The data in a Data Warehouse system is loaded with an ETL (Extract, Transform, Load) tool. As the name suggests, it performs the following three operations
You can also extract data from flat files like spreadsheets and CSV files using an ETL tool and load it into an OLAP data warehouse for data analysis and reporting. Let us take an example to understand it better.
Let us now discuss in a little more detail the key steps involved in an ETL procedure
A typical ETL tool-based data warehouse uses staging area, data integration, and access layers to perform its functions. It’s normally a 3-layer architecture.
- Staging Layer − The staging layer or staging database is used to store the data extracted from different source data systems.
- Data Integration Layer − The integration layer transforms the data from the staging layer and moves the data to a database, where the data is arranged into hierarchical groups, often called dimensions, and into facts and aggregate facts. The combination of facts and dimensions tables in a DW system is called a schema.
- Access Layer − The access layer is used by end-users to retrieve the data for analytical reporting and information.
ETL testing is done before data is moved into a production data warehouse system. It is sometimes also called as table balancing or production reconciliation. It is different from database testing in terms of its scope and the steps to be taken to complete this.
The main objective of ETL testing is to identify and mitigate data defects and general errors that occur prior to processing of data for analytical reporting.
ETL Testing – Tasks to be Performed
Here is a list of the common tasks involved in ETL Testing −
- Understand the data to be used for reporting.
- Review the Data Model.
- Source to target mapping.
- Data checks on source data.
- Packages and schema validation.
- Data verification in the target system.
- Verification of data transformation calculations and aggregation rules.
- Sample data comparison between the source and the target system.
- Data integrity and quality checks in the target system.
- Performance testing on data.
ETL testing involves the following operations -
Common ETL testing tools include QuerySurge, Informatica, etc.
- Validation of data movement from the source to the target system.
- Verification of data count in the source and the target system.
- Verifying data extraction, transformation as per requirement and expectation.
- Verifying if table relations – joins and keys – are preserved during the transformation.
ETL Testing – Performance
ETL performance tuning is used to ensure if an ETL system can handle an expected load of multiple users and transactions. Performance tuning typically involves server-side workload on the ETL system. It is used to test the server response in multiuser environment and to find bottlenecks. These can be found in source and target systems, mapping of systems, configuration like session management properties, etc.
How to Perform ETL Testing Performance Tuning?
Follow the steps given below to perform ETL testing performance tuning −
- Step 1 − Find the load that is being transformed in production.
- Step 2 − Create new data of that same load or move from Production data to your local performance server.
- Step 3 − Disable the ETL until you generate the load required.
- Step 4 − Take the count of the needed data from the tables of the database.
- Step 5 − Note down the last run of ETL and enable the ETL, so that it will get enough stress to transform the entire load created. Run it
- Step 6 − After the ETL completes its run, take the count of the data created.
ETL Testing – Scalability
The goal of ETL testing is to achieve credible data. Data credibility can be attained by making the testing cycle more effective.
A comprehensive test strategy is the setting up of an effective test cycle. The testing strategy should cover test planning for each stage of ETL process, every time the data moves and state the responsibilities of each stakeholder, e.g., business analysts, infrastructure team, QA team, DBA’s, Developers and Business Users.
To ensure testing readiness from all aspects, the key areas a test strategy should focus on are −
- Scope of testing − Describe testing techniques and types to be used.
- Setting up the test environment.
- Test data availability − It is recommended to have production like data covering all/critical business requirement.
- Data quality and performance acceptance criteria.
ETL Testing – Data Quality
Checking data quality during ETL testing involves performing quality checks on data that is loaded in the target system. It includes the following tests
The Number format should be same across the target system. For example, in the source system, the format of numbering the columns is x.30, but if the target is only 30, then it has to load not prefixing x. in target column number.
The Date format should be consistent in both the source and the target systems. For example, it should be same across all the records. The Standard format is: yyyy-mm-dd.
Precision value should display as expected in the target table. For example, in the source table, the value is 15.2323422, but in the target table, it should display as 15.23 or round of 15.
It involves checking the data as per the business requirement. The records that don’t meet certain criteria should be filtered out.
Example − Only those records whose date_id >=2015 and Account_Id != ‘001’ should load in the target table.
Some columns should have Null as per the requirement and possible values for that field.
Example − Termination Date column should display Null unless and until its Active status Column is “T” or “Deceased”.
Common checks like From_Date should not greater than To_Date can be done.