| databricks technical interview questions: 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 technical interview questions: DataBricks® PySpark 2.x Certification Practice Questions , This book contains the questions answers and some FAQ about the Databricks Spark Certification for version 2.x, which is the latest release from Apache Spark. In this book we will be having in total 75 practice questions. Almost all required question would have in detail explanation to the questions and answers, wherever required. Don’t consider this book as a guide, it is more of question and answer practice book. This book also give some references as well like how to prepare further to ensure that you clear the certification exam. This book will particularly focus on the Python version of the certification preparation material. Please note these are practice questions and not dumps, hence just memorizing the question and answers will not help in the real exam. You need to understand the concepts in detail as well as you should be able to solve the programming questions at the end in real worlds work you should be able to write code using PySpark whether you are Data Engineer, Data Analytics Engineer, Data Scientists or Programmer. Hence, take the opportunity to learn each question and also go through the explanation of the questions. |
| databricks technical interview questions: Interview Questions and Answers Richard McMunn, 2013-05 |
| databricks technical interview questions: Ace the Data Science Interview Kevin Huo, Nick Singh, 2021 |
| databricks technical interview questions: 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 technical interview questions: 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 technical interview questions: 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 technical interview questions: Building Analytics Teams John K. Thompson, Douglas B. Laney, 2020-06-30 Master the skills necessary to hire and manage a team of highly skilled individuals to design, build, and implement applications and systems based on advanced analytics and AI Key FeaturesLearn to create an operationally effective advanced analytics team in a corporate environmentSelect and undertake projects that have a high probability of success and deliver the improved top and bottom-line resultsUnderstand how to create relationships with executives, senior managers, peers, and subject matter experts that lead to team collaboration, increased funding, and long-term success for you and your teamBook Description In Building Analytics Teams, John K. Thompson, with his 30+ years of experience and expertise, illustrates the fundamental concepts of building and managing a high-performance analytics team, including what to do, who to hire, projects to undertake, and what to avoid in the journey of building an analytically sound team. The core processes in creating an effective analytics team and the importance of the business decision-making life cycle are explored to help achieve initial and sustainable success. The book demonstrates the various traits of a successful and high-performing analytics team and then delineates the path to achieve this with insights on the mindset, advanced analytics models, and predictions based on data analytics. It also emphasizes the significance of the macro and micro processes required to evolve in response to rapidly changing business needs. The book dives into the methods and practices of managing, developing, and leading an analytics team. Once you've brought the team up to speed, the book explains how to govern executive expectations and select winning projects. By the end of this book, you will have acquired the knowledge to create an effective business analytics team and develop a production environment that delivers ongoing operational improvements for your organization. What you will learnAvoid organizational and technological pitfalls of moving from a defined project to a production environmentEnable team members to focus on higher-value work and tasksBuild Advanced Analytics and Artificial Intelligence (AA&AI) functions in an organizationOutsource certain projects to competent and capable third partiesSupport the operational areas that intend to invest in business intelligence, descriptive statistics, and small-scale predictive analyticsAnalyze the operational area, the processes, the data, and the organizational resistanceWho this book is for This book is for senior executives, senior and junior managers, and those who are working as part of a team that is accountable for designing, building, delivering and ensuring business success through advanced analytics and artificial intelligence systems and applications. At least 5 to 10 years of experience in driving your organization to a higher level of efficiency will be helpful. |
| databricks technical interview questions: Hadoop Administration : Apache Ambari Interview Questions Rashmi Shah, Hadoop Admin: Apache Ambari interview Questions which include the 118 questions in total and it will prepare you for the Hadoop Administration. It is not necessary this all questions would be asked during the interview process. But HadoopExam tries to cover all possible concepts which needs to learn for knowing the Apache Ambari Hadoop Cluster management tool. These questions and answer would be helpful to understand the various components, operations, monitoring and administering the Hadoop cluster for sure. The benefit of Question and answer format is that, it would allow you to understand the thing in depth and you can get the better insight on the subject. This book was created by the Engineering team of HadoopExam which has in depth knowledge about the Hadoop Cluster Administration and Created HandsOn Hadoop Administration training. The team target is to make you learn the subject as in depth as possible with the minimum effort hence we have material in Question, Answers format, On-demand video trainings, E-Books, Projects and POC etc. We are delighted when learners come and give the feedback about our material and become repeat subscriber because they regularly get new material as well as updated material. Again all the best and please provide the feedback on the admin@hadoopexam.com or hadoopexam@gmail.com . Wherever possible we are trying to help you in your career. |
| databricks technical interview questions: Oracle PL/SQL Best Practices Steven Feuerstein, 2001-04-09 In this book, Steven Feuerstein, widely recognized as one of the world's experts on the Oracle PL/SQL language, distills his many years of programming, writing, and teaching about PL/SQL into a set of PL/SQL language best practices--rules for writing code that is readable, maintainable, and efficient. Too often, developers focus on simply writing programs that run without errors--and ignore the impact of poorly written code upon both system performance and their ability (and their colleagues' ability) to maintain that code over time.Oracle PL/SQL Best Practices is a concise, easy-to-use reference to Feuerstein's recommendations for excellent PL/SQL coding. It answers the kinds of questions PL/SQL developers most frequently ask about their code: How should I format my code? What naming conventions, if any, should I use? How can I write my packages so they can be more easily maintained? What is the most efficient way to query information from the database? How can I get all the developers on my team to handle errors the same way? The book contains 120 best practices, divided by topic area. It's full of advice on the program development process, coding style, writing SQL in PL/SQL, data structures, control structures, exception handling, program and package construction, and built-in packages. It also contains a handy, pull-out quick reference card. As a helpful supplement to the text, code examples demonstrating each of the best practices are available on the O'Reilly web site.Oracle PL/SQL Best Practices is intended as a companion to O'Reilly's larger Oracle PL/SQL books. It's a compact, readable reference that you'll turn to again and again--a book that no serious developer can afford to be without. |
| databricks technical interview questions: 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 technical interview questions: Guide for Databricks® Spark Python (PySpark) CRT020 Certification Rashmi Shah, Apache® Spark is one of the fastest growing technology in BigData computing world. It supports multiple programming languages like Java, Scala, Python and R. Hence, many existing and new framework started to integrate Spark platform as well in their platform for instance Hadoop, Cassandra, EMR etc. While creating Spark certification material HadoopExam Engineering team found that there is no proper material and book is available for the Spark (version 2.x) which covers the concepts as well as use of various features and found difficulty in creating the material. Therefore, they decided to create full length book for Spark (Databricks® CRT020 Spark Scala/Python or PySpark Certification) and outcome of that is this book. In this book technical team try to cover both fundamental concepts of Spark 2.x topics which are part of the certification syllabus as well as add as many exercises as possible and in current version we have around 46 hands on exercises added which you can execute on the Databricks community edition, because each of this exercises tested on that platform as well, as this book is focused on the PySpark version of the certification, hence all the exercises and their solution provided in the Python. This book is divided in 13 chapters, as you move ahead chapter by chapter you would be comfortable with the Databricks Spark Python certification (CRT020). Same exercises you can convert into different programming language like Java, Scala & R as well. Its more about the syntax. |
| databricks technical interview questions: A Practical Guide To Quantitative Finance Interviews Xinfeng Zhou, 2020-05-05 This book will prepare you for quantitative finance interviews by helping you zero in on the key concepts that are frequently tested in such interviews. In this book we analyze solutions to more than 200 real interview problems and provide valuable insights into how to ace quantitative interviews. The book covers a variety of topics that you are likely to encounter in quantitative interviews: brain teasers, calculus, linear algebra, probability, stochastic processes and stochastic calculus, finance and programming. |
| databricks technical interview questions: Java/J2EE Job Interview Companion Arulkumaran Kumaraswamipillai, A. Sivayini, 2007 400+ Java/J2EE Interview questions with clear and concise answers for: job seekers (junior/senior developers, architects, team/technical leads), promotion seekers, pro-active learners and interviewers. Lulu top 100 best seller. Increase your earning potential by learning, applying and succeeding. Learn the fundamentals relating to Java/J2EE in an easy to understand questions and answers approach. Covers 400+ popular interview Q&A with lots of diagrams, examples, code snippets, cross referencing and comparisons. This is not only an interview guide but also a quick reference guide, a refresher material and a roadmap covering a wide range of Java/J2EE related topics. More Java J2EE interview questions and answers & resume resources at http: //www.lulu.com/java-succes |
| databricks technical interview questions: SQL and NoSQL Interview Questions Vishwanathan Narayanan, 2023-06-05 A comprehensive guide to SQL and NoSQL interview questions for software professionals KEY FEATURES ● Get familiar with different concepts and queries in SQL. ● Comprehensive coverage of different types of NoSQL databases. ● Understand the performance tuning strategies and best practices for NoSQL databases. DESCRIPTION In every software-based job interview, database systems will undoubtedly be a topic of discussion. It has become customary to ask at least a few database-related questions. As NoSQL technologies continue to gain popularity, asking about their functionality and practical applications during interviews is becoming more commonplace. This book focuses on these two areas, aiming to familiarize you with the types of questions you may encounter in interviews and providing guidance on preparing and strategizing accordingly. This book thoroughly explores the NoSQL family, covering everything from the fundamentals to advanced topics such as architecture, optimization, and practical use cases. It also includes a selection of frequently asked questions from a query perspective. Moreover, this book is designed to assist you in last-minute revisions. This book also tackles a common interview challenge of effectively communicating complex concepts in a clear and concise manner, even if you have a strong understanding of the subject matter. By the end of the book, you will be well-equipped to handle interviews and confidently answer queries related to both, database systems and NoSQL. WHAT YOU WILL LEARN ● Get an in-depth understanding of Relational Databases. ● Understand the differences between Relational databases and NoSQL databases. ● Explore the architecture for each type of NoSQL database. ● Get insights into the application areas of each type of NoSQL database. ● Understand the paradigm shift in designing NoSQL schema and queries. WHO THIS BOOK IS FOR This book is for current and aspiring emerging tech professionals, students, and anyone who wishes to have a rewarding career in emerging technologies such as Relational database and NOSQL. TABLE OF CONTENTS 1. Relational Database 2. NoSQL 3. MongoDB 4. Cassandra 5. Redis 6. HBase 7. Elasticsearch 8. Neo4j |
| databricks technical interview questions: Deep Learning and the Game of Go Kevin Ferguson, Max Pumperla, 2019-01-06 Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword by Thore Graepel, DeepMind Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios! What's inside Build and teach a self-improving game AI Enhance classical game AI systems with deep learning Implement neural networks for deep learning About the Reader All you need are basic Python skills and high school-level math. No deep learning experience required. About the Author Max Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo. Table of Contents PART 1 - FOUNDATIONS Toward deep learning: a machine-learning introduction Go as a machine-learning problem Implementing your first Go bot PART 2 - MACHINE LEARNING AND GAME AI Playing games with tree search Getting started with neural networks Designing a neural network for Go data Learning from data: a deep-learning bot Deploying bots in the wild Learning by practice: reinforcement learning Reinforcement learning with policy gradients Reinforcement learning with value methods Reinforcement learning with actor-critic methods PART 3 - GREATER THAN THE SUM OF ITS PARTS AlphaGo: Bringing it all together AlphaGo Zero: Integrating tree search with reinforcement learning |
| databricks technical interview questions: SAS Base Interview Questions Rashmi Shah, SAS® is one of the fastest growing and matured software solutions for the analytics worlds and recent development in the Machine Learning and Artificial intelligence made this SAS software even more useful and well-integrated with BigData computing world. It has its own programming languages which is popularly known as Base SAS and if you want to learn and become expert for the SAS then you must learn this SAS Base programing. In this book we are covering around 165 SAS Base interview questions and answers which are popularly asked in the interview and must aware all this concept covered. In this book we are not covering advanced concepts like Machine Learning, Data science, Artificial intelligence, Big Data etc., there would be separate book launched for the same. This book also helps for the learners who are preparing for the SAS certification like A00-215, A00-231 & A00-232 global SAS certification which include both multiple choice as well as project-based questions and answers. However, for complete questions and answer please visit our website and you can get the same questions and answer in video cum audio book. You must go through this Question and Answer before your real SAS interview questions and keep this book handy if you are working or plan to work in the SAS world. On regular basis we would be updating this book based on the learners feedback and more interview questions would be added, hence it is always recommended that you have access to the latest edition of the book. |
| databricks technical interview questions: Machine Learning Bookcamp Alexey Grigorev, 2021-11-23 The only way to learn is to practice! In Machine Learning Bookcamp, you''ll create and deploy Python-based machine learning models for a variety of increasingly challenging projects. Taking you from the basics of machine learning to complex applications such as image and text analysis, each new project builds on what you''ve learned in previous chapters. By the end of the bookcamp, you''ll have built a portfolio of business-relevant machine learning projects that hiring managers will be excited to see. about the technology Machine learning is an analysis technique for predicting trends and relationships based on historical data. As ML has matured as a discipline, an established set of algorithms has emerged for tackling a wide range of analysis tasks in business and research. By practicing the most important algorithms and techniques, you can quickly gain a footing in this important area. Luckily, that''s exactly what you''ll be doing in Machine Learning Bookcamp. about the book In Machine Learning Bookcamp you''ll learn the essentials of machine learning by completing a carefully designed set of real-world projects. Beginning as a novice, you''ll start with the basic concepts of ML before tackling your first challenge: creating a car price predictor using linear regression algorithms. You''ll then advance through increasingly difficult projects, developing your skills to build a churn prediction application, a flight delay calculator, an image classifier, and more. When you''re done working through these fun and informative projects, you''ll have a comprehensive machine learning skill set you can apply to practical on-the-job problems. what''s inside Code fundamental ML algorithms from scratch Collect and clean data for training models Use popular Python tools, including NumPy, Pandas, Scikit-Learn, and TensorFlow Apply ML to complex datasets with images and text Deploy ML models to a production-ready environment about the reader For readers with existing programming skills. No previous machine learning experience required. about the author Alexey Grigorev has more than ten years of experience as a software engineer, and has spent the last six years focused on machine learning. Currently, he works as a lead data scientist at the OLX Group, where he deals with content moderation and image models. He is the author of two other books on using Java for data science and TensorFlow for deep learning. |
| databricks technical interview questions: Kubernetes in Action Marko Luksa, 2017-12-14 Summary Kubernetes in Action is a comprehensive guide to effectively developing and running applications in a Kubernetes environment. Before diving into Kubernetes, the book gives an overview of container technologies like Docker, including how to build containers, so that even readers who haven't used these technologies before can get up and running. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Kubernetes is Greek for helmsman, your guide through unknown waters. The Kubernetes container orchestration system safely manages the structure and flow of a distributed application, organizing containers and services for maximum efficiency. Kubernetes serves as an operating system for your clusters, eliminating the need to factor the underlying network and server infrastructure into your designs. About the Book Kubernetes in Action teaches you to use Kubernetes to deploy container-based distributed applications. You'll start with an overview of Docker and Kubernetes before building your first Kubernetes cluster. You'll gradually expand your initial application, adding features and deepening your knowledge of Kubernetes architecture and operation. As you navigate this comprehensive guide, you'll explore high-value topics like monitoring, tuning, and scaling. What's Inside Kubernetes' internals Deploying containers across a cluster Securing clusters Updating applications with zero downtime About the Reader Written for intermediate software developers with little or no familiarity with Docker or container orchestration systems. About the Author Marko Luksa is an engineer at Red Hat working on Kubernetes and OpenShift. Table of Contents PART 1 - OVERVIEW Introducing Kubernetes First steps with Docker and Kubernetes PART 2 - CORE CONCEPTS Pods: running containers in Kubernetes Replication and other controllers: deploying managed pods Services: enabling clients to discover and talk to pods Volumes: attaching disk storage to containers ConfigMaps and Secrets: configuring applications Accessing pod metadata and other resources from applications Deployments: updating applications declaratively StatefulSets: deploying replicated stateful applications PART 3 - BEYOND THE BASICS Understanding Kubernetes internals Securing the Kubernetes API server Securing cluster nodes and the network Managing pods' computational resources Automatic scaling of pods and cluster nodes Advanced scheduling Best practices for developing apps Extending Kubernetes |
| databricks technical interview questions: Pragmatic AI Noah Gift, 2018-07-12 Master Powerful Off-the-Shelf Business Solutions for AI and Machine Learning Pragmatic AI will help you solve real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Noah Gift demystifies all the concepts and tools you need to get results—even if you don’t have a strong background in math or data science. Gift illuminates powerful off-the-shelf cloud offerings from Amazon, Google, and Microsoft, and demonstrates proven techniques using the Python data science ecosystem. His workflows and examples help you streamline and simplify every step, from deployment to production, and build exceptionally scalable solutions. As you learn how machine language (ML) solutions work, you’ll gain a more intuitive understanding of what you can achieve with them and how to maximize their value. Building on these fundamentals, you’ll walk step-by-step through building cloud-based AI/ML applications to address realistic issues in sports marketing, project management, product pricing, real estate, and beyond. Whether you’re a business professional, decision-maker, student, or programmer, Gift’s expert guidance and wide-ranging case studies will prepare you to solve data science problems in virtually any environment. Get and configure all the tools you’ll need Quickly review all the Python you need to start building machine learning applications Master the AI and ML toolchain and project lifecycle Work with Python data science tools such as IPython, Pandas, Numpy, Juypter Notebook, and Sklearn Incorporate a pragmatic feedback loop that continually improves the efficiency of your workflows and systems Develop cloud AI solutions with Google Cloud Platform, including TPU, Colaboratory, and Datalab services Define Amazon Web Services cloud AI workflows, including spot instances, code pipelines, boto, and more Work with Microsoft Azure AI APIs Walk through building six real-world AI applications, from start to finish Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. |
| databricks technical interview questions: Data Pipelines Pocket Reference James Densmore, 2021-02-10 Data pipelines are the foundation for success in data analytics. Moving data from numerous diverse sources and transforming it to provide context is the difference between having data and actually gaining value from it. This pocket reference defines data pipelines and explains how they work in today's modern data stack. You'll learn common considerations and key decision points when implementing pipelines, such as batch versus streaming data ingestion and build versus buy. This book addresses the most common decisions made by data professionals and discusses foundational concepts that apply to open source frameworks, commercial products, and homegrown solutions. You'll learn: What a data pipeline is and how it works How data is moved and processed on modern data infrastructure, including cloud platforms Common tools and products used by data engineers to build pipelines How pipelines support analytics and reporting needs Considerations for pipeline maintenance, testing, and alerting |
| databricks technical interview questions: Blitzscaling: The Lightning-Fast Path to Building Massively Valuable Companies Reid Hoffman, Chris Yeh, 2018-10-09 Foreword by Bill Gates From the authors of New York Times bestsellers, The Alliance and The Start-up of You, comes a smart and accessible must-have guide for budding entrepreneurs everywhere. |
| databricks technical interview questions: 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 technical interview questions: Training Kit (Exam 70-461): Querying Microsoft SQL Server 2012 Itzik Ben-Gan, Dejan Sarka, Ron Talmage, 2012-11 Ace your preparation for Microsoft® Certification Exam 70-461 with this 2-in-1 Training Kit from Microsoft Press®. Work at your own pace through a series of lessons and practical exercises, and then assess your skills with practice tests on CD—featuring multiple, customizable testing options. Maximize your performance on the exam by learning how to: Create database objects Work with data Modify data Troubleshoot and optimize queries You also get an exam discount voucher—making this book an exceptional value and a great career investment. |
| databricks technical interview questions: Hadoop Administrator Interview Questions Rashmi Shah, Cloudera® Enterprise is one of the fastest growing platforms for the BigData computing world, which accommodate various open source tools like CDH, Hive, Impala, HBase and many more as well as licensed products like Cloudera Manager and Cloudera Navigator. There are various organization who had already deployed the Cloudera Enterprise solution in the production env, and running millions of queries and data processing on daily basis. Cloudera Enterprise is such a vast and managed platform, that as individual, cannot manage the entire cluster. Even single administrator cannot have entire cluster knowledge, that’s the reason there is a huge demand for the Cloudera Administrator in the market specially in the North America, Canada, France, UAE, Germany, India etc. Many international investment and retail bank already installed the Cloudera Enterprise in the production environment, Healthcare and retail e-commerce industry which has huge volume of data generated on daily basis do not have a choice and they have to have Hadoop based platform deployed. Cloudera Enterprise is the pioneer and not any other company is close to the Cloudera for the Hadoop Solution, and demand for Cloudera certified Hadoop Administrators are high in demand. That’s the reason HadoopExam is launching Hadoop Administrator Interview Preparation Material, which is specially designed for the Cloudera Enterprise product, you have to go through all the questions mentioned in this book before your real interview. This book certainly helpful for your real interview, however does not guarantee that you will clear that interview or not. In this book we have covered various terminology, concepts, architectural perspective, Impala, Hive, Cloudera Manager, Cloudera Navigator and Some part of Cloudera Altus. We will be continuously upgrading this book. So, you can get the access to most recent material. Please keep in mind this book is written mainly for the Cloudera Enterprise Hadoop Administrator, and it may be helpful if you are working on any other Hadoop Solution provider as well. |
| databricks technical interview questions: How to Lead in Data Science Jike Chong, Yue Cathy Chang, 2021-12-28 A field guide for the unique challenges of data science leadership, filled with transformative insights, personal experiences, and industry examples. In How To Lead in Data Science you will learn: Best practices for leading projects while balancing complex trade-offs Specifying, prioritizing, and planning projects from vague requirements Navigating structural challenges in your organization Working through project failures with positivity and tenacity Growing your team with coaching, mentoring, and advising Crafting technology roadmaps and championing successful projects Driving diversity, inclusion, and belonging within teams Architecting a long-term business strategy and data roadmap as an executive Delivering a data-driven culture and structuring productive data science organizations How to Lead in Data Science is full of techniques for leading data science at every seniority level—from heading up a single project to overseeing a whole company's data strategy. Authors Jike Chong and Yue Cathy Chang share hard-won advice that they've developed building data teams for LinkedIn, Acorns, Yiren Digital, large asset-management firms, Fortune 50 companies, and more. You'll find advice on plotting your long-term career advancement, as well as quick wins you can put into practice right away. Carefully crafted assessments and interview scenarios encourage introspection, reveal personal blind spots, and highlight development areas. About the technology Lead your data science teams and projects to success! To make a consistent, meaningful impact as a data science leader, you must articulate technology roadmaps, plan effective project strategies, support diversity, and create a positive environment for professional growth. This book delivers the wisdom and practical skills you need to thrive as a data science leader at all levels, from team member to the C-suite. About the book How to Lead in Data Science shares unique leadership techniques from high-performance data teams. It’s filled with best practices for balancing project trade-offs and producing exceptional results, even when beginning with vague requirements or unclear expectations. You’ll find a clearly presented modern leadership framework based on current case studies, with insights reaching all the way to Aristotle and Confucius. As you read, you’ll build practical skills to grow and improve your team, your company’s data culture, and yourself. What's inside How to coach and mentor team members Navigate an organization’s structural challenges Secure commitments from other teams and partners Stay current with the technology landscape Advance your career About the reader For data science practitioners at all levels. About the author Dr. Jike Chong and Yue Cathy Chang build, lead, and grow high-performing data teams across industries in public and private companies, such as Acorns, LinkedIn, large asset-management firms, and Fortune 50 companies. Table of Contents 1 What makes a successful data scientist? PART 1 THE TECH LEAD: CULTIVATING LEADERSHIP 2 Capabilities for leading projects 3 Virtues for leading projects PART 2 THE MANAGER: NURTURING A TEAM 4 Capabilities for leading people 5 Virtues for leading people PART 3 THE DIRECTOR: GOVERNING A FUNCTION 6 Capabilities for leading a function 7 Virtues for leading a function PART 4 THE EXECUTIVE: INSPIRING AN INDUSTRY 8 Capabilities for leading a company 9 Virtues for leading a company PART 5 THE LOOP AND THE FUTURE 10 Landscape, organization, opportunity, and practice 11 Leading in data science and a future outlook |
| databricks technical interview questions: Data analytics using R , 2018 |
| databricks technical interview questions: The Data Warehouse Toolkit Ralph Kimball, Margy Ross, 2011-08-08 This old edition was published in 2002. The current and final edition of this book is The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling, 3rd Edition which was published in 2013 under ISBN: 9781118530801. The authors begin with fundamental design recommendations and gradually progress step-by-step through increasingly complex scenarios. Clear-cut guidelines for designing dimensional models are illustrated using real-world data warehouse case studies drawn from a variety of business application areas and industries, including: Retail sales and e-commerce Inventory management Procurement Order management Customer relationship management (CRM) Human resources management Accounting Financial services Telecommunications and utilities Education Transportation Health care and insurance By the end of the book, you will have mastered the full range of powerful techniques for designing dimensional databases that are easy to understand and provide fast query response. You will also learn how to create an architected framework that integrates the distributed data warehouse using standardized dimensions and facts. |
| databricks technical interview questions: Parallel and Concurrent Programming in Haskell Simon Marlow, 2013-07-12 If you have a working knowledge of Haskell, this hands-on book shows you how to use the language’s many APIs and frameworks for writing both parallel and concurrent programs. You’ll learn how parallelism exploits multicore processors to speed up computation-heavy programs, and how concurrency enables you to write programs with threads for multiple interactions. Author Simon Marlow walks you through the process with lots of code examples that you can run, experiment with, and extend. Divided into separate sections on Parallel and Concurrent Haskell, this book also includes exercises to help you become familiar with the concepts presented: Express parallelism in Haskell with the Eval monad and Evaluation Strategies Parallelize ordinary Haskell code with the Par monad Build parallel array-based computations, using the Repa library Use the Accelerate library to run computations directly on the GPU Work with basic interfaces for writing concurrent code Build trees of threads for larger and more complex programs Learn how to build high-speed concurrent network servers Write distributed programs that run on multiple machines in a network |
| databricks technical interview questions: Introducing MLOps Mark Treveil, Nicolas Omont, Clément Stenac, Kenji Lefevre, Du Phan, Joachim Zentici, Adrien Lavoillotte, Makoto Miyazaki, Lynn Heidmann, 2020-11-30 More than half of the analytics and machine learning (ML) models created by organizations today never make it into production. Some of the challenges and barriers to operationalization are technical, but others are organizational. Either way, the bottom line is that models not in production can't provide business impact. This book introduces the key concepts of MLOps to help data scientists and application engineers not only operationalize ML models to drive real business change but also maintain and improve those models over time. Through lessons based on numerous MLOps applications around the world, nine experts in machine learning provide insights into the five steps of the model life cycle--Build, Preproduction, Deployment, Monitoring, and Governance--uncovering how robust MLOps processes can be infused throughout. This book helps you: Fulfill data science value by reducing friction throughout ML pipelines and workflows Refine ML models through retraining, periodic tuning, and complete remodeling to ensure long-term accuracy Design the MLOps life cycle to minimize organizational risks with models that are unbiased, fair, and explainable Operationalize ML models for pipeline deployment and for external business systems that are more complex and less standardized |
| databricks technical interview questions: SAS Certified Specialist Prep Guide SAS Institute, 2019-02-11 The SAS® Certified Specialist Prep Guide: Base Programming Using SAS® 9.4 prepares you to take the new SAS 9.4 Base Programming -- Performance-Based Exam. This is the official guide by the SAS Global Certification Program. This prep guide is for both new and experienced SAS users, and it covers all the objectives that are tested on the exam. New in this edition is a workbook whose sample scenarios require you to write code to solve problems and answer questions. Answers for the chapter quizzes and solutions for the sample scenarios in the workbook are included. You will also find links to exam objectives, practice exams, and other resources such as the Base SAS® glossary and a list of practice data sets. Major topics include importing data, creating and modifying SAS data sets, and identifying and correcting both data syntax and programming logic errors. All exam topics are covered in these chapters: Setting Up Practice Data Basic Concepts Accessing Your Data Creating SAS Data Sets Identifying and Correcting SAS Language Errors Creating Reports Understanding DATA Step Processing BY-Group Processing Creating and Managing Variables Combining SAS Data Sets Processing Data with DO Loops SAS Formats and Informats SAS Date, Time, and Datetime Values Using Functions to Manipulate Data Producing Descriptive Statistics Creating Output Practice Programming Scenarios (Workbook) |
| databricks technical interview questions: Advanced Analytics with Spark Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills, 2015-04-02 In this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—classification, collaborative filtering, and anomaly detection among others—to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you’ll find these patterns useful for working on your own data applications. Patterns include: Recommending music and the Audioscrobbler data set Predicting forest cover with decision trees Anomaly detection in network traffic with K-means clustering Understanding Wikipedia with Latent Semantic Analysis Analyzing co-occurrence networks with GraphX Geospatial and temporal data analysis on the New York City Taxi Trips data Estimating financial risk through Monte Carlo simulation Analyzing genomics data and the BDG project Analyzing neuroimaging data with PySpark and Thunder |
| databricks technical interview questions: Exam Ref AZ-103 Microsoft Azure Administrator Michael Washam, Jonathan Tuliani, Scott Hoag, 2019-01-02 Prepare for Microsoft Exam AZ-103—and help demonstrate your real-world mastery of deploying and managing infrastructure in Microsoft Azure cloud environments. Designed for experienced cloud professionals ready to advance their status, Exam Ref focuses on the critical thinking and decision-making acumen needed for success at the Microsoft Certified Associate level. Focus on the expertise measured by these objectives: Manage Azure subscriptions and resources Implement and manage storage Deploy and manage virtual machines (VMs) Configure and manage virtual networks Manage identities This Microsoft Exam Ref: Organizes its coverage by exam objectives Features strategic, what-if scenarios to challenge you Assumes you are an experienced Azure administrator who understands and manages diverse storage, security, networking and/or compute cloud services About the Exam Exam AZ-103 focuses on skills and knowledge needed to manage Azure subscriptions; analyze resource utilization and consumption; manage resource groups; establish storage accounts; import/export data; configure Azure files; implement backup; create, configure, and automate VM deployment; manage VMs and VM backups; implement, manage, and connect virtual networks; configure name resolution; create and configure Network Security Groups; manage Azure AD and its objects; and implement and manage hybrid identities. About Microsoft Certification Passing exam AZ-103 earns your Microsoft Certified: Azure Administrator Associate certification, demonstrating your skills in implementing, monitoring, and maintaining Microsoft Azure solutions, including major services related to compute, storage, network, and security. |
| databricks technical interview questions: Implementing MLOps in the Enterprise Yaron Haviv, Noah Gift, 2023-11-30 With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production. Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs. You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you: Learn the MLOps process, including its technological and business value Build and structure effective MLOps pipelines Efficiently scale MLOps across your organization Explore common MLOps use cases Build MLOps pipelines for hybrid deployments, real-time predictions, and composite AI Learn how to prepare for and adapt to the future of MLOps Effectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy |
| databricks technical interview questions: Microsoft Azure Security Center Yuri Diogenes, Tom Shinder, 2018-06-04 Discover high-value Azure security insights, tips, and operational optimizations This book presents comprehensive Azure Security Center techniques for safeguarding cloud and hybrid environments. Leading Microsoft security and cloud experts Yuri Diogenes and Dr. Thomas Shinder show how to apply Azure Security Center’s full spectrum of features and capabilities to address protection, detection, and response in key operational scenarios. You’ll learn how to secure any Azure workload, and optimize virtually all facets of modern security, from policies and identity to incident response and risk management. Whatever your role in Azure security, you’ll learn how to save hours, days, or even weeks by solving problems in most efficient, reliable ways possible. Two of Microsoft’s leading cloud security experts show how to: • Assess the impact of cloud and hybrid environments on security, compliance, operations, data protection, and risk management • Master a new security paradigm for a world without traditional perimeters • Gain visibility and control to secure compute, network, storage, and application workloads • Incorporate Azure Security Center into your security operations center • Integrate Azure Security Center with Azure AD Identity Protection Center and third-party solutions • Adapt Azure Security Center’s built-in policies and definitions for your organization • Perform security assessments and implement Azure Security Center recommendations • Use incident response features to detect, investigate, and address threats • Create high-fidelity fusion alerts to focus attention on your most urgent security issues • Implement application whitelisting and just-in-time VM access • Monitor user behavior and access, and investigate compromised or misused credentials • Customize and perform operating system security baseline assessments • Leverage integrated threat intelligence to identify known bad actors |
| databricks technical interview questions: AWS Certified SysOps Administrator Official Study Guide Chris Fitch, Steve Friedberg, Shaun Qualheim, Jerry Rhoads, Michael Roth, Blaine Sundrud, Stephen Cole, Gareth Digby, 2017-09-20 Comprehensive, interactive exam preparation and so much more The AWS Certified SysOps Administrator Official Study Guide: Associate Exam is a comprehensive exam preparation resource. This book bridges the gap between exam preparation and real-world readiness, covering exam objectives while guiding you through hands-on exercises based on situations you'll likely encounter as an AWS Certified SysOps Administrator. From deployment, management, and operations to migration, data flow, cost control, and beyond, this guide will help you internalize the processes and best practices associated with AWS. The Sybex interactive online study environment gives you access to invaluable preparation aids, including an assessment test that helps you focus your study on areas most in need of review, and chapter tests to help you gauge your mastery of the material. Electronic flashcards make it easy to study anytime, anywhere, and a bonus practice exam gives you a sneak preview so you know what to expect on exam day. Cloud computing offers businesses a cost-effective, instantly scalable IT infrastructure. The AWS Certified SysOps Administrator - Associate credential shows that you have technical expertise in deployment, management, and operations on AWS. Study exam objectives Gain practical experience with hands-on exercises Apply your skills to real-world scenarios Test your understanding with challenging review questions Earning your AWS Certification is much more than just passing an exam—you must be able to perform the duties expected of an AWS Certified SysOps Administrator in a real-world setting. This book does more than coach you through the test: it trains you in the tools, procedures, and thought processes to get the job done well. If you're serious about validating your expertise and working at a higher level, the AWS Certified SysOps Administrator Official Study Guide: Associate Exam is the resource you've been seeking. |
| databricks technical interview questions: Optimized C++ Kurt Guntheroth, 2016-04-27 In today’s fast and competitive world, a program’s performance is just as important to customers as the features it provides. This practical guide teaches developers performance-tuning principles that enable optimization in C++. You’ll learn how to make code that already embodies best practices of C++ design run faster and consume fewer resources on any computer—whether it’s a watch, phone, workstation, supercomputer, or globe-spanning network of servers. Author Kurt Guntheroth provides several running examples that demonstrate how to apply these principles incrementally to improve existing code so it meets customer requirements for responsiveness and throughput. The advice in this book will prove itself the first time you hear a colleague exclaim, “Wow, that was fast. Who fixed something?” Locate performance hot spots using the profiler and software timers Learn to perform repeatable experiments to measure performance of code changes Optimize use of dynamically allocated variables Improve performance of hot loops and functions Speed up string handling functions Recognize efficient algorithms and optimization patterns Learn the strengths—and weaknesses—of C++ container classes View searching and sorting through an optimizer’s eye Make efficient use of C++ streaming I/O functions Use C++ thread-based concurrency features effectively |
| databricks technical interview questions: Streaming Systems Tyler Akidau, Slava Chernyak, Reuven Lax, 2018-07-16 Streaming data is a big deal in big data these days. As more and more businesses seek to tame the massive unbounded data sets that pervade our world, streaming systems have finally reached a level of maturity sufficient for mainstream adoption. With this practical guide, data engineers, data scientists, and developers will learn how to work with streaming data in a conceptual and platform-agnostic way. Expanded from Tyler Akidau’s popular blog posts Streaming 101 and Streaming 102, this book takes you from an introductory level to a nuanced understanding of the what, where, when, and how of processing real-time data streams. You’ll also dive deep into watermarks and exactly-once processing with co-authors Slava Chernyak and Reuven Lax. You’ll explore: How streaming and batch data processing patterns compare The core principles and concepts behind robust out-of-order data processing How watermarks track progress and completeness in infinite datasets How exactly-once data processing techniques ensure correctness How the concepts of streams and tables form the foundations of both batch and streaming data processing The practical motivations behind a powerful persistent state mechanism, driven by a real-world example How time-varying relations provide a link between stream processing and the world of SQL and relational algebra |
| databricks technical interview questions: 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 technical interview questions: Top 50 SQL Tricky Interview Questions Knowledge Powerhouse, 2016-12-11 This book contains tricky and nasty SQL interview questions that an interviewer asks. It is a compilation of advanced SQL interview questions after attending dozens of technical interviews in top-notch companies like- Oracle, Google, Ebay, Amazon etc.Each question is accompanied with an answer because you want to save your time while preparing for an interview.The difficulty rating on these Questions varies from a Junior level programmer to Architect level. Sample Questions are:How can we retrieve alternate records from a table in Oracle?Given a list of student names and grade. Write a query to print a comma separated list of student names in a grade.Write SQL Query to get Student Name and number of Students in same grade.Write SQL query to delete duplicate rows in a table?Write SQL query to get the second highest salary among all Employees?Write SQL Query to get Employee Name, Manager ID and number of employees in the department?Write SQL query to get the nth highest salary among all Employees.Given an Employee table with Manager_ID as column, print First name, Manager ID and Level of employees in Organization Structure?Why is the difference between NVL and NVL2 functions in SQL?What is the difference between UNION and UNION ALL?What are the reasons for de-normalizing the data?What is a Pseudocolumn?How can you find 10 employees with Odd number as Employee ID?What is the difference between DELETE and TRUNCATE in SQL?Which SQL feature can be used to view data in a table sequentially?What are the differences between CASE and DECODE in SQL?Write a SQL Query to get the Quarter from date.http://www.knowledgepowerhouse.com |
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 …
