Learn how to collect, organize and analyze large sets of data to discover patterns and useful information for business insights.
Understand the analytical findings that can lead to more effective marketing, new revenue opportunities, better customer service, improved operational efficiency, over rival organizations and other business benefits.
In this session we will discuss about the origination, explosion and use cases of Big Data. We will learn how Big Data is helping us becoming proactive. We’ll understand the challenges that are associated with Big Data and characteristics of Big Data. Learn how to describe the Big Data landscape including examples of real world big data problems including the three key sources of Big Data: people, organizations, and sensors.
This course is designed to give you in-depth knowledge of the Big Data framework using Hadoop and Spark, including HDFS, YARN, and MapReduce. You will learn to use Pig, Hive, and Impala to process and analyze large datasets stored in the HDFS, and use Sqoop and Flume for data ingestion with our big data training.
Understand Hadoop Distributed File System (HDFS) and YARN architecture, and learn how to work with them for storage and resource management. Understand MapReduce and its characteristics and assimilate advanced MapReduce concepts. Ingest data using Sqoop and Flume. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution.
Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations. Understand and work with HBase, its architecture and data storage, and learn the difference between HBase and RDBMS. Gain a working knowledge of Pig and its components. Do functional programming in Spark, and implement and build Spark applications