ETL Testing

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Course Overview

This course covers ETL/Data warehouse testing which is used to test all the stages involved in raw data’s transformation into business intelligence. Businesses collect raw data from daily transactions carried out in human resources, administration, sales, and the ETL process consolidates and transforms this data into usable information. It then stores it in data warehouses. ETL testing performs a series of tasks to debug each stage of the transformation ensuring efficiency. It verifies the quality of the data, validates the source, runs data checks, checks the schema, runs performance tests on the data, keeps error logs, and runs a host of other tests. Thus, ETL and data warehouse testing are an indispensable part of a company’s ability to gather accurate information about its performance in the market.


  • Resume & Interviews Preparation Support
  • Hands on Experience on One Live Project.
  • 100 % Placement Assistance
  • Resume Preparation
  • Interview Preparation
  • Multiple Flexible Batches

Course Duration

  • 6-8 Weekends (Weekend Batches)

Prerequisites :

  • Knowledge of Manual Testing
  • Knowledge of DBMS/SQL basics is advantageous

Who Should Attend?

  • IT professionals
  • Manual & Automation Tester’s
  • Basic knowledge of SQL
  • Database Developers/Administrators


1.1 Database/SQL

  • Data Models
  • Entity Relationship Model
  • Normalization
  • Introduction to SQL and its Data types
  • Operators
  • Data Definition Language (DDL)
  • Data Manipulation Language (DML)
  • Transaction Control Language (TCL)
  • Data Control language (DCL)

1.2 Aggregate data

  • Aggregate functions
  • GROUP BY function
  • Having clause

1.3 Handling multiple tables

  • Joining multiple tables
  • Inner joins,Outer Joins and Self Joins

1.4 Sub-queries Usage

  • Define sub-queries
  • Single-row and multiple row sub-queries
  • Co-related sub-queries
  • Nested sub-queries
  • Inline views
  • Hierarchical queries
  • Distributed queries

1.5 Set operators


1.6 Datawarehouse Fundamental

  • Datawarehouse Overview
  • DWH Characteristics
  • Types of Datawarehouse
  • Data Mart
  • Database vs Data Warehouse
  • Data Mart vs Data Warehouse
  • Data Warehouse benefits
  • DWH Architecture

1.7 Datawarehouse Terminologies

  • Data Cleansing, Partitioning
  • Staging area, Metadata
  • Surrogate Key
  • Snapshot, View and Materialized View
  • Meta Data, Data Mining & Data Cube

1.8 Dimension Modeling

  • Facts and Dimensions
  • Hierarchies and Levels
  • Measures & DWH Schema
  • Star, Snow-flake and Galaxy Schema
  • Additive, Semi Additive, Non Additive Fact/Measure

1.9 ETL Overview

  • Process Flow of ETL
  • Types of Sources and Target
  • ETL Data Load types
  • Active vs Passive transformation
  • Extraction Methods in ETL
  • Tracing Level
  • Types of Data used for ETL Process

1.10 Slowly Changing Dimension

  • What is Slowly Changing Dimension?
  • SCD Types : Type-1, Type-2, Type-3

1.11 ETL Testing Process

  • Categories of ETL Testing
  • Types of ETL Testing
  • How to create ETL Test Case
  • ETL Test Scenarios and Test Cases
  • Types of ETL Bugs/Defects
  • Responsibly of ETL Tester

1.12 Informatica Tool Overview

  • Introduction to Informatica
  • How developers work on Power Center
  • Different components of Power Center and uses
  • Tasks performed in different Components
  • Mapping, Session, Worklet, Workflow, Mapplet
  • Naming Conventions

1.13 Designer Component

  • Working with Sources
  • Working with Flat Files
  • Working with Targets
  • Data Transformation Source and Target
  • Mapping Parameters and Variables
  • Uses of Mapping Wizards

1.14 Workflow Manager Component

  • Workflow Manager Overview
  • Workflow Designer
  • Task Developer
  • Worklet Designer
  • Using Workflow Wizard
  • Creating a Task
  • Configuring Task
  • Workflow links
  • Validating Tasks,Workflows
  • Scheduling and Running Workflows

1.15 Workflow Monitor Component

  • Workflow Monitor Overview
  • Using Workflow Monitor
  • Working with Tasks and Workflows
  • Workflow and Task Status
  • Using Gantt Chart and Task Views
  • Session and Workflow Logs

1.16 Transformation

  • How to use Transformations
  • Aggregator Transformation
  • Expression Transformation
  • Filter Transformation
  • Router Transformation
  • Union Transformation
  • Rank Transformation
  • Sorter Transformation
  • Joiner Transformation


ETL stands for Extract, Transform and Load. Extract does the process of reading data from a database. Transform does the converting of data into a format that could be appropriate for reporting and analysis. While, load does the process of writing the data into the target database.

Classes are held on weekdays and weekends. You can check available schedules and choose the batch timings which are convenient for you.

Yes! We organize a weekend batches with flexible timing for training sessions as per requirement of candidate.

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