Share

Ultimate Data Engineering with Databricks

2024-02-14 Computers
Download Ultimate Data Engineering with Databricks PDF Online Free

Author :
Release : 2024-02-14
Genre : Computers
Kind : eBook
Book Rating : 788/5 ( reviews)

GET EBOOK


Book Synopsis Ultimate Data Engineering with Databricks by : Mayank Malhotra

Download or read book Ultimate Data Engineering with Databricks written by Mayank Malhotra. This book was released on 2024-02-14. Available in PDF, EPUB and Kindle. Book excerpt: 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

Data Engineering with Apache Spark, Delta Lake, and Lakehouse

2021-10-22 Computers
Download Data Engineering with Apache Spark, Delta Lake, and Lakehouse PDF Online Free

Author :
Release : 2021-10-22
Genre : Computers
Kind : eBook
Book Rating : 321/5 ( reviews)

GET EBOOK


Book Synopsis Data Engineering with Apache Spark, Delta Lake, and Lakehouse by : Manoj Kukreja

Download or read book Data Engineering with Apache Spark, Delta Lake, and Lakehouse written by Manoj Kukreja. This book was released on 2021-10-22. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Machine Learning Engineering in Action

2022-05-17 Computers
Download Machine Learning Engineering in Action PDF Online Free

Author :
Release : 2022-05-17
Genre : Computers
Kind : eBook
Book Rating : 580/5 ( reviews)

GET EBOOK


Book Synopsis Machine Learning Engineering in Action by : Ben Wilson

Download or read book Machine Learning Engineering in Action written by Ben Wilson. This book was released on 2022-05-17. Available in PDF, EPUB and Kindle. Book excerpt: Field-tested tips, tricks, and design patterns for building machine learning projects that are deployable, maintainable, and secure from concept to production. In Machine Learning Engineering in Action, you will learn: Evaluating data science problems to find the most effective solution Scoping a machine learning project for usage expectations and budget Process techniques that minimize wasted effort and speed up production Assessing a project using standardized prototyping work and statistical validation Choosing the right technologies and tools for your project Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices Ferrying a machine learning project from your data science team to your end users is no easy task. Machine Learning Engineering in Action will help you make it simple. Inside, you'll find fantastic advice from veteran industry expert Ben Wilson, Principal Resident Solutions Architect at Databricks. Ben introduces his personal toolbox of techniques for building deployable and maintainable production machine learning systems. You'll learn the importance of Agile methodologies for fast prototyping and conferring with stakeholders, while developing a new appreciation for the importance of planning. Adopting well-established software development standards will help you deliver better code management, and make it easier to test, scale, and even reuse your machine learning code. Every method is explained in a friendly, peer-to-peer style and illustrated with production-ready source code. About the technology Deliver maximum performance from your models and data. This collection of reproducible techniques will help you build stable data pipelines, efficient application workflows, and maintainable models every time. Based on decades of good software engineering practice, machine learning engineering ensures your ML systems are resilient, adaptable, and perform in production. About the book Machine Learning Engineering in Action teaches you core principles and practices for designing, building, and delivering successful machine learning projects. You'll discover software engineering techniques like conducting experiments on your prototypes and implementing modular design that result in resilient architectures and consistent cross-team communication. Based on the author's extensive experience, every method in this book has been used to solve real-world projects. What's inside Scoping a machine learning project for usage expectations and budget Choosing the right technologies for your design Making your codebase more understandable, maintainable, and testable Automating your troubleshooting and logging practices About the reader For data scientists who know machine learning and the basics of object-oriented programming. About the author Ben Wilson is Principal Resident Solutions Architect at Databricks, where he developed the Databricks Labs AutoML project, and is an MLflow committer.

Learning Spark

2020-07-16 Computers
Download Learning Spark PDF Online Free

Author :
Release : 2020-07-16
Genre : Computers
Kind : eBook
Book Rating : 016/5 ( reviews)

GET EBOOK


Book Synopsis Learning Spark by : Jules S. Damji

Download or read book Learning Spark written by Jules S. Damji. This book was released on 2020-07-16. Available in PDF, EPUB and Kindle. Book excerpt: 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 with Databricks

2023-12-17
Download Data Engineering with Databricks PDF Online Free

Author :
Release : 2023-12-17
Genre :
Kind : eBook
Book Rating : /5 ( reviews)

GET EBOOK


Book Synopsis Data Engineering with Databricks by : Sumit Verma

Download or read book Data Engineering with Databricks written by Sumit Verma. This book was released on 2023-12-17. Available in PDF, EPUB and Kindle. Book excerpt: The book teaches readers on Databricks Lakehouse, Delta Live table, Streaming, Workflow, Delta Lake using Databrick platform. The subsequent chapters discuss creating data pipelines utilizing the Databricks Lakehouse platform with data processing. The book teaches to leverage the Databricks Lakehouse platform to develop delta live tables, streamline ETL/ELT operations, orchestration, Data governance using unity catalog, Delta Lake optimization and Databricks Repo. What you will learn Develop end to end data pipeline using Databrick workflow. Data governance using Unity catalog. Delta lake optimization Version control using Databrick Repos.

You may also like...