What is Data Warehouse Testing?
Data Warehouse stores massive amounts of information, usually collected from multiple heterogeneous sources like DBMS, files, etc. to supply statistical results that help in taking decisions.
Testing is extremely important for data warehouse systems for data validation and to form them to work efficiently and correctly.
Challenges in Data Warehouse Testing:-
Data warehouse testing is separate from application testing in that it requires a data-centric testing method. The following are some of the problems that Data Warehouse Testing faces:
- Testing a data warehouse involves comparing enormous amounts of data, usually millions of records.
- Data that needs to be compared can come from a variety of sources, including databases, flat files, and so on.
- Data is frequently altered, which may require complicated technical queries for data comparison. Thus, it poses challenges for a tester who has limited technical skills.
- The availability of test data with various test scenarios is critical for Data Warehouse testing.
- Business intelligence solutions like OBIEE, Cognos, Business Objects, and Tableau generate reports on the fly using a metadata schema. It can be difficult to test diverse combinations of qualities and metrics.
- Testing these reports for instability, stress, and functioning can be difficult due to the volume of reports and data.
What is involved in Data Warehouse Testing
- Regression Testing
- Data Quality
- Data Transformation
- Data Completeness
ETL Testing + BI Reporting Testing = Data warehouse Testing
ETL testing is a sub-part part of the Data Warehouse testing process. Data extractions, data transformations, and data loading are used to create a data warehouse. Data is extracted from different sources, transformed according to BI reporting needs, and then loaded into a target data warehouse using ETL operations.
We offer upgraded technologies to resolve Data Warehouse Testing issues and worries. To get better data insights, we provide testing solution in a very systematic way:-
We can extract data from multiple-data platforms in less time. We work on image base data extraction so that data cannot be modified in any way. There is no limit on data size extraction
Scheduling & Auto-email:
We can schedule testing at any time of the day and on completion, the system automatically sends an email to stakeholders.
We provide instant detailed output report at the end of the activity via auto-email or manually to address the testing issues if any so, that the stakeholders can respond effectively.
Supported data technologies
Our solutions support below data platform for either legacy/Source or new/target system
- Amazon Redshift, DynamoDB, Simple Storage Service (S3), Athena
- Confluent KSQL
- Databricks in Azure
- Google BigQuery
- HP Tandem
- Pivotal GreenPlum
- Teradata, Aster
- Apache Hadoop/Hive/Spark/Kafka
- Flat Files (delimited and fixed-width)
- Oracle (Oracle db, MySQL, Exadata)
- IBM (DashDB, BigInsights, DB2, Netezza, Informix, Cloudant, Cognos Analytics)
- Microsoft (Azure Synapse Analytics, SQL Server, PDW, SSAS, Access, Excel)
- SAP (HANA, IQ, ASE, SQL Anywhere, Business Objects)
- Azure Analysis Services, Data Lake Storage, Blob Storage, SQL Data Warehouse, SQL Database