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Optimizing Databricks Workloads

You're reading from   Optimizing Databricks Workloads Harness the power of Apache Spark in Azure and maximize the performance of modern big data workloads

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Product type Paperback
Published in Dec 2021
Publisher Packt
ISBN-13 9781801819077
Length 230 pages
Edition 1st Edition
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Authors (3):
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Anshul Bhatnagar Anshul Bhatnagar
Author Profile Icon Anshul Bhatnagar
Anshul Bhatnagar
Sarthak Sarbahi Sarthak Sarbahi
Author Profile Icon Sarthak Sarbahi
Sarthak Sarbahi
Anirudh Kala Anirudh Kala
Author Profile Icon Anirudh Kala
Anirudh Kala
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Toc

Table of Contents (13) Chapters Close

Preface 1. Section 1: Introduction to Azure Databricks
2. Chapter 1: Discovering Databricks FREE CHAPTER 3. Chapter 2: Batch and Real-Time Processing in Databricks 4. Chapter 3: Learning about Machine Learning and Graph Processing in Databricks 5. Section 2: Optimization Techniques
6. Chapter 4: Managing Spark Clusters 7. Chapter 5: Big Data Analytics 8. Chapter 6: Databricks Delta Lake 9. Chapter 7: Spark Core 10. Section 3: Real-World Scenarios
11. Chapter 8: Case Studies 12. Other Books You May Enjoy

Learning about dynamic partition pruning

Dynamic partition pruning is a data-skipping technique that can drastically speed up query execution time. Delta lake collects metadata on the partition files it manages so that data can be skipped without the need to access it. This technique is very useful for star schema types of queries as it can dynamically skip partitions and their respective files. Using this technique, we can prune the partitions of a fact table during the join to a dimension table. This is made possible when the filter that's applied to a dimension table to prune its partitions is dynamically applied to the fact table. We will now learn how this technique works by looking at an example. Before we get started, do not forget to spin up the packt-cluster cluster!

In this example, we will demonstrate a star schema model by joining a fact table and a dimension table. A star schema is one of the simplest ways to build a data warehouse. It consists of one or more fact...

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