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  • Book Overview & Buying Spark for Data Science
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Spark for Data Science

Spark for Data Science

By : Srinivas Duvvuri, Bikramaditya Singhal
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Spark for Data Science

Spark for Data Science

By: Srinivas Duvvuri, Bikramaditya Singhal
Buy this Book

Overview of this book

This is the era of Big Data. The words ‘Big Data’ implies big innovation and enables a competitive advantage for businesses. Apache Spark was designed to perform Big Data analytics at scale, and so Spark is equipped with the necessary algorithms and supports multiple programming languages. Whether you are a technologist, a data scientist, or a beginner to Big Data analytics, this book will provide you with all the skills necessary to perform statistical data analysis, data visualization, predictive modeling, and build scalable data products or solutions using Python, Scala, and R. With ample case studies and real-world examples, Spark for Data Science will help you ensure the successful execution of your data science projects.
Table of Contents (12 chapters)
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Preface
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Preface
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What this book covers
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What you need for this book
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Who this book is for
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Conventions
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Reader feedback
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Customer support
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1
1. Big Data and Data Science – An Introduction
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1. Big Data and Data Science – An Introduction
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Big data overview
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Challenges with big data analytics
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Evolution of big data analytics
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Spark for data analytics
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The Spark stack
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Summary
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References
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2. The Spark Programming Model
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2. The Spark Programming Model
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The programming paradigm
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The Spark engine
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The RDD API
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RDD operations
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Summary
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References
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3. Introduction to DataFrames
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3. Introduction to DataFrames
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Why DataFrames?
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Spark SQL
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The DataFrame API
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Creating DataFrames
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DataFrame operations
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Summary
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References
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4. Unified Data Access
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4. Unified Data Access
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Data abstractions in Apache Spark
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Datasets
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Spark SQL
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Structured Streaming
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Continuous applications
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Summary
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References
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5. Data Analysis on Spark
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5. Data Analysis on Spark
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Data analytics life cycle
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Data acquisition
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Data preparation
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Basics of statistics
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Descriptive statistics
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Inferential statistics
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Summary
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References
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6. Machine Learning
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6. Machine Learning
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Introduction
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MLlib and the Pipeline API
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Introduction to machine learning
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Regression methods
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Classification methods
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Linear Support Vector Machines (SVM)
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Training an SVM
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Decision trees
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Ensembles
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Multilayer perceptron classifier
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Clustering techniques
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Summary
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References
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7. Extending Spark with SparkR
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7. Extending Spark with SparkR
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SparkR basics
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Advantages and limitations
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Programming with SparkR
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SparkR DataFrames
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Machine learning
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Summary
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References
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8. Analyzing Unstructured Data
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8. Analyzing Unstructured Data
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Sources of unstructured data
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Processing unstructured data
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Text classification
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Text clustering
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Dimensionality reduction
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Singular Value Decomposition
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Summary
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References:
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9. Visualizing Big Data
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9. Visualizing Big Data
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Why visualize data?
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Data visualization tools
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Data visualization techniques
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Summary
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References
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10. Putting It All Together
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10. Putting It All Together
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A quick recap
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Introducing a case study
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The business problem
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Data acquisition and data cleansing
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Developing the hypothesis
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Data exploration
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Data preparation
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Model building
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Data visualization
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Communicating the results to business users
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Summary
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References
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11. Building Data Science Applications
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11. Building Data Science Applications
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Scope of development
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The Scala advantage
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Spark development status
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The big data trends
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Summary
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References

Spark for data analytics

Soon after the Spark project was successful in the AMP labs, it was made open source in 2010 and transferred to the Apache Software Foundation in 2013. It is currently being led by Databricks.

Spark offers many distinct advantages over other distributed computing platforms, such as:

  • A faster execution platform for both iterative machine learning and interactive data analysis
  • Single stack for batch processing, SQL queries, real-time stream processing, graph processing, and complex data analytics
  • Provides high-level API to develop a diverse range of distributed applications by hiding the complexities of distributed programming
  • Seamless support for various data sources such as RDBMS, HBase, Cassandra, Parquet, MongoDB, HDFS, Amazon S3, and so on

Spark for data analytics

The following is a pictorial representation of in-memory data sharing for iterative algorithms:

Spark for data analytics

Spark hides the complexities in writing the core MapReduce jobs and provides most of the functionalities through simple function calls. Because of its simplicity, it is able to cater to wider and bigger audience groups such as data scientists, data engineers, statisticians, and R/Python/Scala/Java developers.

The Spark architecture broadly consists of a data storage layer, management framework, and API. It is designed to work on top of an HDFS filesystem, and thereby leverages the existing ecosystem. Deployment could be as a standalone server or on distributed computing frameworks such as Apache Mesos or YARN. An API is provided for Scala, the language in which Spark is written, along with Java, R and Python.

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Tech Concepts
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Programming languages
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