Data Warehouse Testing Landscape and Tools

27th Aug 2019
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A data warehouse is a set of technologies that allows organizations to collect, store and manage data from various sources. It provides a centralized data repository for information that comes from single or multiple data sources.


Usually, the data warehouse is a combination of different components and technologies that make the data more useful to the stakeholders. It merges the information from a wide range of sources and puts it on a unified and comprehensive database.


The data is usually processed, transformed and ingested. This allows users to access the processed data using SQL clients, business intelligent tools, spreadsheets, and other techniques. Users include data business intelligence professionals, scientists, researchers, developers and more, while industries include finance, manufacturing, ecommerce, health, government bodies, etc.



Data warehouse testing needs


Handling data relies on a process called the Extract-Transform-Load (ETL). In its simplest form, ETL extracts the data from its source, transforms it and finally loads it to the data warehouse or a specific project.


With increasing data sources, types and complexity, it becomes challenging ensuring the data quality and its ability to reliably meet the organization’s needs. In particular, there is a wide range of issues that can compromise data integrity as it is being created, moved, integrated, or transformed.


As such, organizations need to develop comprehensive approaches and strategies to test the data for performance, accuracy, security, business functionality, validity, and other issues. Addressing any issues ensures the success of the data warehouse and business intelligence projects.



Data warehouse testing strategies and typical tests


Ensuring data integrity requires comprehensive testing at all stages. This means testing everything that relates to the data warehouse, including the input, all intermediate stages, and the output. Ideally, this means testing at the boundary of the warehouse right from where the data enters up to the exit as well as all the transformations within the system.


Usually, data warehouse testing falls into four main categories regardless of the tools or technology used. This includes new data warehouse testing, migration testing, change request tests, and report tests.


Generally, comprehensive testing covers


  • Data sources
  • The ETL layer - checks and confirms that data is transformed and properly loaded to the target as expected
  • Actual data warehouse
  • The data warehouse’s front end applications such as BI - checks the accuracy of the data in the reports
  • Testing data warehouses practices usually differ from those of the applications in that is more data-centric. Every organization needs to develop and prioritize a clear strategy on how to test the extraction, transformation, and loading of their data warehouses.


Typical tests revolve around


  • Data-centric testing to validate the quality and correctness of the data.
  • Business testing: To ensure that the data will fulfill the business needs while obeying certain rules. It also confirms that data that is completely and accurately loaded, copied or moved.
  • Data accuracy tests to confirm that the data is accurately transformed, then loaded as required. The test helps to check if errors are arising from issues such as improper mapping of columns, truncated characters, logic issues, etc.
  • Data Transformation testing
  • Production validation testing
  • Data integrity testing
  •  ETL Testing: The ETL testing checks for the accuracy of the data loaded from a source to the destination and also after transformation.



Data warehouse testing challenges


Testing data warehouses is usually a complex and challenging exercise due to the nature of the tests. Some testing methods require higher levels of SQL programming skills which only a few analysts, testers and operations people have.


Despite this, every organization needs to develop a well-defined testing approach to guarantee the best results or reach for an independent software testing services provider to do it for you. Some challenges or issues testers encounter include;


  • The testing deals with huge amounts of data which typically involve comparing millions of records.
  • Deals with heterogeneous data sources. This includes sources such as databases, flat files, and others
  • There may be some data loss during the testing process
  • Lack of comprehensive testbed sometimes
  • Difficulties gathering and building the test data
  •  Ambiguous software requirements
  • Larger data volumes and complexity
  • A very large number of possible test cases
  • incorrect business procedures and processes
  • Inconsistent flow of business data. Most of the times the sample data may not represent all possible business processes.
  • Test results depend on the appropriate test data for different scenarios and this may be unavailable at times.
  • Requires complex SQL queries when there is a need to compare transformed data

 

Most often, testing of the data warehouse is done towards the end of the cycle. At this time, there is a likelihood of bypassing some tests due to limited resources and time constraints. This increases the risks of missing out on critical integrity issues. Also, there are risks of performing the testing during the later cycles. This is because it is usually more difficult and expensive to address integrity issues identified towards the end of the cycle.



Manual and automated testing


Organizations use various techniques to ensure the integrity and validity of the data in the warehouse. These include manual and automated tests which provide different levels of assessment and results. Most of the data testing is done by SQL scripts. Some of the manual practices are resource-intensive, time-consuming, error-prone and inefficient while the automated are faster and more comprehensive and delivers higher levels of accuracy.


Limitations of manual tests include;


  • Narrower test coverage and can only sample a few hundred records.
  • Limits the amount of data that can be tested and have higher risks of bad data reaching the production systems.
  • Performing regression testing is more difficult with manual methods. In addition to increased complexities, it requires more resources and time.
  • Manual methods that rely on SQL scripting or other technologies are time-consuming, error-prone and usually unable to provide comprehensive test coverage.
  • It requires testers with SQL programming expertise and coding skills.
  • Difficulties checking data completeness in the transformed columns

 

Automation helps to speed up the testing, reduce costs, and improve efficiency, reliability, defect detection and coverage among many other benefits. In general, automation eases the work of creating, executing, and maintaining the tests while providing a better means of verifying the data quality across the entire data warehouse stages.


Other benefits of automation include;


  • Automatically identifying problems, anomalies, and other data issues
  • Repeatable, more accurate and scalable, hence ability to easily identify errors by comparing large amounts of data
  •  Enables automatic identification of data integrity issues early enough as they happen and before people make data-driven decisions
  • Does not require high coding or programming skills
  • Ability to provide easy heterogeneous connectivity
  • Intuitive reports & dashboards
  • Automation of the complex SQL validation tests

 


Data warehouse test tools


The most common way people work with data in the warehouse is through the SQL. This is the structured query language and usually used to;


  •  Execute queries: The language is used to ask structured questions as well as receive the answers from the data in the warehouse
  • Retrieve data from the warehouse
  • Insert, delete or update the records in the data in the warehouse
  • Create and set permissions - new tables, views, stored procedures, etc.


Almost all the tools that query, read, and write data to and from the warehouse rely on SQL. This includes tools such as data preparation tools, ETL tools, business intelligence, and OLAP servers and tools.


Most organizations rely on the Extraction, Transformation and Load processes when handling the data in the warehouse. Using ETL, organizations can


  • Fetch data from the source (Extract),
  • Transform it according to the business tools
  • Load transformed data to the data warehouse


The ETL tools ensure seamless integration between the data from different sources. In its basic form, an ETL tool will work as an integrator where it extracts the data from different sources, transforms according to the business rules and finally loads into the unified data warehouse. As the data moves between stages or is transformed, it can get compromised and there is, therefore, the need to ensure that it remains intact throughout the entire cycle.


ETL testing is usually done before moving the data into the data warehouse. This testing, whose main goal is to find defects and allows mitigation measures, is usually done manually or through the use of tools such as QuerySurge and others. Generally testing the ETL processes is a complex exercise that requires proper planning and execution as well as the right tool and strategies.


Generally, a well-planned and defined testing ensures a robust and reliable data warehouse by verifying and validating the ETL processes.



Popular data warehouse automation tools

 

There are both open source and commercial testing tools for the data warehouses. Although there are manual-based testing practices which have been common for traditional data warehouses, the automation tools are increasingly becoming popular. The benefits of automation include simplifying the processes, accuracy, faster, repeatability, etc.


Most of the available ETL tools are designed to automate the testing processes for a data warehouse as well as big data. This ensures the data remains intact right from the source where it is extracted from, during transformation and when loaded to the target systems.


Although the tools may differ in their operations and what they deliver, some typical deliverables include;


  • Automating most of the manual testing practices and efforts
  • Improving the quality of data easily
  • Accelerating the data warehouse testing processes and delivery cycles
  • Simplifying testing data warehouses across different platforms
  • Increasing data testing coverage
  • Providing shareable data health dashboards and reports
  • Performing complete data integrity and validity tests while identifying data defects
  • Reducing the need for higher programming skills, manual efforts, data testing costs, and resource requirements


Some of the popular data warehouse testing tools includes


  • QuerySurge
  • Informatica
  • QualiDI
  • ICEDQ
  • ETL Validator

 


Conclusion


A data warehouse provides a unified and centralized system that stores all the current and historical transactional information for the organization. Today, organizations are relying on the information from the data warehouses to make important business decisions.

 

However, bad data and other integrity issues can lead to wrong decisions, mistrust and project failures. Also, lack of trust will lead to low usage of the data hence low ROI. As such, it is important to conduct comprehensive testing to confirm that the information in the data warehouse is valid and accurate.


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