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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

Automation of CI/CD pipelines 

POC and Pilot code to prove out an end-to-end path does not get sanctioned for production as is. Typically, it makes its way through dev, stage, and prod environments, where it gets tested and scrutinized. A data product may involve different data teams and different departments to come together and test the data product holistically. An ML cycle has a few additional steps around ML artifact testing to ensure that insights are not only generated, but also valid and relevant. So, Continuous Training (CT) and Continuous Monitoring (CM) are additional steps in the pipeline. Last but not least, data has to be versioned because outcomes need to be compared with expected results, sometimes within an acceptable threshold.

Automation takes a little time to build, but it saves a lot more time and grief in the long run. So, investing in testing frameworks and automation around CI/CD pipelines is a task that is worth investing in. Continuous Integration...

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