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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

Data as code – An intelligent pipeline

All the operationalizing aspects referred to in the previous sections would have to be explicitly coded by DevOps, MLOps, and DataOps personas. A managed platform such as Databricks has abstracted the complexity of all these features as part of its DLT offering. The culmination of all these features out of the box gives rise to intelligent pipelines. There is a shift from a procedural to a declarative definition of a pipeline where, as an end user, you specify the "what" aspects of the data transformations, delegating the "how" aspects to the underlying platform. This is especially useful for simplifying the ETL development and go to production process when pipelines need to be democratized across multiple use cases for large, fast-moving data volumes such as IoT sensor data.

These are the key differentiators:

  • The ability to understand the dependencies of the transformations to generate the underlying DAG...

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