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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

Chapter 8: Handling Atypical Data Scenarios with Delta

"Every problem has a solution. Sometimes it just takes a long time to find the solution – even if it's right in front of your nose."       

– Daniel Handler, American author and musician

In the previous chapters, we established the need for a Lakehouse architecture paradigm to handle a wide range of use cases, from BI to AI. Data wrangling by itself may not be sufficient to get the data ready for consumption. Several conditions need to be addressed to ensure not only that the data is cleansed and transformed as per the business requirements but also that it is fit for the use case at hand. So, even when the logic of the pipelines has been ironed out, other statistical attributes of the data need to be addressed. This helps ensure that the data patterns for which it was initially designed still hold and are making the most...

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