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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

Considerations around data governance

Data governance refers to aligning all aspects of data strategy, business strategy, and compliance requirements. A three-pronged approach of people, policy, and process will provide oversight for all data operations from the time data touches a system to the point it leaves. Roles and responsibilities dictate who has access to what data, something that needs to be enforced and monitored. Data lineage is tracked to provide accountability for how data has been transformed at various steps. Delta's history functionality provides a good audit trail. A central catalog builds on top of it and provides a central place for defining the rules, enforcing them, and monitoring compliance via audit logs. Some of these catalogs have to be built and stitched together unless a managed platform that has taken care of these aspects is leveraged.

People using data need to be assured of its quality, so being able to define constraints, note when they have...

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