Book Image

Apache Spark 2: Data Processing and Real-Time Analytics

By : Romeo Kienzler, Md. Rezaul Karim, Sridhar Alla, Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen Mei
Book Image

Apache Spark 2: Data Processing and Real-Time Analytics

By: Romeo Kienzler, Md. Rezaul Karim, Sridhar Alla, Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen Mei

Overview of this book

Apache Spark is an in-memory, cluster-based data processing system that provides a wide range of functionalities such as big data processing, analytics, machine learning, and more. With this Learning Path, you can take your knowledge of Apache Spark to the next level by learning how to expand Spark's functionality and building your own data flow and machine learning programs on this platform. You will work with the different modules in Apache Spark, such as interactive querying with Spark SQL, using DataFrames and datasets, implementing streaming analytics with Spark Streaming, and applying machine learning and deep learning techniques on Spark using MLlib and various external tools. By the end of this elaborately designed Learning Path, you will have all the knowledge you need to master Apache Spark, and build your own big data processing and analytics pipeline quickly and without any hassle. This Learning Path includes content from the following Packt products: • Mastering Apache Spark 2.x by Romeo Kienzler • Scala and Spark for Big Data Analytics by Md. Rezaul Karim, Sridhar Alla • Apache Spark 2.x Machine Learning Cookbook by Siamak Amirghodsi, Meenakshi Rajendran, Broderick Hall, Shuen MeiCookbook
Table of Contents (23 chapters)
Title Page
Copyright
About Packt
Contributors
Preface
Index

Introduction


Text analytics is at the intersection of machine learning, mathematics, linguistics, and natural language processing. Text analytics, referred to as text mining in older literature, attempts to extract information and infer higher level concepts, sentiment, and semantic details from unstructured and semi-structured data. It is important to note that the traditional keyword searches are insufficient to deal with noisy, ambiguous, and irrelevant tokens and concepts that need to be filtered out based on the actual context.

Ultimately, what we are trying to do is for a given set of documents (text, tweets, web, and social media), is determine what the gist of the communication is and what concepts it is trying to convey (topics and concepts). These days, breaking down a document into its parts and taxonomy is too primitive to be considered text analytics. We can do better.

Spark provides a set of tools and facilities to make text analytics easier, but it is up to the users to combine...