Book Image

Big Data Analytics

By : Venkat Ankam
Book Image

Big Data Analytics

By: Venkat Ankam

Overview of this book

Big Data Analytics book aims at providing the fundamentals of Apache Spark and Hadoop. All Spark components – Spark Core, Spark SQL, DataFrames, Data sets, Conventional Streaming, Structured Streaming, MLlib, Graphx and Hadoop core components – HDFS, MapReduce and Yarn are explored in greater depth with implementation examples on Spark + Hadoop clusters. It is moving away from MapReduce to Spark. So, advantages of Spark over MapReduce are explained at great depth to reap benefits of in-memory speeds. DataFrames API, Data Sources API and new Data set API are explained for building Big Data analytical applications. Real-time data analytics using Spark Streaming with Apache Kafka and HBase is covered to help building streaming applications. New Structured streaming concept is explained with an IOT (Internet of Things) use case. Machine learning techniques are covered using MLLib, ML Pipelines and SparkR and Graph Analytics are covered with GraphX and GraphFrames components of Spark. Readers will also get an opportunity to get started with web based notebooks such as Jupyter, Apache Zeppelin and data flow tool Apache NiFi to analyze and visualize data.
Table of Contents (18 chapters)
Big Data Analytics
About the Author
About the Reviewers

Spark SQL as a distributed SQL engine

Spark SQL is generally used in two different ways. The first way is to use it as a library to write SQL, Hive QL, DSL, or to write queries in languages such as Java, Scala, Python, or R. The second way is to use it as a distributed SQL engine in which clients connect to a Thrift server and submit SQL or Hive QL queries using JDBC or ODBC interfaces. It is really useful for data warehousing users to write and execute queries from Business Intelligence (BI) tools interactively. So, Spark SQL can be used for data warehousing solutions as well a distributed SQL query engine.

Spark SQL's Thrift server for JDBC/ODBC access

Spark SQL's thrift server provides JDBC access to Spark SQL.

The Thrift JDBC server corresponds to HiveServer2 in Hive. You can test the JDBC server with the beeline client or any SQL client. From Spark 1.6, by default, the Thrift server runs in multi-session mode.

For a complete list of options for starting thriftserver, use the following command...