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Distributed Data Systems with Azure Databricks

You're reading from   Distributed Data Systems with Azure Databricks Create, deploy, and manage enterprise data pipelines

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Product type Paperback
Published in May 2021
Publisher Packt
ISBN-13 9781838647216
Length 414 pages
Edition 1st Edition
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Author (1):
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Alan Bernardo Palacio Alan Bernardo Palacio
Author Profile Icon Alan Bernardo Palacio
Alan Bernardo Palacio
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Introducing Databricks
2. Chapter 1: Introduction to Azure Databricks FREE CHAPTER 3. Chapter 2: Creating an Azure Databricks Workspace 4. Section 2: Data Pipelines with Databricks
5. Chapter 3: Creating ETL Operations with Azure Databricks 6. Chapter 4: Delta Lake with Azure Databricks 7. Chapter 5: Introducing Delta Engine 8. Chapter 6: Introducing Structured Streaming 9. Section 3: Machine and Deep Learning with Databricks
10. Chapter 7: Using Python Libraries in Azure Databricks 11. Chapter 8: Databricks Runtime for Machine Learning 12. Chapter 9: Databricks Runtime for Deep Learning 13. Chapter 10: Model Tracking and Tuning in Azure Databricks 14. Chapter 11: Managing and Serving Models with MLflow and MLeap 15. Chapter 12: Distributed Deep Learning in Azure Databricks 16. Other Books You May Enjoy

Chapter 6: Introducing Structured Streaming

Many organizations have a need to consume large amounts of data continuously in their everyday processes. Therefore, in order to be able to extract insights and use the data, we need to be able to process this information as it arrives, resulting in a need for continuous data ingestion processes. These continuous applications create a need to overcome challenges such as creating a reliable process that ensures the correctness of the data, despite possible failures such as traffic spikes, data not arriving in time, upstream failures, and so on, which are common when working with continuously incoming data or transforming data without consistent file formats that have different structure levels or need to be aggregated before being used.

The most traditional way of dealing with these issues was to work with batches of data executed in periodic tasks, which processed raw streams and data and stored them into more efficient formats to allow...

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