the main algorithms for supervised and unsupervised machine learning and examples show ML practices using both R and Python languages, the main languages inside Microsoft technologies. What You'll Learn Choose the right Microsoft product for your machine learning solutionCreate and manage Microsoft’s tool environments for development, testing, and production of a machine learning projectImplement and deploy supervised and unsupervised learning in Microsoft products Set up Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, and HD Insight to perform machine learning Set up a data science virtual machine and test-drive installed tools, such as Azure ML Workbench, Azure ML Server Developer, Anaconda Python, Jupyter Notebook, Power BI Desktop, Cognitive Services, machine learning and data analytics tools, and more Architect a machine learning solution factoring in all aspects of self service, enterprise, deployment, and sharing Who This Book Is For Data scientists, data analysts, developers, architects, and managers who want to leverage machine learning in their products, organization, and services, and make educated, cost-saving decisions about their ML architecture and tool set.
Databricks: Leading Data and AI Solutions for Enterprises
Databricks offers a unified platform for data, analytics and AI. Build better AI with a data-centric approach. Simplify ETL, data warehousing, governance and AI on the Data Intelligence Platform.

What is Databricks? | Databricks Documentation
May 5, 2025 · What is Databricks? Databricks is a unified, open analytics platform for building, deploying, sharing, and maintaining enterprise-grade data, analytics, and AI solutions at scale. …

About Databricks: The data and AI company
Headquartered in San Francisco, with offices around the world, Databricks is on a mission to simplify and democratize data and AI, helping data and AI teams solve the world’s toughest …

Learn Databricks - Training & Resources | Databricks
Explore Databricks resources for data and AI, including training, certification, events, and community support to enhance your skills.

データとAIの企業：未来をリードするデータインテリジェンスプ …
Databricks のデータプラットフォームは、ETL、データの取り込み、BI、AI、ガバナンスのための現行ツールと統合します。 有効なツールはそのままで、新たなツールを採用できます。

Databricks IQ: AI-Driven Analytics for Faster Data Insights
Databricks IQ powers AI-driven analytics to help you derive faster insights, optimize decision-making, and scale your data analytics workflows with ease.

Databricks components | Databricks Documentation
Learn fundamental Databricks components such as workspaces, data objects, clusters, machine learning models, and access.

Data Lakehouse Architecture - Databricks
The Databricks Data Intelligence Platform is built on lakehouse architecture, which combines the best elements of data lakes and data warehouses to help you reduce costs and deliver on …

Get started tutorials on Databricks
May 13, 2025 · Build a machine learning classification model using the scikit-learn library on Databricks to predict whether a wine is considered “high-quality”. This tutorial also illustrates …

Data Science with Databricks Platform | Databricks
Write code in Python, R, Scala and SQL, explore data with interactive visualizations and discover new insights with Databricks Notebooks. Confidently and securely share code with …

Databricks: Leading Data and AI Solutions for Enterprises
Databricks offers a unified platform for data, analytics and AI. Build better AI with a data-centric approach. Simplify ETL, data warehousing, governance and AI on the Data Intelligence Platform.

What is Databricks? | Databricks Documentation
May 5, 2025 · What is Databricks? Databricks is a unified, open analytics platform for building, deploying, sharing, and maintaining enterprise-grade data, analytics, and AI solutions at scale. …

About Databricks: The data and AI company
Headquartered in San Francisco, with offices around the world, Databricks is on a mission to simplify and democratize data and AI, helping data and AI teams solve the world’s toughest …

Learn Databricks - Training & Resources | Databricks
Explore Databricks resources for data and AI, including training, certification, events, and community support to enhance your skills.

データとAIの企業：未来をリードするデータインテリジェンスプ …
Databricks のデータプラットフォームは、ETL、データの取り込み、BI、AI、ガバナンスのための現行ツールと統合します。 有効なツールはそのままで、新たなツールを採用できます。

Databricks IQ: AI-Driven Analytics for Faster Data Insights
Databricks IQ powers AI-driven analytics to help you derive faster insights, optimize decision-making, and scale your data analytics workflows with ease.

Databricks components | Databricks Documentation
Learn fundamental Databricks components such as workspaces, data objects, clusters, machine learning models, and access.

Data Lakehouse Architecture - Databricks
The Databricks Data Intelligence Platform is built on lakehouse architecture, which combines the best elements of data lakes and data warehouses to help you reduce costs and deliver on …

Get started tutorials on Databricks
May 13, 2025 · Build a machine learning classification model using the scikit-learn library on Databricks to predict whether a wine is considered “high-quality”. This tutorial also illustrates …

Data Science with Databricks Platform | Databricks
Write code in Python, R, Scala and SQL, explore data with interactive visualizations and discover new insights with Databricks Notebooks. Confidently and securely share code with …