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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

Moving toward real-time systems

As their names suggest, batch is a form of periodic ingestion of data, whereas streaming is a process where data ingestion is either continuous or in micro-batches. There is no denying that the trend is toward the real-time ingestion, analysis, and consumption of data. This gives rise to the question of why is every pipeline not a streaming one?

There may be several producers of data for the same target table. Some may be fast-moving, while others could be slower. If the nature of your data is such that it comes once a month, then we certainly do not want to have compute running more frequently than once a month from a cost savings perspective. Hence, some folks may say that cases such as these force us to have batch ingestion. In this chapter, we will present an argument to justify that batch is actually a type of streaming workload and that all workloads can be expressed as a streaming pipeline. You may argue, 'Isn't streaming more complex...

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