Big Data Analytics with Spark Training Course

The analysis of large datasets involves using an equally large set of computers. Successfully using so many computers entails the use of distributed files systems, such as the Hadoop Distributed File System (HDFS) and parallel computational models, such as Hadoop, MapReduce and Spark.

In this Big Data Analytics with Spark Training Course, you will learn what the blocks are in vast parallel computation projects, and how to use Spark to minimise these tailbacks.

This Big Data Analytics with Spark Training Course will teach you how to conduct supervised an unsupervised machine learning on substantial datasets using the Machine Learning Library (MLlib) and gain hands-on experience using PySpark.

What skills are covered in this Big Data Spark training course? This program will provide you with knowledge and expertise in Scala programming, Spark installation, Resilient Distributed Datasets (RDD), SparkSQL, Spark Streaming, Spark ML Programming, and GraphX programming.

This Zoe training course will empower you with crucial, in-demand Apache Spark skills and guide you to build a competitive advantage for an exciting career as a Hadoop developer.

Course Objectives:

Upon completing this Big Data Analytics with Spark Training Course successfully, participants will be able to:

• Obtain an overview of Big Data & Hadoop including HDFS and YARN (Yet Another Resource Negotiator)
• Gain comprehensive knowledge of various tools that fall in the Spark ecosystem
• Understand how to ingest data in HDFS using Sqoop & Flume
• Program Spark using Pyspark
• Identify the computational trade-offs in a Spark application
• Model data through statistical and machine learning methods
• Use the power of handling real-time data feeds through a publish-subscribe messaging system like Kafka
• Gain exposure to many real-life industry-based projects
• Study projects which are diverse in nature, like banking, telecommunication, social media, and in the government field