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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

COE best practices

Establishing an internal steering committee/team as the Center of Excellence (COE) for creating advanced analytics is a complex process. Its primary purpose is to provide a blueprint to onboard data teams, and enable them with technical and operational practices, support for handling issues and tickets, and executive alignment to ensure that technical investments align to business objectives and value can be realized and quantified. The role is that of an enabler, as a governance overseer, but never to the point of a bottleneck. In some organizations, the COE team is responsible for managing all or part of an infrastructure and the shared data ingestion process, ratifying vendor tools and frameworks for internal consumption. They are either funded directly or get compensated by a chargeback model from the individual lines of business that they service.

The foundational blocks include the following aspects:

  • Cloud strategy: Which cloud to use, whether a...

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