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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

Addressing data skew

In Spark, data resides in different "partitions" that guide the decision of how to divide the data among different worker nodes to get the benefits of parallelism. In an ideal case, data in each of the partitions is divided equally so that the load on the workers is uniform and the cluster resources are utilized more efficiently. Data skew is a condition in which a table's data is unevenly distributed among partitions in the cluster. This has several negative consequences, namely a reduction in the performance of queries, especially those that involve joins. Joins typically result in shuffle and data skew, which can lead to a labor imbalance among the workers. This means that only a few workers are doing the heavy lifting, prolonging the query response time and resulting in unnecessary compute wastage. Let's look at the four main types of joins:

  • Broadcast Hash Join
    • Requires one side to be small. 
    • No shuffle nor sort is involved...

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