Spark Analytics for Real-Time Data Processing

English | MP4 | AVC 1920×1080 | AAC 48KHz 2ch | 1h 38m | 279 MB

Query data using Spark SQL, analyze data and perform real-time processing using Spark Streaming.

This tutorial is focused on analytics and real-time data processing using Apache Spark. You will begin with Spark SQL, using the Spark SQL API and built-in functions; within Apache Spark, you will go through some interactive analysis and look at some integrations between Spark and Java/Scala/Python.

You will explore Spark Streaming, streaming context, and DStreams. You will learn how Spark streaming works on top of the Spark core, thus inheriting its features. You will stream data and also learn best practices for managing high-velocity streaming and external data sources.

By the end of this course, you will be able to load data from a variety of structured sources (for example, JSON, Hive, and Parquet) using Spark SQL and schema RDDs and will perform real-time data processing.

Filled with examples, this course will help viewers perform real-time data analysis and help them get started with analytics. Viewers will learn to build streaming applications and handle high-velocity streaming.

What You Will Learn

• Loading data from a variety of structured sources (for example, JSON, Hive, and Parquet) using Spark SQL and schema RDDs.
• Querying data using spark SQL from external tools using JDBC/ODBC for example, Tableau, Qlik, and from the Spark program.
• Integration between SQL and Java/Scala/Python code.
• How Spark Streaming works on top of the Spark core and inherits all its features
• Architecture of Spark Streaming.
• Spark Streaming programming and DStreams.
• Best Practice for managing high-velocity streaming data.
• Best Practice for External data sources.