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Mastering Apache Spark 2.x

You're reading from   Mastering Apache Spark 2.x Advanced techniques in complex Big Data processing, streaming analytics and machine learning

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Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781786462749
Length 354 pages
Edition 2nd Edition
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Author (1):
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Romeo Kienzler Romeo Kienzler
Author Profile Icon Romeo Kienzler
Romeo Kienzler
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Table of Contents (15) Chapters Close

Preface 1. A First Taste and What’s New in Apache Spark V2 2. Apache Spark SQL FREE CHAPTER 3. The Catalyst Optimizer 4. Project Tungsten 5. Apache Spark Streaming 6. Structured Streaming 7. Apache Spark MLlib 8. Apache SparkML 9. Apache SystemML 10. Deep Learning on Apache Spark with DeepLearning4j and H2O 11. Apache Spark GraphX 12. Apache Spark GraphFrames 13. Apache Spark with Jupyter Notebooks on IBM DataScience Experience 14. Apache Spark on Kubernetes

Understanding the DataSource API

The DataSource API was introduced in Apache Spark 1.1, but is constantly being extended. You have already used the DataSource API without knowing when reading and writing data using SparkSession or DataFrames.

The DataSource API provides an extensible framework to read and write data to and from an abundance of different data sources in various formats. There is built-in support for Hive, Avro, JSON, JDBC, Parquet, and CSV and a nearly infinite number of third-party plugins to support, for example, MongoDB, Cassandra, ApacheCouchDB, Cloudant, or Redis.

Usually, you never directly use classes from the DataSource API as they are wrapped behind the read method of SparkSession or the write method of the DataFrame or Dataset. Another thing that is hidden from the user is schema discovery.

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You have been reading a chapter from
Mastering Apache Spark 2.x - Second Edition
Published in: Jul 2017
Publisher: Packt
ISBN-13: 9781786462749
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