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Simplifying Data Engineering and Analytics with Delta

Simplifying Data Engineering and Analytics with Delta

By : Anindita Mahapatra
4.9 (15)
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Simplifying Data Engineering and Analytics with Delta

Simplifying Data Engineering and Analytics with Delta

4.9 (15)
By: Anindita Mahapatra

Overview of this book

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.
Table of Contents (18 chapters)
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1
Section 1 – Introduction to Delta Lake and Data Engineering Principles
5
Section 2 – End-to-End Process of Building Delta Pipelines
13
Section 3 – Operationalizing and Productionalizing Delta Pipelines

Optimizing with Delta

Delta's support for ACID transactions and quality guarantees helps ensure data reliability, thereby reducing superfluous validation steps and shortening the end-to-end time. This involves less downtime and triage cycles. Delta's support of fine-grained updates, deletes, and merges applies at a file level instead of to the entire partition, leading to less data manipulation and faster operations. This also leads to fewer compute resources, leading to cost savings.

Changing the data layout in storage

Optimizing the layout of the data can help speed up query performance, and there are two ways to do so, namely the following:

  • Compaction, also known as bin-packing
    • Here, lots of smaller files are combined into fewer large ones.
    • Depending on how many files are involved, this can be an expensive operation and it is a good idea to run it either during off-peak hours or on a separate cluster from the main pipeline to avoid unnecessary delays to the...

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