How do you test data warehouses for different platforms?

1 Choose the right testing approach

Depending on the platform and the type of data warehouse, you may need to adopt different testing approaches. For example, for cloud-based data warehouses, you may need to use cloud-native tools and services, such as AWS Glue or Azure Data Factory, to test the ETL processes and the data quality. For on-premise data warehouses, you may need to use traditional tools and frameworks, such as SQL Server Integration Services or Informatica, to test the ETL processes and the data quality. For relational data warehouses, you may need to use schema-based testing, which focuses on verifying the structure and integrity of the tables and columns. For dimensional data warehouses, you may need to use business rule-based testing, which focuses on verifying the logic and consistency of the dimensions and measures.

2 Define the test scope and objectives

Before you start testing your data warehouse, you need to define the test scope and objectives clearly. The test scope defines what aspects of the data warehouse you will test, such as the data sources, the data models, the ETL processes, the reports, or the performance. The test objectives define what you want to achieve from the testing, such as validating the data quality, verifying the data accuracy, ensuring the data security, or measuring the data performance. By defining the test scope and objectives, you can plan your testing strategy and prioritize your test cases accordingly.

3 Design and execute test cases

Once you have defined the test scope and objectives, you need to design and execute test cases that cover the different aspects of the data warehouse. Test cases are specific scenarios that describe the expected input, output, and behavior of the data warehouse. You can use various techniques and methods to design test cases, such as boundary value analysis, equivalence partitioning, decision tables, or use cases. You can also use different types of test cases, such as unit testing, integration testing, system testing, or user acceptance testing. To execute test cases, you can use manual or automated testing tools, depending on the complexity and frequency of the testing. For example, you can use SQL queries or scripts to test the data quality and accuracy manually, or you can use tools like Talend or Selenium to automate the testing of the ETL processes and the reports.

4 Compare and validate data

One of the most important steps in testing data warehouses is to compare and validate the data between the source and the target systems. This means that you need to check whether the data in the data warehouse is consistent and correct with the data in the original data sources, such as databases, applications, or files. You also need to check whether the data in the data warehouse meets the business requirements and expectations. You can use various techniques and tools to compare and validate data, such as data profiling, data reconciliation, data auditing, or data lineage. For example, you can use data profiling tools like Dataedo or Alteryx to analyze the data quality and characteristics, or you can use data reconciliation tools like QuerySurge or Informatica Data Validation Option to compare the data between the source and the target systems.

5 Monitor and optimize performance

Another crucial step in testing data warehouses is to monitor and optimize the performance of the data warehouse and its components. This means that you need to measure and improve the speed, efficiency, and reliability of the data warehouse and its processes, such as data loading, data processing, data querying, or data reporting. You can use various techniques and tools to monitor and optimize performance, such as performance testing, benchmarking, tuning, or scaling. For example, you can use performance testing tools like JMeter or LoadRunner to simulate and evaluate the load and stress on the data warehouse, or you can use tuning tools like SQL Server Performance Monitor or Oracle Enterprise Manager to optimize the queries and indexes of the data warehouse.

6 Document and report results

The final step in testing data warehouses is to document and report the results of the testing. This means that you need to record and communicate the findings, issues, and recommendations of the testing to the stakeholders, such as the developers, the analysts, the managers, or the clients. You can use various techniques and tools to document and report results, such as test logs, test reports, dashboards, or charts. For example, you can use test logs to track the execution and status of the test cases, or you can use test reports to summarize the outcomes and metrics of the testing. You can also use dashboards or charts to visualize and present the results of the testing in a clear and concise way.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?