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| data engineering open source projects: I Heart Logs Jay Kreps, 2014-09-23 Why a book about logs? That’s easy: the humble log is an abstraction that lies at the heart of many systems, from NoSQL databases to cryptocurrencies. Even though most engineers don’t think much about them, this short book shows you why logs are worthy of your attention. Based on his popular blog posts, LinkedIn principal engineer Jay Kreps shows you how logs work in distributed systems, and then delivers practical applications of these concepts in a variety of common uses—data integration, enterprise architecture, real-time stream processing, data system design, and abstract computing models. Go ahead and take the plunge with logs; you’re going love them. Learn how logs are used for programmatic access in databases and distributed systems Discover solutions to the huge data integration problem when more data of more varieties meet more systems Understand why logs are at the heart of real-time stream processing Learn the role of a log in the internals of online data systems Explore how Jay Kreps applies these ideas to his own work on data infrastructure systems at LinkedIn |
| data engineering open source projects: 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. |
| data engineering open source projects: Financial Data Engineering Tamer Khraisha, 2024-10-09 Today, investment in financial technology and digital transformation is reshaping the financial landscape and generating many opportunities. Too often, however, engineers and professionals in financial institutions lack a practical and comprehensive understanding of the concepts, problems, techniques, and technologies necessary to build a modern, reliable, and scalable financial data infrastructure. This is where financial data engineering is needed. A data engineer developing a data infrastructure for a financial product possesses not only technical data engineering skills but also a solid understanding of financial domain-specific challenges, methodologies, data ecosystems, providers, formats, technological constraints, identifiers, entities, standards, regulatory requirements, and governance. This book offers a comprehensive, practical, domain-driven approach to financial data engineering, featuring real-world use cases, industry practices, and hands-on projects. You'll learn: The data engineering landscape in the financial sector Specific problems encountered in financial data engineering The structure, players, and particularities of the financial data domain Approaches to designing financial data identification and entity systems Financial data governance frameworks, concepts, and best practices The financial data engineering lifecycle from ingestion to production The varieties and main characteristics of financial data workflows How to build financial data pipelines using open source tools and APIs Tamer Khraisha, PhD, is a senior data engineer and scientific author with more than a decade of experience in the financial sector. |
| data engineering open source projects: Data Engineering on Azure Vlad Riscutia, 2021-08-17 Build a data platform to the industry-leading standards set by Microsoft’s own infrastructure. Summary In Data Engineering on Azure you will learn how to: Pick the right Azure services for different data scenarios Manage data inventory Implement production quality data modeling, analytics, and machine learning workloads Handle data governance Using DevOps to increase reliability Ingesting, storing, and distributing data Apply best practices for compliance and access control Data Engineering on Azure reveals the data management patterns and techniques that support Microsoft’s own massive data infrastructure. Author Vlad Riscutia, a data engineer at Microsoft, teaches you to bring an engineering rigor to your data platform and ensure that your data prototypes function just as well under the pressures of production. You'll implement common data modeling patterns, stand up cloud-native data platforms on Azure, and get to grips with DevOps for both analytics and machine learning. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build secure, stable data platforms that can scale to loads of any size. When a project moves from the lab into production, you need confidence that it can stand up to real-world challenges. This book teaches you to design and implement cloud-based data infrastructure that you can easily monitor, scale, and modify. About the book In Data Engineering on Azure you’ll learn the skills you need to build and maintain big data platforms in massive enterprises. This invaluable guide includes clear, practical guidance for setting up infrastructure, orchestration, workloads, and governance. As you go, you’ll set up efficient machine learning pipelines, and then master time-saving automation and DevOps solutions. The Azure-based examples are easy to reproduce on other cloud platforms. What's inside Data inventory and data governance Assure data quality, compliance, and distribution Build automated pipelines to increase reliability Ingest, store, and distribute data Production-quality data modeling, analytics, and machine learning About the reader For data engineers familiar with cloud computing and DevOps. About the author Vlad Riscutia is a software architect at Microsoft. Table of Contents 1 Introduction PART 1 INFRASTRUCTURE 2 Storage 3 DevOps 4 Orchestration PART 2 WORKLOADS 5 Processing 6 Analytics 7 Machine learning PART 3 GOVERNANCE 8 Metadata 9 Data quality 10 Compliance 11 Distributing data |
| data engineering open source projects: 97 Things Every Data Engineer Should Know Tobias Macey, 2021-06-11 Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers. Topics include: The Importance of Data Lineage - Julien Le Dem Data Security for Data Engineers - Katharine Jarmul The Two Types of Data Engineering and Data Engineers - Jesse Anderson Six Dimensions for Picking an Analytical Data Warehouse - Gleb Mezhanskiy The End of ETL as We Know It - Paul Singman Building a Career as a Data Engineer - Vijay Kiran Modern Metadata for the Modern Data Stack - Prukalpa Sankar Your Data Tests Failed! Now What? - Sam Bail |
| data engineering open source projects: DATA ENGINEERING IN THE AGE OF AI GENERATIVE MODELS AND DEEP LEARNING UNLEASHED Siddharth Konkimalla, MANIKANTH SARISA, MOHIT SURENDER REDDY, SANJAY BAUSKAR, .The advances in data engineering technologies, including big data infrastructure, knowledge graphs, and mechanism design, will have a long-lasting impact on artificial intelligence (AI) research and development. This paper introduces data engineering in AI with a focus on the basic concepts, applications, and emerging frontiers. As a new research field, most data engineering in AI is yet to be properly defined, and there are abundant problems and applications to be explored. The primary purpose of this paper is to expose the AI community to this shining star of data science, stimulate AI researchers to think differently and form a roadmap of data engineering for AI. Since this is primarily an informal essay rather than an academic paper, its coverage is limited. The vast majority of the stimulating studies and ongoing projects are not mentioned in the paper. |
| data engineering open source projects: Fundamentals of Data Engineering Joe Reis, Matt Housley, 2022-06-22 Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle. Authors Joe Reis and Matt Housley walk you through the data engineering lifecycle and show you how to stitch together a variety of cloud technologies to serve the needs of downstream data consumers. You'll understand how to apply the concepts of data generation, ingestion, orchestration, transformation, storage, and governance that are critical in any data environment regardless of the underlying technology. This book will help you: Get a concise overview of the entire data engineering landscape Assess data engineering problems using an end-to-end framework of best practices Cut through marketing hype when choosing data technologies, architecture, and processes Use the data engineering lifecycle to design and build a robust architecture Incorporate data governance and security across the data engineering lifecycle |
| data engineering open source projects: Recent Progress in Data Engineering and Internet Technology Ford Lumban Gaol, 2012-08-13 The latest inventions in internet technology influence most of business and daily activities. Internet security, internet data management, web search, data grids, cloud computing, and web-based applications play vital roles, especially in business and industry, as more transactions go online and mobile. Issues related to ubiquitous computing are becoming critical. Internet technology and data engineering should reinforce efficiency and effectiveness of business processes. These technologies should help people make better and more accurate decisions by presenting necessary information and possible consequences for the decisions. Intelligent information systems should help us better understand and manage information with ubiquitous data repository and cloud computing. This book is a compilation of some recent research findings in Internet Technology and Data Engineering. This book provides state-of-the-art accounts in computational algorithms/tools, database management and database technologies, intelligent information systems, data engineering applications, internet security, internet data management, web search, data grids, cloud computing, web-based application, and other related topics. |
| data engineering open source projects: Data, Engineering and Applications Sanjeev Sharma, Sheng-Lung Peng, Jitendra Agrawal, Rajesh K. Shukla, Dac-Nhuong Le, 2022-10-11 The book contains select proceedings of the 3rd International Conference on Data, Engineering, and Applications (IDEA 2021). It includes papers from experts in industry and academia that address state-of-the-art research in the areas of big data, data mining, machine learning, data science, and their associated learning systems and applications. This book will be a valuable reference guide for all graduate students, researchers, and scientists interested in exploring the potential of big data applications. |
| data engineering open source projects: Data Engineering for AI/ML Pipelines Venkata Karthik Penikalapati, Mitesh Mangaonkar, 2024-10-18 DESCRIPTION Data engineering is the art of building and managing data pipelines that enable efficient data flow for AI/ML projects. This book serves as a comprehensive guide to data engineering for AI/ML systems, equipping you with the knowledge and skills to create robust and scalable data infrastructure. This book covers everything from foundational concepts to advanced techniques. It begins by introducing the role of data engineering in AI/ML, followed by exploring the lifecycle of data, from data generation and collection to storage and management. Readers will learn how to design robust data pipelines, transform data, and deploy AI/ML models effectively for real-world applications. The book also explains security, privacy, and compliance, ensuring responsible data management. Finally, it explores future trends, including automation, real-time data processing, and advanced architectures, providing a forward-looking perspective on the evolution of data engineering. By the end of this book, you will have a deep understanding of the principles and practices of data engineering for AI/ML. You will be able to design and implement efficient data pipelines, select appropriate technologies, ensure data quality and security, and leverage data for building successful AI/ML models. KEY FEATURES ● Comprehensive guide to building scalable AI/ML data engineering pipelines. ● Practical insights into data collection, storage, processing, and analysis. ● Emphasis on data security, privacy, and emerging trends in AI/ML. WHAT YOU WILL LEARN ● Architect scalable data solutions for AI/ML-driven applications. ● Design and implement efficient data pipelines for machine learning. ● Ensure data security and privacy in AI/ML systems. ● Leverage emerging technologies in data engineering for AI/ML. ● Optimize data transformation processes for enhanced model performance. WHO THIS BOOK IS FOR This book is ideal for software engineers, ML practitioners, IT professionals, and students wanting to master data pipelines for AI/ML. It is also valuable for developers and system architects aiming to expand their knowledge of data-driven technologies. TABLE OF CONTENTS 1. Introduction to Data Engineering for AI/ML 2. Lifecycle of AI/ML Data Engineering 3. Architecting Data Solutions for AI/ML 4. Technology Selection in AI/ML Data Engineering 5. Data Generation and Collection for AI/ML 6. Data Storage and Management in AI/ML 7. Data Ingestion and Preparation for ML 8. Transforming and Processing Data for AI/ML 9. Model Deployment and Data Serving 10. Security and Privacy in AI/ML Data Engineering 11. Emerging Trends and Future Direction |
| data engineering open source projects: Data Engineering with dbt Roberto Zagni, 2023-06-30 Use easy-to-apply patterns in SQL and Python to adopt modern analytics engineering to build agile platforms with dbt that are well-tested and simple to extend and run Purchase of the print or Kindle book includes a free PDF eBook Key Features Build a solid dbt base and learn data modeling and the modern data stack to become an analytics engineer Build automated and reliable pipelines to deploy, test, run, and monitor ELTs with dbt Cloud Guided dbt + Snowflake project to build a pattern-based architecture that delivers reliable datasets Book Descriptiondbt Cloud helps professional analytics engineers automate the application of powerful and proven patterns to transform data from ingestion to delivery, enabling real DataOps. This book begins by introducing you to dbt and its role in the data stack, along with how it uses simple SQL to build your data platform, helping you and your team work better together. You’ll find out how to leverage data modeling, data quality, master data management, and more to build a simple-to-understand and future-proof solution. As you advance, you’ll explore the modern data stack, understand how data-related careers are changing, and see how dbt enables this transition into the emerging role of an analytics engineer. The chapters help you build a sample project using the free version of dbt Cloud, Snowflake, and GitHub to create a professional DevOps setup with continuous integration, automated deployment, ELT run, scheduling, and monitoring, solving practical cases you encounter in your daily work. By the end of this dbt book, you’ll be able to build an end-to-end pragmatic data platform by ingesting data exported from your source systems, coding the needed transformations, including master data and the desired business rules, and building well-formed dimensional models or wide tables that’ll enable you to build reports with the BI tool of your choice.What you will learn Create a dbt Cloud account and understand the ELT workflow Combine Snowflake and dbt for building modern data engineering pipelines Use SQL to transform raw data into usable data, and test its accuracy Write dbt macros and use Jinja to apply software engineering principles Test data and transformations to ensure reliability and data quality Build a lightweight pragmatic data platform using proven patterns Write easy-to-maintain idempotent code using dbt materialization Who this book is for This book is for data engineers, analytics engineers, BI professionals, and data analysts who want to learn how to build simple, futureproof, and maintainable data platforms in an agile way. Project managers, data team managers, and decision makers looking to understand the importance of building a data platform and foster a culture of high-performing data teams will also find this book useful. Basic knowledge of SQL and data modeling will help you get the most out of the many layers of this book. The book also includes primers on many data-related subjects to help juniors get started. |
| data engineering open source projects: Google Cloud Platform for Data Engineering Alasdair Gilchrist, Google Cloud Platform for Data Engineering is designed to take the beginner through a journey to become a competent and certified GCP data engineer. The book, therefore, is split into three parts; the first part covers fundamental concepts of data engineering and data analysis from a platform and technology-neutral perspective. Reading part 1 will bring a beginner up to speed with the generic concepts, terms and technologies we use in data engineering. The second part, which is a high-level but comprehensive introduction to all the concepts, components, tools and services available to us within the Google Cloud Platform. Completing this section will provide the beginner to GCP and data engineering with a solid foundation on the architecture and capabilities of the GCP. Part 3, however, is where we delve into the moderate to advanced techniques that data engineers need to know and be able to carry out. By this time the raw beginner you started the journey at the beginning of part 1 will be a knowledgable albeit inexperienced data engineer. However, by the conclusion of part 3, they will have gained the advanced knowledge of data engineering techniques and practices on the GCP to pass not only the certification exam but also most interviews and practical tests with confidence. In short part 3, will provide the prospective data engineer with detailed knowledge on setting up and configuring DataProc - GCPs version of the Spark/Hadoop ecosystem for big data. They will also learn how to build and test streaming and batch data pipelines using pub/sub/ dataFlow and BigQuery. Furthermore, they will learn how to integrate all the ML and AI Platform components and APIs. They will be accomplished in connecting data analysis and visualisation tools such as Datalab, DataStudio and AI notebooks amongst others. They will also by now know how to build and train a TensorFlow DNN using APIs and Keras and optimise it to run large public data sets. Also, they will know how to provision and use Kubeflow and Kube Pipelines within Google Kubernetes engines to run container workloads as well as how to take advantage of serverless technologies such as Cloud Run and Cloud Functions to build transparent and seamless data processing platforms. The best part of the book though is its compartmental design which means that anyone from a beginner to an intermediate can join the book at whatever point they feel comfortable. |
| data engineering open source projects: Software and Data Engineering Wenying Feng, |
| data engineering open source projects: Model and Data Engineering Yassine Ouhammou, Mirjana Ivanovic, Alberto Abelló, Ladjel Bellatreche, 2017-09-18 This book constitutes the refereed proceedings of the 7th International Conference on Model and Data Engineering, MEDI 2017, held in Barcelona, Spain, in October 2017. The 20 full papers and 7 short papers presented together with 2 invited talks were carefully reviewed and selected from 69 submissions. The papers are organized in topical sections on domain specific languages; systems and software assessments; modeling and formal methods; data engineering; data exploration and exp loitation; modeling heterogeneity and behavior; model-based applications; and ontology-based applications. |
| data engineering open source projects: Practical Data Analysis Dhiraj Bhuyan, 2019-11-30 “Practical Data Analysis – Using Python & Open Source Technology” uses a case-study based approach to explore some of the real-world applications of open source data analysis tools and techniques. Specifically, the following topics are covered in this book: 1. Open Source Data Analysis Tools and Techniques. 2. A Beginner’s Guide to “Python” for Data Analysis. 3. Implementing Custom Search Engines On The Fly. 4. Visualising Missing Data. 5. Sentiment Analysis and Named Entity Recognition. 6. Automatic Document Classification, Clustering and Summarisation. 7. Fraud Detection Using Machine Learning Techniques. 8. Forecasting - Using Data to Map the Future. 9. Continuous Monitoring and Real-Time Analytics. 10. Creating a Robot for Interacting with Web Applications. Free samples of the book is available at - http://timesofdatascience.com |
| data engineering open source projects: Simplifying Data Engineering and Analytics with Delta Anindita Mahapatra, Doug May, 2022-07-29 Explore how Delta brings reliability, performance, and governance to your data lake and all the AI and BI use cases built on top of it Key Features • Learn Delta’s core concepts and features as well as what makes it a perfect match for data engineering and analysis • Solve business challenges of different industry verticals using a scenario-based approach • Make optimal choices by understanding the various tradeoffs provided by Delta Book Description Delta helps you generate reliable insights at scale and simplifies architecture around data pipelines, allowing you to focus primarily on refining the use cases being worked on. This is especially important when you consider that existing architecture is frequently reused for new use cases. In this book, you'll learn about the principles of distributed computing, data modeling techniques, and big data design patterns and templates that help solve end-to-end data flow problems for common scenarios and are reusable across use cases and industry verticals. You'll also learn how to recover from errors and the best practices around handling structured, semi-structured, and unstructured data using Delta. After that, you'll get to grips with features such as ACID transactions on big data, disciplined schema evolution, time travel to help rewind a dataset to a different time or version, and unified batch and streaming capabilities that will help you build agile and robust data products. By the end of this Delta book, you'll be able to use Delta as the foundational block for creating analytics-ready data that fuels all AI/BI use cases. What you will learn • Explore the key challenges of traditional data lakes • Appreciate the unique features of Delta that come out of the box • Address reliability, performance, and governance concerns using Delta • Analyze the open data format for an extensible and pluggable architecture • Handle multiple use cases to support BI, AI, streaming, and data discovery • Discover how common data and machine learning design patterns are executed on Delta • Build and deploy data and machine learning pipelines at scale using Delta Who this book is for Data engineers, data scientists, ML practitioners, BI analysts, or anyone in the data domain working with big data will be able to put their knowledge to work with this practical guide to executing pipelines and supporting diverse use cases using the Delta protocol. Basic knowledge of SQL, Python programming, and Spark is required to get the most out of this book. |
| data engineering open source projects: Data Engineering with Apache Spark, Delta Lake, and Lakehouse Manoj Kukreja, Danil Zburivsky, 2021-10-22 Understand the complexities of modern-day data engineering platforms and explore strategies to deal with them with the help of use case scenarios led by an industry expert in big data Key FeaturesBecome well-versed with the core concepts of Apache Spark and Delta Lake for building data platformsLearn how to ingest, process, and analyze data that can be later used for training machine learning modelsUnderstand how to operationalize data models in production using curated dataBook Description In the world of ever-changing data and schemas, it is important to build data pipelines that can auto-adjust to changes. This book will help you build scalable data platforms that managers, data scientists, and data analysts can rely on. Starting with an introduction to data engineering, along with its key concepts and architectures, this book will show you how to use Microsoft Azure Cloud services effectively for data engineering. You'll cover data lake design patterns and the different stages through which the data needs to flow in a typical data lake. Once you've explored the main features of Delta Lake to build data lakes with fast performance and governance in mind, you'll advance to implementing the lambda architecture using Delta Lake. Packed with practical examples and code snippets, this book takes you through real-world examples based on production scenarios faced by the author in his 10 years of experience working with big data. Finally, you'll cover data lake deployment strategies that play an important role in provisioning the cloud resources and deploying the data pipelines in a repeatable and continuous way. By the end of this data engineering book, you'll know how to effectively deal with ever-changing data and create scalable data pipelines to streamline data science, ML, and artificial intelligence (AI) tasks. What you will learnDiscover the challenges you may face in the data engineering worldAdd ACID transactions to Apache Spark using Delta LakeUnderstand effective design strategies to build enterprise-grade data lakesExplore architectural and design patterns for building efficient data ingestion pipelinesOrchestrate a data pipeline for preprocessing data using Apache Spark and Delta Lake APIsAutomate deployment and monitoring of data pipelines in productionGet to grips with securing, monitoring, and managing data pipelines models efficientlyWho this book is for This book is for aspiring data engineers and data analysts who are new to the world of data engineering and are looking for a practical guide to building scalable data platforms. If you already work with PySpark and want to use Delta Lake for data engineering, you'll find this book useful. Basic knowledge of Python, Spark, and SQL is expected. |
| data engineering open source projects: Intelligent Data Engineering and Automated Learning – IDEAL 2020 Cesar Analide, Paulo Novais, David Camacho, Hujun Yin, 2020-10-29 This two-volume set of LNCS 12489 and 12490 constitutes the thoroughly refereed conference proceedings of the 21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020, held in Guimaraes, Portugal, in November 2020.* The 93 papers presented were carefully reviewed and selected from 134 submissions. These papers provided a timely sample of the latest advances in data engineering and machine learning, from methodologies, frameworks, and algorithms to applications. The core themes of IDEAL 2020 include big data challenges, machine learning, data mining, information retrieval and management, bio-/neuro-informatics, bio-inspiredmodels, agents and hybrid intelligent systems, real-world applications of intelligent techniques and AI. * The conference was held virtually due to the COVID-19 pandemic. |
| data engineering open source projects: Data Engineering with AWS Gareth Eagar, 2021-12-29 The missing expert-led manual for the AWS ecosystem — go from foundations to building data engineering pipelines effortlessly Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Learn about common data architectures and modern approaches to generating value from big data Explore AWS tools for ingesting, transforming, and consuming data, and for orchestrating pipelines Learn how to architect and implement data lakes and data lakehouses for big data analytics from a data lakes expert Book DescriptionWritten by a Senior Data Architect with over twenty-five years of experience in the business, Data Engineering for AWS is a book whose sole aim is to make you proficient in using the AWS ecosystem. Using a thorough and hands-on approach to data, this book will give aspiring and new data engineers a solid theoretical and practical foundation to succeed with AWS. As you progress, you’ll be taken through the services and the skills you need to architect and implement data pipelines on AWS. You'll begin by reviewing important data engineering concepts and some of the core AWS services that form a part of the data engineer's toolkit. You'll then architect a data pipeline, review raw data sources, transform the data, and learn how the transformed data is used by various data consumers. You’ll also learn about populating data marts and data warehouses along with how a data lakehouse fits into the picture. Later, you'll be introduced to AWS tools for analyzing data, including those for ad-hoc SQL queries and creating visualizations. In the final chapters, you'll understand how the power of machine learning and artificial intelligence can be used to draw new insights from data. By the end of this AWS book, you'll be able to carry out data engineering tasks and implement a data pipeline on AWS independently.What you will learn Understand data engineering concepts and emerging technologies Ingest streaming data with Amazon Kinesis Data Firehose Optimize, denormalize, and join datasets with AWS Glue Studio Use Amazon S3 events to trigger a Lambda process to transform a file Run complex SQL queries on data lake data using Amazon Athena Load data into a Redshift data warehouse and run queries Create a visualization of your data using Amazon QuickSight Extract sentiment data from a dataset using Amazon Comprehend Who this book is for This book is for data engineers, data analysts, and data architects who are new to AWS and looking to extend their skills to the AWS cloud. Anyone new to data engineering who wants to learn about the foundational concepts while gaining practical experience with common data engineering services on AWS will also find this book useful. A basic understanding of big data-related topics and Python coding will help you get the most out of this book but it’s not a prerequisite. Familiarity with the AWS console and core services will also help you follow along. |
| data engineering open source projects: Driving Transformative Technology Trends With Cloud Computing Gochhait, Saikat, 2024-08-08 Modern businesses face a pressing challenge in navigating the complex landscape of cloud computing, 5G, and artificial intelligence (AI) integration. Despite these technologies' transformative potential, businesses often need help with bandwidth, latency, connection density, and cost constraints. This hinders the widespread adoption of cloud computing, limiting its impact on various industries. Driving Transformative Technology Trends With Cloud Computing offers a comprehensive solution for academic scholars seeking to understand and leverage the potential of cloud computing, 5G, and AI integration. By exploring topics such as green cloud computing, edge computing, cloud cryptography, and more, scholars can gain valuable insights into the synergistic effects of these technologies. This book provides a roadmap for leveraging the power of cloud computing and its integration with 5G and AI to drive innovation and growth in academia and beyond. By offering practical insights and strategies, this book equips scholars with the knowledge and tools needed to navigate the complexities of modern technology integration and drive meaningful change in their respective fields. |
| data engineering open source projects: 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 |
| data engineering open source projects: Data Engineering with Google Cloud Platform Adi Wijaya, 2024-04-30 Become a successful data engineer by building and deploying your own data pipelines on Google Cloud, including making key architectural decisions Key Features Get up to speed with data governance on Google Cloud Learn how to use various Google Cloud products like Dataform, DLP, Dataplex, Dataproc Serverless, and Datastream Boost your confidence by getting Google Cloud data engineering certification guidance from real exam experiences Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionThe second edition of Data Engineering with Google Cloud builds upon the success of the first edition by offering enhanced clarity and depth to data professionals navigating the intricate landscape of data engineering. Beyond its foundational lessons, this new edition delves into the essential realm of data governance within Google Cloud, providing you with invaluable insights into managing and optimizing data resources effectively. Written by a Data Strategic Cloud Engineer at Google, this book helps you stay ahead of the curve by guiding you through the latest technological advancements in the Google Cloud ecosystem. You’ll cover essential aspects, from exploring Cloud Composer 2 to the evolution of Airflow 2.5. Additionally, you’ll explore how to work with cutting-edge tools like Dataform, DLP, Dataplex, Dataproc Serverless, and Datastream to perform data governance on datasets. By the end of this book, you'll be equipped to navigate the ever-evolving world of data engineering on Google Cloud, from foundational principles to cutting-edge practices.What you will learn Load data into BigQuery and materialize its output Focus on data pipeline orchestration using Cloud Composer Formulate Airflow jobs to orchestrate and automate a data warehouse Establish a Hadoop data lake, generate ephemeral clusters, and execute jobs on the Dataproc cluster Harness Pub/Sub for messaging and ingestion for event-driven systems Apply Dataflow to conduct ETL on streaming data Implement data governance services on Google Cloud Who this book is for Data analysts, IT practitioners, software engineers, or any data enthusiasts looking to have a successful data engineering career will find this book invaluable. Additionally, experienced data professionals who want to start using Google Cloud to build data platforms will get clear insights on how to navigate the path. Whether you're a beginner who wants to explore the fundamentals or a seasoned professional seeking to learn the latest data engineering concepts, this book is for you. |
| data engineering open source projects: Handbook of Research on Open Source Software: Technological, Economic, and Social Perspectives St.Amant, Kirk, Still, Brian, 2007-04-30 This handbook of research is one of the few texts to combine Open Source Software (OSS) in public and private sector activities into a single reference source. It examines how the use of OSS affects practices in society, business, government, education, and law. |
| data engineering open source projects: Trends in Data Engineering Methods for Intelligent Systems Jude Hemanth, Tuncay Yigit, Bogdan Patrut, Anastassia Angelopoulou, 2021-07-05 This book briefly covers internationally contributed chapters with artificial intelligence and applied mathematics-oriented background-details. Nowadays, the world is under attack of intelligent systems covering all fields to make them practical and meaningful for humans. In this sense, this edited book provides the most recent research on use of engineering capabilities for developing intelligent systems. The chapters are a collection from the works presented at the 2nd International Conference on Artificial Intelligence and Applied Mathematics in Engineering held within 09-10-11 October 2020 at the Antalya, Manavgat (Turkey). The target audience of the book covers scientists, experts, M.Sc. and Ph.D. students, post-docs, and anyone interested in intelligent systems and their usage in different problem domains. The book is suitable to be used as a reference work in the courses associated with artificial intelligence and applied mathematics. |
| data engineering open source projects: AWS Certified Data Analytics Study Guide with Online Labs Asif Abbasi, 2021-04-13 Virtual, hands-on learning labs allow you to apply your technical skills in realistic environments. So Sybex has bundled AWS labs from XtremeLabs with our popular AWS Certified Data Analytics Study Guide to give you the same experience working in these labs as you prepare for the Certified Data Analytics Exam that you would face in a real-life application. These labs in addition to the book are a proven way to prepare for the certification and for work as an AWS Data Analyst. AWS Certified Data Analytics Study Guide: Specialty (DAS-C01) Exam is intended for individuals who perform in a data analytics-focused role. This UPDATED exam validates an examinee's comprehensive understanding of using AWS services to design, build, secure, and maintain analytics solutions that provide insight from data. It assesses an examinee's ability to define AWS data analytics services and understand how they integrate with each other; and explain how AWS data analytics services fit in the data lifecycle of collection, storage, processing, and visualization. The book focuses on the following domains: • Collection • Storage and Data Management • Processing • Analysis and Visualization • Data Security This is your opportunity to take the next step in your career by expanding and validating your skills on the AWS cloud. AWS is the frontrunner in cloud computing products and services, and the AWS Certified Data Analytics Study Guide: Specialty exam will get you fully prepared through expert content, and real-world knowledge, key exam essentials, chapter review questions, and much more. Written by an AWS subject-matter expert, this study guide covers exam concepts, and provides key review on exam topics. Readers will also have access to Sybex's superior online interactive learning environment and test bank, including chapter tests, practice exams, a glossary of key terms, and electronic flashcards. And included with this version of the book, XtremeLabs virtual labs that run from your browser. The registration code is included with the book and gives you 6 months of unlimited access to XtremeLabs AWS Certified Data Analytics Labs with 3 unique lab modules based on the book. |
| data engineering open source projects: Learn SQL with MySQL Ashwin Pajankar, 2020-07-18 A step-by-step guide that will help you manage data in a relational database using SQL with ease Ê DESCRIPTIONÊ Ê This book starts with the concepts in RDBMS (Relational Database Management Systems) and SQL (Structured Query Language). The first few chapters cover the definitions and a brief explanation of all the important concepts. They also cover the installation of MariaDB and MySQL on Windows and Raspberry Pi, as well as the setup of various tools used to connect to MySQL and MariaDB server processes. We will also understand how to install sample schemas and how to use basic SQL queries. Then we move on to the SELECT query in detail. The book explores the data retrieval aspect of SQL queries in detail with the WHERE clause and NULL handling in detail. The book also explores the functions available in MySQL. Those are single row and group functions. Then we explore how to combine the data from multiple sources. The technique is known as Joins, and we will learn ANSI style and the old-style syntax for all the types of Joins.Ê The last part explores the DDL and DMLs in depth. We also learn the concepts of Transactions and Constraints. The book explores how we can run the SQL queries from a Python 3 program and load a pandas DataFrame with the data from a table in a schema in the MySQL database. Ê KEY FEATURESÊ _Ê Understand the concepts related to relational databases. _Ê Learn how to install MariaDB and MySQL on Windows, Linux and tools to access it. _ ÊLearn how to connect Python and Pandas to MySQL/MariaDB. WHAT WILL YOU LEARNÊ _Ê Understand the basics of MySQL and MariaDB. _ Get familiar with MySQL Arithmetic Operators, DDL, DML, DCL & TCL commands. _Ê Understand the concept of Single-Row Functions and Group Functions in detail. _Ê Retrieve data from multiple sources using the Joins. WHO THIS BOOK IS FOR This book is designed for beginners as well as professionals alike. The book will also be useful to Data Scientists, Data Analysts, Database Administrators, and Data Engineers. Table of Contents 1. Introduction and Installation 2. Getting Started with MySQL 3. Getting Started with SQL Queries 4. The WHERE clause in detail 5. Single Row Functions 6. Group Functions 7. Joins in MySQL 8.Ê Subqueries 9. DDL, DML, and Transactions 10. Views 11. Python 3, MySQL, and Pandas |
| data engineering open source projects: Google Cloud Professional Data Engineer , 2024-10-26 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 |
| data engineering open source projects: Data Pipelines with Apache Airflow Bas P. Harenslak, Julian de Ruiter, 2021-04-27 This book teaches you how to build and maintain effective data pipelines. Youll explore the most common usage patterns, including aggregating multiple data sources, connecting to and from data lakes, and cloud deployment. -- |
| data engineering open source projects: Fundamental Concepts of MATLAB Programming Dr. Brijesh Parmar Bakariya, Dr. Kulwinder Singh, 2020-09-03 Learn how to use MATLAB commands and functions in an efficient and effective manner Key Features a- Get familiar and work with the in-built functions in MATLAB a- Learn how to solve algebraic equations in MATLAB a- Explore various techniques for plotting numerical data a- Learn how to preprocess data to ensure accurate, efficient, and meaningful analysis a- Learn how to issue commands to create variables and call functions Description MATLAB has been an essential platform for data computation. There are various types of technologies that are going on, but it requires a tool for data handling. MATLAB provides better computing power for a massive amount of data. This book will be your comprehensive guide to creating applications, simulation, computation measures. The book begins with an introduction MATLAB and quickly goes on to teach you the usage of MATLAB. After this, we will explore the various commands and essential concepts and topics about MATLAB. Moving forward, we'll explore importing and exporting data, handling data, and visualization of data through different ways to plot a graph. Towards the end, we will explore the basic algebraic functions used in MATLAB. What will you learn a- Learn how to build and run MATLAB statements a- Execute a block of code repeatedly using the Loop Control Statements a- Create a user-defined function by using MATLAB a- Create, Concatenate, and Expand the most basic MATLAB data structure; Matrix a- Understand how to plot a 2D and 3D graph Who this book is for This book is for everyone from the Engineering and Sciences background. It is also for PGDCA, B.Tech. B.E., BCA, BSc, M.Tech. /M.E., MCA, M.Com., MSc, Ph.D. other UG, and PG degree students. Table of Contents 1. Basics of MATLAB 2. Expressions and Basic Commands of MATLAB 3. Data Types, Variables and Operators 4. Decision Control Statements 5. Loops Control Statements 6. Vectors 7. Matrix 8. Arrays 9. Strings 10. Functions 11. Data Import and Export 12. Plotting a Graph 13. Graphics 14. Basic Algebra in MATLAB About the Authors Dr. Brijesh Bakariya is an Assistant Professor in the Department of CSE, IKGPTU, Jalandhar (Punjab). He has authored 01 book and published more than 15 research papers in the journals of international repute. Dr. Kulwinder Singh Parmar is an Assistant Professor in the Department of Mathematical Sciences, IKGPTU, Jalandhar (Punjab). He has published more than 25 research papers in the journals of international repute. |
| data engineering open source projects: Intelligence Science and Big Data Engineering Yuxin Peng, Kai Yu, Jiwen Lu, Xingpeng Jiang, 2018-11-08 This book constitutes the proceedings of the 8th International Conference on Intelligence Science and Big DataEngineering, IScIDE 2018, held in Lanzhou, China, in August 2018.The 59 full papers presented in this book were carefully reviewed and selected from 121 submissions.They are grouped in topical sections on robots and intelligent systems; statistics and learning; deep learning; objects and language; classification and clustering; imaging; and biomedical signal processing. |
| data engineering open source projects: AI-DRIVEN DATA ENGINEERING TRANSFORMING BIG DATA INTO ACTIONABLE INSIGHT Eswar Prasad Galla, Chandrababu Kuraku, Hemanth Kumar Gollangi, Janardhana Rao Sunkara, Chandrakanth Rao Madhavaram, ..... |
| data engineering open source projects: Beautiful Code Greg Wilson, Andy Oram, 2007-06-26 How do the experts solve difficult problems in software development? In this unique and insightful book, leading computer scientists offer case studies that reveal how they found unusual, carefully designed solutions to high-profile projects. You will be able to look over the shoulder of major coding and design experts to see problems through their eyes. This is not simply another design patterns book, or another software engineering treatise on the right and wrong way to do things. The authors think aloud as they work through their project's architecture, the tradeoffs made in its construction, and when it was important to break rules. This book contains 33 chapters contributed by Brian Kernighan, KarlFogel, Jon Bentley, Tim Bray, Elliotte Rusty Harold, Michael Feathers,Alberto Savoia, Charles Petzold, Douglas Crockford, Henry S. Warren,Jr., Ashish Gulhati, Lincoln Stein, Jim Kent, Jack Dongarra and PiotrLuszczek, Adam Kolawa, Greg Kroah-Hartman, Diomidis Spinellis, AndrewKuchling, Travis E. Oliphant, Ronald Mak, Rogerio Atem de Carvalho andRafael Monnerat, Bryan Cantrill, Jeff Dean and Sanjay Ghemawat, SimonPeyton Jones, Kent Dybvig, William Otte and Douglas C. Schmidt, AndrewPatzer, Andreas Zeller, Yukihiro Matsumoto, Arun Mehta, TV Raman,Laura Wingerd and Christopher Seiwald, and Brian Hayes. Beautiful Code is an opportunity for master coders to tell their story. All author royalties will be donated to Amnesty International. |
| data engineering open source projects: Intelligent Data Engineering and Automated Learning - IDEAL 2009 Emilio Corchado, Hujun Yin, 2009-09-07 This book constitutes the refereed proceedings of the 10th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2009, held in Burgos, Sapin, in September 2009. The 100 revised full papers presented were carefully reviewed and selected from over 200 submissions for inclusion in the book. The papers are organized in topical sections on learning and information processing; data mining and information management; neuro-informatics, bio-informatics, and bio-inspired models; agents and hybrid systems; soft computing techniques in data mining; recent advances on swarm-based computing; intelligent computational techniques in medical image processing; advances on ensemble learning and information fursion; financial and business engineering (modeling and applications); MIR day 2009 - Burgos; and nature inspired models for industrial applications. |
| data engineering open source projects: Getting Started with DuckDB Simon Aubury, Ned Letcher, 2024-06-24 Analyze and transform data efficiently with DuckDB, a versatile, modern, in-process SQL database Key Features Use DuckDB to rapidly load, transform, and query data across a range of sources and formats Gain practical experience using SQL, Python, and R to effectively analyze data Learn how open source tools and cloud services in the broader data ecosystem complement DuckDB’s versatile capabilities Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDuckDB is a fast in-process analytical database. Getting Started with DuckDB offers a practical overview of its usage. You'll learn to load, transform, and query various data formats, including CSV, JSON, and Parquet. The book covers DuckDB's optimizations, SQL enhancements, and extensions for specialized applications. Working with examples in SQL, Python, and R, you'll explore analyzing public datasets and discover tools enhancing DuckDB workflows. This guide suits both experienced and new data practitioners, quickly equipping you to apply DuckDB's capabilities in analytical projects. You'll gain proficiency in using DuckDB for diverse tasks, enabling effective integration into your data workflows.What you will learn Understand the properties and applications of a columnar in-process database Use SQL to load, transform, and query a range of data formats Discover DuckDB's rich extensions and learn how to apply them Use nested data types to model semi-structured data and extract and model JSON data Integrate DuckDB into your Python and R analytical workflows Effectively leverage DuckDB's convenient SQL enhancements Explore the wider ecosystem and pathways for building DuckDB-powered data applications Who this book is for If you’re interested in expanding your analytical toolkit, this book is for you. It will be particularly valuable for data analysts wanting to rapidly explore and query complex data, data and software engineers looking for a lean and versatile data processing tool, along with data scientists needing a scalable data manipulation library that integrates seamlessly with Python and R. You will get the most from this book if you have some familiarity with SQL and foundational database concepts, as well as exposure to a programming language such as Python or R. |
| data engineering open source projects: Intelligent Data Engineering and Analytics Vikrant Bhateja, Xin-She Yang, Jerry Chun-Wei Lin, Ranjita Das, 2023-02-23 The book presents the proceedings of the 10th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA 2022), held at NIT Mizoram, Aizawl, Mizoram, India during 18 – 19 June 2022. Researchers, scientists, engineers, and practitioners exchange new ideas and experiences in the domain of intelligent computing theories with prospective applications in various engineering disciplines in the book. These proceedings are divided into two volumes. It covers broad areas of information and decision sciences, with papers exploring both the theoretical and practical aspects of data-intensive computing, data mining, evolutionary computation, knowledge management and networks, sensor networks, signal processing, wireless networks, protocols and architectures. This volume is a valuable resource for postgraduate students in various engineering disciplines. |
| data engineering open source projects: The Rise of Open-Source Software IntroBooks Team, Open source software finds its connections with free software and is part of free and open source software over the extended term. Open-source software (OSS) is a form of computer software program that releases source code under a licensing legitimacy wherein the copyright holder allows users the privilege to analyze, modify and share the software to anyone and for any productive purpose. Open-source software may be created either freely and collaboratively. Open-source software is a case in point of open collaboration. The progress of open-source software will offer assorted perspectives beyond those of a single enterprise A 2008 study from the Standish Group reported that the implementation of open-source software models culminated in savings for customers of around $60 billion (£ 48 billion) annually. Initiatives of open source software are undertaken and managed by a network of voluntary programming associations and are commonly used in the gamut of both free and commercial outputs. |
| data engineering open source projects: 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 |
| data engineering open source projects: Deep Learning with MXNet Cookbook Andrés P. Torres, 2023-12-29 Gain practical, recipe-based insights into the world of deep learning using Apache MXNet for flexible and efficient research prototyping, training, and deployment to production Key Features Create scalable deep learning applications using MXNet products with step-by-step tutorials Implement tasks such as transfer learning, transformers, and more with the required speed and scalability Analyze model performance and fine-tune for accuracy, scalability, and speed Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionExplore the capabilities of the open-source deep learning framework MXNet to train and deploy neural network models and implement state-of-the-art (SOTA) architectures in Computer Vision, natural language processing, and more. The Deep Learning with MXNet Cookbook is your gateway to constructing fast and scalable deep learning solutions using Apache MXNet. Starting with the different versions of MXNet, this book helps you choose the optimal version for your use and install your library. You’ll work with MXNet/Gluon libraries to solve classification and regression problems and gain insights into their inner workings. Venturing further, you’ll use MXNet to analyze toy datasets in the areas of numerical regression, data classification, picture classification, and text classification. From building and training deep-learning neural network architectures from scratch to delving into advanced concepts such as transfer learning, this book covers it all. You'll master the construction and deployment of neural network architectures, including CNN, RNN, LSTMs, and Transformers, and integrate these models into your applications. By the end of this deep learning book, you’ll wield the MXNet and Gluon libraries to expertly create and train deep learning networks using GPUs and deploy them in different environments.What you will learn Grasp the advantages of MXNet and Gluon libraries Build and train network models from scratch using MXNet Apply transfer learning for more complex, fine-tuned network architectures Address modern Computer Vision and NLP problems using neural network techniques Train state-of-the-art models with GPUs and leverage modern optimization techniques Improve inference run-times and deploy models in production Who this book is for This book is for data scientists, machine learning engineers, and developers who want to work with Apache MXNet for building fast and scalable deep learning solutions. Python programming knowledge and access to a working coding environment with Python 3.6+ is necessary to get started. Although not a prerequisite, a solid theoretical understanding of mathematics for deep learning will be beneficial. |
| data engineering open source projects: 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. |
| data engineering open source projects: Open Source Development, Adoption and Innovation Joseph Feller, Brian Fitzgerald, Walt Scacchi, Alberto Sillitti, 2007-08-10 Open source software has emerged as a major field of scientific inquiry across a number of disciplines. When the concept of open source began to gain mindshare in the global business community, decision makers faced a challenge: to convert hype and potential into sustainable profit and viable business models. This volume addresses this challenge through presenting some of the newest, extensively peer-reviewed research in the area. |
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with minimum time …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, released in …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process from …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical barriers …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be collected, …
Data and Digital Outputs Management Plan (DDOMP)
Data and Digital Outputs Management Plan (DDOMP)
Building New Tools for Data Sharing and Reuse through a …
Jan 10, 2019 · The SEI CRA will closely link research thinking and technological innovation toward accelerating the full path of discovery-driven data use and open science. This will enable a …
Open Data Policy and Principles - Belmont Forum
The data policy includes the following principles: Data should be: Discoverable through catalogues and search engines; Accessible as open data by default, and made available with …
Belmont Forum Adopts Open Data Principles for Environmental …
Jan 27, 2016 · Adoption of the open data policy and principles is one of five recommendations in A Place to Stand: e-Infrastructures and Data Management for Global Change Research, …
Belmont Forum Data Accessibility Statement and Policy
The DAS encourages researchers to plan for the longevity, reusability, and stability of the data attached to their research publications and results. Access to data promotes reproducibility, …
Climate-Induced Migration in Africa and Beyond: Big Data and …
CLIMB will also leverage earth observation and social media data, and combine them with survey and official statistical data. This holistic approach will allow us to analyze migration process …
Advancing Resilience in Low Income Housing Using Climate …
Jun 4, 2020 · Environmental sustainability and public health considerations will be included. Machine Learning and Big Data Analytics will be used to identify optimal disaster resilient …
Belmont Forum
What is the Belmont Forum? The Belmont Forum is an international partnership that mobilizes funding of environmental change research and accelerates its delivery to remove critical …
Waterproofing Data: Engaging Stakeholders in Sustainable Flood …
Apr 26, 2018 · Waterproofing Data investigates the governance of water-related risks, with a focus on social and cultural aspects of data practices. Typically, data flows up from local levels to …
Data Management Annex (Version 1.4) - Belmont Forum
A full Data Management Plan (DMP) for an awarded Belmont Forum CRA project is a living, actively updated document that describes the data management life cycle for the data to be …
