Big Data Immersion

Course Summary

his 3-day class combines all of RTTS' Big Data courses into an intensive session designed to accelerate your learning in this area.

The Big Data and ETL Testing Fundamentals course is designed to familiarize business professionals in the Big Data and ETL space with the basics of testing and validating. This course focuses on getting professionals the knowledge required in order to successfully test and validate Big Data and ETL processes.

The Introduction to Big Data Testing using Hive and HQL course is comprised of of lectures and hands-on training that is designed to provide students with the foundation necessary for testing data warehouses. The course covers several common transformation tests and the HQL syntax required to retrieve the data in order to perform the test.

The Advanced Big Data Testing using HQL course is comprised of lectures and hands-on training that is designed to provide students with advanced techniques necessary for testing big data environments. The course covers advanced HQL transformations and the challenges these issues cause in testing big data scenarios.

Intended Audience

  • Manual Testers
  • Automation Engineers
  • Quality Assurance Analysts
  • Developers
  • Project Managers
  • anyone involved with providing software quality for Big Data

At the end of the course, you will be able to:

  • Describe the purpose of a Big Data and the ETL process
  • Determine an appropriate testing strategy
  • Understand a source-target mapping document
  • Describe an approach to test each business rule
  • Recognize the different testing methods
  • Determine appropriate sample sizes and data permutations
  • Explain the different data error types
  • Have knowledge of the different testing tools
  • Understand the importance automated testing
  • understand Big Data structures and architectures
  • implement a successful process for big data testing
  • create basic HQL queries to return data from a database
  • create HQL queries to join data from multiple tables
  • utilize different filtering commands to narrow data results
  • make use of different aggregate HQL functions
  • manipulate and format data returned from queries
  • manipulate and format data returned from queries
  • convert data types for comparisons
  • create and utilize HQL subqueries
  • create and execute more sophisticated transforation tests
  • utilize regular expressions for data comparisons
  • create and utilize subqueries
  • work with derived tables and inlined views
  • take advantage of advanced techniques for big data techniques
  • create tests for unstructured or semi-structured data

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