You're reading from Learning PySpark Build data-intensive applications locally and deploy at scale using the combined powers of Python and Spark 2.0

Product type Paperback

Published in Feb 2017

Publisher Packt

ISBN-13 9781786463708

Length 274 pages

Edition 1st Edition

Languages

Python

Tools

Apache Spark

Concepts

Data Processing

Authors (2):

Denny Lee

Tomasz Drabas

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Table of Contents (13) Chapters

Preface

1. Understanding Spark FREE CHAPTER

2. Resilient Distributed Datasets

3. DataFrames

4. Prepare Data for Modeling

5. Introducing MLlib

6. Introducing the ML Package

7. GraphFrames

8. TensorFrames

9. Polyglot Persistence with Blaze

10. Structured Streaming

11. Packaging Spark Applications

Index

What is Apache Spark?

Apache Spark is an open-source powerful distributed querying and processing engine. It provides flexibility and extensibility of MapReduce but at significantly higher speeds: Up to 100 times faster than Apache Hadoop when data is stored in memory and up to 10 times when accessing disk.

Apache Spark allows the user to read, transform, and aggregate data, as well as train and deploy sophisticated statistical models with ease. The Spark APIs are accessible in Java, Scala, Python, R and SQL. Apache Spark can be used to build applications or package them up as libraries to be deployed on a cluster or perform quick analytics interactively through notebooks (like, for instance, Jupyter, Spark-Notebook, Databricks notebooks, and Apache Zeppelin).

Apache Spark exposes a host of libraries familiar to data analysts, data scientists or researchers who have worked with Python's pandas or R's data.frames or data.tables. It is important to note that while Spark DataFrames will be familiar to pandas or data.frames / data.tables users, there are some differences so please temper your expectations. Users with more of a SQL background can use the language to shape their data as well. Also, delivered with Apache Spark are several already implemented and tuned algorithms, statistical models, and frameworks: MLlib and ML for machine learning, GraphX and GraphFrames for graph processing, and Spark Streaming (DStreams and Structured). Spark allows the user to combine these libraries seamlessly in the same application.

Apache Spark can easily run locally on a laptop, yet can also easily be deployed in standalone mode, over YARN, or Apache Mesos - either on your local cluster or in the cloud. It can read and write from a diverse data sources including (but not limited to) HDFS, Apache Cassandra, Apache HBase, and S3: