Simplifying Data Engineering and Analytics with Delta

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.
Table of Contents
An Introduction to Data Engineering
Data Modeling and ETL
Delta - The Foundation Block for Big Data
Unifying Batch and Streaming with Delta
Data Consolidation in Delta Lake
Solving Common Data Pattern Scenarios with Delta
Delta for Data Warehouse Use Cases
Handling Atypical Data Scenarios with Delta
Delta for Reproducible Machine Learning Pipelines
Delta for Data Products and Services
Operationalizing Data and ML Pipelines
Optimizing Cost and Performance with Delta
Managing Your Data Journey


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