Simplifying Data Engineering And Analytics With Delta
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Simplifying Data Engineering And Analytics With Delta
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Author : Anindita Mahapatra
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-07-29
Simplifying Data Engineering And Analytics With Delta written by Anindita Mahapatra and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-29 with Computers categories.
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.
Simplifying Data Engineering And Analytics With Delta
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Author : ANINDITA. MAHAPATRA
language : en
Publisher:
Release Date : 2022-07-29
Simplifying Data Engineering And Analytics With Delta written by ANINDITA. MAHAPATRA and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-07-29 with categories.
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.
Azure Data Engineering Cookbook
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Author : Nagaraj Venkatesan
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-09-26
Azure Data Engineering Cookbook written by Nagaraj Venkatesan and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-26 with Computers categories.
Nearly 80 recipes to help you collect and transform data from multiple sources into a single data source, making it way easier to perform analytics on the data Key FeaturesBuild data pipelines from scratch and find solutions to common data engineering problemsLearn how to work with Azure Data Factory, Data Lake, Databricks, and Synapse AnalyticsMonitor and maintain your data engineering pipelines using Log Analytics, Azure Monitor, and Azure PurviewBook Description The famous quote 'Data is the new oil' seems more true every day as the key to most organizations' long-term success lies in extracting insights from raw data. One of the major challenges organizations face in leveraging value out of data is building performant data engineering pipelines for data visualization, ingestion, storage, and processing. This second edition of the immensely successful book by Ahmad Osama brings to you several recent enhancements in Azure data engineering and shares approximately 80 useful recipes covering common scenarios in building data engineering pipelines in Microsoft Azure. You'll explore recipes from Azure Synapse Analytics workspaces Gen 2 and get to grips with Synapse Spark pools, SQL Serverless pools, Synapse integration pipelines, and Synapse data flows. You'll also understand Synapse SQL Pool optimization techniques in this second edition. Besides Synapse enhancements, you'll discover helpful tips on managing Azure SQL Database and learn about security, high availability, and performance monitoring. Finally, the book takes you through overall data engineering pipeline management, focusing on monitoring using Log Analytics and tracking data lineage using Azure Purview. By the end of this book, you'll be able to build superior data engineering pipelines along with having an invaluable go-to guide. What you will learnProcess data using Azure Databricks and Azure Synapse AnalyticsPerform data transformation using Azure Synapse data flowsPerform common administrative tasks in Azure SQL DatabaseBuild effective Synapse SQL pools which can be consumed by Power BIMonitor Synapse SQL and Spark pools using Log AnalyticsTrack data lineage using Microsoft Purview integration with pipelinesWho this book is for This book is for data engineers, data architects, database administrators, and data professionals who want to get well versed with the Azure data services for building data pipelines. Basic understanding of cloud and data engineering concepts will help in getting the most out of this book.
Mastering Data Engineering And Analytics With Databricks
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Author : Manoj Kumar
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2024-09-30
Mastering Data Engineering And Analytics With Databricks written by Manoj Kumar and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-09-30 with Computers categories.
TAGLINE Master Databricks to Transform Data into Strategic Insights for Tomorrow’s Business Challenges KEY FEATURES ● Combines theory with practical steps to master Databricks, Delta Lake, and MLflow. ● Real-world examples from FMCG and CPG sectors demonstrate Databricks in action. ● Covers real-time data processing, ML integration, and CI/CD for scalable pipelines. ● Offers proven strategies to optimize workflows and avoid common pitfalls. DESCRIPTION In today’s data-driven world, mastering data engineering is crucial for driving innovation and delivering real business impact. Databricks is one of the most powerful platforms which unifies data, analytics and AI requirements of numerous organizations worldwide. Mastering Data Engineering and Analytics with Databricks goes beyond the basics, offering a hands-on, practical approach tailored for professionals eager to excel in the evolving landscape of data engineering and analytics. This book uniquely blends foundational knowledge with advanced applications, equipping readers with the expertise to build, optimize, and scale data pipelines that meet real-world business needs. With a focus on actionable learning, it delves into complex workflows, including real-time data processing, advanced optimization with Delta Lake, and seamless ML integration with MLflow—skills critical for today’s data professionals. Drawing from real-world case studies in FMCG and CPG industries, this book not only teaches you how to implement Databricks solutions but also provides strategic insights into tackling industry-specific challenges. From setting up your environment to deploying CI/CD pipelines, you'll gain a competitive edge by mastering techniques that are directly applicable to your organization’s data strategy. By the end, you’ll not just understand Databricks—you’ll command it, positioning yourself as a leader in the data engineering space. WHAT WILL YOU LEARN ● Design and implement scalable, high-performance data pipelines using Databricks for various business use cases. ● Optimize query performance and efficiently manage cloud resources for cost-effective data processing. ● Seamlessly integrate machine learning models into your data engineering workflows for smarter automation. ● Build and deploy real-time data processing solutions for timely and actionable insights. ● Develop reliable and fault-tolerant Delta Lake architectures to support efficient data lakes at scale. WHO IS THIS BOOK FOR? This book is designed for data engineering students, aspiring data engineers, experienced data professionals, cloud data architects, data scientists and analysts looking to expand their skill sets, as well as IT managers seeking to master data engineering and analytics with Databricks. A basic understanding of data engineering concepts, familiarity with data analytics, and some experience with cloud computing or programming languages such as Python or SQL will help readers fully benefit from the book’s content. TABLE OF CONTENTS SECTION 1 1. Introducing Data Engineering with Databricks 2. Setting Up a Databricks Environment for Data Engineering 3. Working with Databricks Utilities and Clusters SECTION 2 4. Extracting and Loading Data Using Databricks 5. Transforming Data with Databricks 6. Handling Streaming Data with Databricks 7. Creating Delta Live Tables 8. Data Partitioning and Shuffling 9. Performance Tuning and Best Practices 10. Workflow Management 11. Databricks SQL Warehouse 12. Data Storage and Unity Catalog 13. Monitoring Databricks Clusters and Jobs 14. Production Deployment Strategies 15. Maintaining Data Pipelines in Production 16. Managing Data Security and Governance 17. Real-World Data Engineering Use Cases with Databricks 18. AI and ML Essentials 19. Integrating Databricks with External Tools Index
The Microsoft Fabric Handbook
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Author : Robert Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-01-06
The Microsoft Fabric Handbook written by Robert Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-06 with Computers categories.
"The Microsoft Fabric Handbook: Simplifying Data Engineering and Analytics" is an essential guide designed for professionals and beginners seeking to navigate the dynamic world of data management and analysis with Microsoft Fabric. This comprehensive resource offers clear, structured insights into each component of the platform, from setting up a robust environment to integrating complex data sources and transforming raw data into valuable insights. With a focus on practical application, readers learn how to effectively harness Microsoft Fabric's capabilities to address real-world challenges in data engineering. The book not only delves into the technical aspects of Microsoft Fabric but also explores its strategic advantages within the broader Microsoft ecosystem. Through detailed case studies and illustrative examples, readers gain a deeper understanding of how to deploy data solutions that drive innovation and efficiency across various industries. Emphasizing best practices in security, compliance, and troubleshooting, this handbook serves as a critical resource for those aiming to optimize data pipelines and achieve excellence in data-driven decision-making. Whether you're embarking on your first project or enhancing existing skills, this book provides the knowledge foundation needed to excel in today's data-centric landscape.
Data Engineering With Databricks Cookbook
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Author : Pulkit Chadha
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-05-31
Data Engineering With Databricks Cookbook written by Pulkit Chadha and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-31 with Computers categories.
Work through 70 recipes for implementing reliable data pipelines with Apache Spark, optimally store and process structured and unstructured data in Delta Lake, and use Databricks to orchestrate and govern your data Key Features Learn data ingestion, data transformation, and data management techniques using Apache Spark and Delta Lake Gain practical guidance on using Delta Lake tables and orchestrating data pipelines Implement reliable DataOps and DevOps practices, and enforce data governance policies on Databricks Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWritten by a Senior Solutions Architect at Databricks, Data Engineering with Databricks Cookbook will show you how to effectively use Apache Spark, Delta Lake, and Databricks for data engineering, starting with comprehensive introduction to data ingestion and loading with Apache Spark. What makes this book unique is its recipe-based approach, which will help you put your knowledge to use straight away and tackle common problems. You’ll be introduced to various data manipulation and data transformation solutions that can be applied to data, find out how to manage and optimize Delta tables, and get to grips with ingesting and processing streaming data. The book will also show you how to improve the performance problems of Apache Spark apps and Delta Lake. Advanced recipes later in the book will teach you how to use Databricks to implement DataOps and DevOps practices, as well as how to orchestrate and schedule data pipelines using Databricks Workflows. You’ll also go through the full process of setup and configuration of the Unity Catalog for data governance. By the end of this book, you’ll be well-versed in building reliable and scalable data pipelines using modern data engineering technologies.What you will learn Perform data loading, ingestion, and processing with Apache Spark Discover data transformation techniques and custom user-defined functions (UDFs) in Apache Spark Manage and optimize Delta tables with Apache Spark and Delta Lake APIs Use Spark Structured Streaming for real-time data processing Optimize Apache Spark application and Delta table query performance Implement DataOps and DevOps practices on Databricks Orchestrate data pipelines with Delta Live Tables and Databricks Workflows Implement data governance policies with Unity Catalog Who this book is for This book is for data engineers, data scientists, and data practitioners who want to learn how to build efficient and scalable data pipelines using Apache Spark, Delta Lake, and Databricks. To get the most out of this book, you should have basic knowledge of data architecture, SQL, and Python programming.
Mastering Delta Lake
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Author : Robert Johnson
language : en
Publisher: HiTeX Press
Release Date : 2025-01-05
Mastering Delta Lake written by Robert Johnson and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-05 with Computers categories.
"Mastering Delta Lake: Optimizing Data Lakes for Performance and Reliability" is an authoritative guide designed to equip data professionals with the knowledge and skills necessary to harness the power of Delta Lake. As businesses increasingly depend on large-scale data processing for strategic insights, efficient data management frameworks have become paramount. This book offers a comprehensive exploration of Delta Lake, unveiling its transformative capabilities in enhancing data lakes with robust transactional guarantees, schema enforcement, and data versioning. By demystifying complex architectures and demonstrating practical applications, the text serves as an invaluable resource for those looking to modernize their data ecosystems. With a structured approach, this book covers the intricate components of Delta Lake, from its foundational architecture to advanced features such as real-time data ingestion and query optimization. Each chapter delves into specialized topics, ensuring that readers understand both the theoretical and practical aspects of Delta Lake implementation. Through detailed case studies and best practices, the book also highlights successful real-world deployments across various industries, providing readers with insightful perspectives on adapting Delta Lake to meet diverse business needs. "Mastering Delta Lake" promises to elevate the proficiency of data architects, engineers, and analysts, empowering them to unlock new levels of performance and reliability in their data management strategies.
Ultimate Data Engineering With Databricks
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Author : Mayank Malhotra
language : en
Publisher: Orange Education Pvt Ltd
Release Date : 2024-02-14
Ultimate Data Engineering With Databricks written by Mayank Malhotra and has been published by Orange Education Pvt Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-14 with Computers categories.
Navigating Databricks with Ease for Unparalleled Data Engineering Insights. KEY FEATURES ● Navigate Databricks with a seamless progression from fundamental principles to advanced engineering techniques. ● Gain hands-on experience with real-world examples, ensuring immediate relevance and practicality. ● Discover expert insights and best practices for refining your data engineering skills and achieving superior results with Databricks. DESCRIPTION Ultimate Data Engineering with Databricks is a comprehensive handbook meticulously designed for professionals aiming to enhance their data engineering skills through Databricks. Bridging the gap between foundational and advanced knowledge, this book employs a step-by-step approach with detailed explanations suitable for beginners and experienced practitioners alike. Focused on practical applications, the book employs real-world examples and scenarios to teach how to construct, optimize, and maintain robust data pipelines. Emphasizing immediate applicability, it equips readers to address real data challenges using Databricks effectively. The goal is not just understanding Databricks but mastering it to offer tangible solutions. Beyond technical skills, the book imparts best practices and expert tips derived from industry experience, aiding readers in avoiding common pitfalls and adopting strategies for optimal data engineering solutions. This book will help you develop the skills needed to make impactful contributions to organizations, enhancing your value as data engineering professionals in today's competitive job market. WHAT WILL YOU LEARN ● Acquire proficiency in Databricks fundamentals, enabling the construction of efficient data pipelines. ● Design and implement high-performance data solutions for scalability. ● Apply essential best practices for ensuring data integrity in pipelines. ● Explore advanced Databricks features for tackling complex data tasks. ● Learn to optimize data pipelines for streamlined workflows. WHO IS THIS BOOK FOR? This book caters to a diverse audience, including data engineers, data architects, BI analysts, data scientists and technology enthusiasts. Suitable for both professionals and students, the book appeals to those eager to master Databricks and stay at the forefront of data engineering trends. A basic understanding of data engineering concepts and familiarity with cloud computing will enhance the learning experience. TABLE OF CONTENTS 1. Fundamentals of Data Engineering 2. Mastering Delta Tables in Databricks 3. Data Ingestion and Extraction 4. Data Transformation and ETL Processes 5. Data Quality and Validation 6. Data Modeling and Storage 7. Data Orchestration and Workflow Management 8. Performance Tuning and Optimization 9. Scalability and Deployment Considerations 10. Data Security and Governance Last Words Index
Fundamentals Of Data Engineering
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Author : Joe Reis
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2022-06-22
Fundamentals Of Data Engineering written by Joe Reis and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-22 with Computers categories.
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
Practical Machine Learning On Databricks
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Author : Debu Sinha
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-11-24
Practical Machine Learning On Databricks written by Debu Sinha and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-24 with Computers categories.
Take your machine learning skills to the next level by mastering databricks and building robust ML pipeline solutions for future ML innovations Key Features Learn to build robust ML pipeline solutions for databricks transition Master commonly available features like AutoML and MLflow Leverage data governance and model deployment using MLflow model registry Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionUnleash the potential of databricks for end-to-end machine learning with this comprehensive guide, tailored for experienced data scientists and developers transitioning from DIY or other cloud platforms. Building on a strong foundation in Python, Practical Machine Learning on Databricks serves as your roadmap from development to production, covering all intermediary steps using the databricks platform. You’ll start with an overview of machine learning applications, databricks platform features, and MLflow. Next, you’ll dive into data preparation, model selection, and training essentials and discover the power of databricks feature store for precomputing feature tables. You’ll also learn to kickstart your projects using databricks AutoML and automate retraining and deployment through databricks workflows. By the end of this book, you’ll have mastered MLflow for experiment tracking, collaboration, and advanced use cases like model interpretability and governance. The book is enriched with hands-on example code at every step. While primarily focused on generally available features, the book equips you to easily adapt to future innovations in machine learning, databricks, and MLflow.What you will learn Transition smoothly from DIY setups to databricks Master AutoML for quick ML experiment setup Automate model retraining and deployment Leverage databricks feature store for data prep Use MLflow for effective experiment tracking Gain practical insights for scalable ML solutions Find out how to handle model drifts in production environments Who this book is forThis book is for experienced data scientists, engineers, and developers proficient in Python, statistics, and ML lifecycle looking to transition to databricks from DIY clouds. Introductory Spark knowledge is a must to make the most out of this book, however, end-to-end ML workflows will be covered. If you aim to accelerate your machine learning workflows and deploy scalable, robust solutions, this book is an indispensable resource.