Simplifying Data Engineering and Analytics with Delta Create analytics-ready data that fuels artificial intelligence and business intelligence

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.

Table of Contents

1. An Introduction to Data Engineering
2. Data Modeling and ETL
3. Delta – The Foundation Block for Big Data
4. Unifying Batch and Streaming with Delta
5. Data Consolidation in Delta Lake
6. Solving Common Data Pattern Scenarios with Delta
7. Delta for Data Warehouse Use Cases
8. Handling Atypical Data Scenarios with Delta
9. Delta for Reproducible Machine Learning Pipelines
10. Delta for Data Products and Services
11. Operationalizing Data and ML Pipelines
12. Optimizing Cost and Performance with Delta
13. Managing Your Data Journey

Anindita Mahapatra is a Solutions Architect at Databricks in the data and AI space helping clients across all industry verticals reap value from their data infrastructure investments.

She teaches a data engineering and analytics course at Harvard University as part of their extension school program.

She has extensive big data and Hadoop consulting experience from Thinkbig/Teradata prior to which she was managing development of algorithmic app discovery and promotion for both Nokia and Microsoft AppStores.

She holds a Masters degree in Liberal Arts and Management from Harvard Extension School, a Masters in Computer Science from Boston University and a Bachelors in Computer Science from BITS Pilani, India.