
Discover how data warehousing consolidates data from multiple sources for analysis and reporting. Explore star and snowflake schemas, slowly changing dimensions, and OLTP versus OLAP for informed decisions.
Cover the basics of SQL, including DDL, DML, and DQL commands, constraints, views, and joins, then explain normalization forms 1NF to 3NF with practical examples.
Master the ETL process through extract, transform, and load with Informatica PowerCenter, focusing on data cleansing, business rule derivations, and loading into a data warehouse.
Learn the types of etl testing, such as not null, unique, and foreign key constraints, check and default values, plus source-to-target validation, duplicates, and end-user testing.
Explore common ETL bugs, from cosmetic UI issues and input/output data type mismatches to calculation errors, data truncation, load conditions, and performance issues under heavy multi-user loads.
Test etl processes end-to-end by validating source tables for data types and duplicates, checking constraints, validating transformations from staging to target, and ensuring loading and quality in the data warehouse.
Discover common etl testing scenarios and test cases for validating source and target structures. Validate data types, mappings, foreign keys, data completeness, quality, and transformations using minus queries.
Learn how to verify data accuracy by comparing source and target data, using value testing and set operators like minus and intersect across transformations.
Learn metadata testing to verify table definitions against the data model, including data types, column details, constraints, and naming standards for source and target tables.
Perform data quality testing in ETL pipelines by validating date formats, nulls, duplicates, and referential integrity, and cleanse production data to enforce validation rules and meaningful data.
Validate data completeness by verifying required loads from source to target. Use record count validation, column data profile checks, and data profiling to compare unique values, null values, and distributions.
Explore ETL testing with Informatica PowerCenter by exploring data warehouse basics, OLAP vs OLTP, dimensional models, SCD types, and core testing techniques like source-to-target and data quality verification.
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COURSE OBJECTIVE
The Primary objective of this course to make you familiar with ETL testing concepts.
The course starts with data warehousing concepts, as that is required before you start testing the data in data warehouse.
You should be well versed is SQL (RDBMS query language), hence this tutorial has a brief introduction to SQL and normalization to structure data in a database.
You will learn how to test the loaded data by ETL process after transformations. After you finish the tutorial, you can start creating a set of test cases for your ETL mapping and build your own queries to execute them.
All sections of the course will have relevant exercises for you to do, so you can practice your new skills.
WHAT IS ETL TESTING?
ETL stands for Extract-Transform-Load. It is a process of loading data from the source system to the data warehouse.
ETL testing is done to ensure that the data loaded from the source system to a data warehouse (after transformations), is accurate.
TOP 3 BENEFITS OF LEARNING ETL TESTING
FREQUENTLY ASKED QUESTIONS
Is it easy to get a job in ETL testing?
I would say it is "easy". But if you have the right knowledge (like this course) and SQL experience, it should not be much of a problem to get a good ETL testing job in various companies. The future of ETL Testing is very bright. There will always be a need to store data for analysis, for any organizations that has large amount of transactions.
What skills are required before I opt for ETL testing?
ETL is all about playing with large data in database. You must have good knowledge of database structured query language (SQL), and knowledge of data warehousing concepts. A short refresher to SQL will be given in the course.
GUARANTEE
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