- Use Apache Spark for data processing with these hands-on recipes
- Implement end-to-end, large-scale data analysis better than ever before
- Work with powerful libraries such as MLLib, SciPy, NumPy, and Pandas to gain insights from your data
Spark has emerged as the most promising big data analytics engine for data science professionals. The true power and value of Apache Spark lies in its ability to execute data science tasks with speed and accuracy. Spark’s selling point is that it combines ETL, batch analytics, real-time stream analysis, machine learning, graph processing, and visualizations. It lets you tackle the complexities that come with raw unstructured data sets with ease.
This guide will get you comfortable and confident performing data science tasks with Spark. You will learn about implementations including distributed deep learning, numerical computing, and scalable machine learning. You will be shown effective solutions to problematic concepts in data science using Spark’s data science libraries such as MLLib, Pandas, NumPy, SciPy, and more. These simple and efficient recipes will show you how to implement algorithms and optimize your work.
What you will learn
- Explore the topics of data mining, text mining, Natural Language Processing, information retrieval, and machine learning.
- Solve real-world analytical problems with large data sets.
- Address data science challenges with analytical tools on a distributed system like Spark (apt for iterative algorithms), which offers in-memory processing and more flexibility for data analysis at scale.
- Get hands-on experience with algorithms like Classification, regression, and recommendation on real datasets using Spark MLLib package.
- Learn about numerical and scientific computing using NumPy and SciPy on Spark.
- Use Predictive Model Markup Language (PMML) in Spark for statistical data mining models.
About the Author
Padma Priya Chitturi is Analytics Lead at Fractal Analytics Pvt Ltd and has over five years of experience in Big Data processing. Currently, she is part of capability development at Fractal and responsible for solution development for analytical problems across multiple business domains at large scale. Prior to this, she worked for an Airlines product on a real-time processing platform serving one million user requests/sec at Amadeus Software Labs. She has worked on realizing large-scale deep networks (Jeffrey dean's work in Google brain) for image classification on the big data platform Spark. She works closely with Big Data technologies such as Spark, Storm, Cassandra and Hadoop. She was an open source contributor to Apache Storm.
Table of Contents
- Big Data Analytics with Spark
- Tricky Statistics with Spark
- Data Analysis with Spark
- Clustering, Classification, and Regression
- Working with Spark MLlib
- NLP with Spark
- Working with Sparkling Water - H2O
- Data Visualization with Spark
- Deep Learning on Spark
- Working with SparkR