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Data is perhaps the best resource that has guided business decisions ever since commerce was invented. Today, we now have capabilities that allow us to store, analyze, and derive actionable insights from business information. It has allowed companies to adapt their business plans to meet changing consumer needs and even predict future trends.

However, with these innovative processes and technologies comes new problems for us to deal with. This is especially true when it comes to the challenges in data warehouse testing.

What Is Data Warehouse Testing?

Data warehouse testing is the term used when referring to the process of developing and executing complex test cases to assess the integrity of data in a warehouse. This process checks to see if the stored information is reliable, accurate, and in line with the organization’s data framework.

The data warehouse testing process is performed at two junctures: the first is when data from multiple sources are received in a warehouse. The second point is when the test takes into account the whole data pipeline, thereby analyzing data during its extraction, transformation, and loading operations. When data is validated by stages, it becomes easier to identify and fix issues quickly.

Besides that, the testing also considers business intelligence reports and dashboards that use the consolidated data from their sources. By introducing this added validation layer, testers can confirm the quality of data once all ETL operations have been completed.

In short, data warehouse testing covers both BI and ETL testing, two vital aspects that any data warehouse needs.

Significance of Data Warehouse Testing

Data warehouse testing is important because it helps businesses extract and store accurate data to create an effective information architecture. This is particularly crucial with data warehouses growing bigger and becoming increasingly complex.

The following are two advantages you can get when you invest in quality data warehouse testing today:

Data Reliability Influences Business Intelligence

When determining the accuracy of your analytics, the quality of data takes precedence over factors like the size of data samples or the quantity. After all, possessing reliable datasets means you can generate more precise results compared to having a large one that has inaccurate information.

When you prioritize quality, this can help you avoid data hoarding, which can reduce the overall processing efficiency of a data warehouse. A good data warehouse testing strategy can allow your organization to make data processes less time-consuming, hassle-free, and more efficient for everyone involved.

Helps Validate Data Transformation and Loading Processes

With data warehousing, you can expect the data here to flow via an ETL pipeline where the information is extracted from various sources, transformed, and then stored in its intended database. However, the testing process puts the pipeline under the microscope as it works to determine if everything is working according to the business requirements.

The objective of the testing phase is to ensure that all information in a data warehouse is suitable for processing, especially since they come from different sources.

Goals of Data Warehouse Testing

The following are the main goals in all data warehouse testing phases:

• Data completeness: This goal is to make sure that all of the expected information is loaded through each ETL procedure.
• Data quality: This is to help ensure that the ETL procedure does not accept incorrect data and substitutes default values.
• Data transformations: These guarantee that all information that is to be transformed is accomplished according to business requirements and specifications.
• Integration testing: This is performed to confirm the ETL process works well with the other upstream and downstream procedures.
• Regression testing: Regression testing is done to make sure all existing functions are intact whenever a new release of ETL code and information is accomplished.
• User-interface testing: This is to certify that the data warehousing solution can meet the expectations of users while anticipating any of their future expectations.
• Performance and scalability: This is to ensure that all technical architecture is easily scalable, and that all queries and data loads perform within expected time periods. 
• User-acceptance Testing: This certifies that the data warehousing solution meets the users' current expectations, and successfully anticipates future expectations.

Data Warehouse Testing Strategy

An effective data warehouse testing strategy involves formulating a formal test plan that verifies data requirements as stated in the following:

• Business requirements
• Source to target mapping
• ETL design documents
• Data models for target schemas and source

The following are some of the procedures involved in creating a test strategy:

1. Identify Various Entry Points

   Since there are several stages involved in loading data into a warehouse, it is crucial to identify the different entry points to analyze and confirm the data on each of these steps. When performed only at the destination, the testing results can be confusing — especially once errors have been identified. This makes it difficult to determine the cause of the problem.

   Some examples of entry points to consider are the sources, the staging database, the various points in the ETL pipeline, and the BI engine where the reports are read from the warehouse data.

2. Prepare Required Collaterals

   The two basic collaterals necessary for the data warehouse testing process are the mapping document and database schema representation.

   The mapping document is often in the form of a spreadsheet that maps every column within the source database back towards the destination database. You can also find complex SQL queries here that let you compare the two columns to assess if the data has arrived correctly at the destination.

   Using a data integration solution can let you generate this mapping document, which you can use as your input for designing data warehouse test cases.

3. Design an Automated, Elastic, and Integrated Framework

   Although some information can be loaded by batches and others instantly, new updates can come in via streaming queues. That is why a testing framework design needs to be generic and flexible to accommodate the various data types and sources.

   Furthermore, being able to integrate the test framework using an automated data solution can increase testing process efficiency.

4. Adopt a Comprehensive Testing Approach

   The data testing framework has to reach 100% coverage of the warehousing process. For example, application components like reporting engines and ETL tools have to be included within the testing framework even if data itself is the priority.

   It’s also essential to design various testing approaches such as performance, integration, and functional testing.

8 Steps To Create a Data Warehouse Implementation Plan

These are the steps to help you create a data warehouse implementation plan:

1. Gather Requirements

   There are multiple stakeholders involved in a company-wide data project. These are:

   • Decision-makers: These are the leaders and strategists who will assist you in staying aligned with company objectives.
   • Analysts: The analysts are the data team that helps you in scoping out a project.
   • IT: This team plays a vital role in areas such as helping connect sources to your data pipeline.
   • Compliance and security: This team is in charge of ensuring a risk-free project.

2. Create Warehouse Environment

   You can choose from the following warehouse environments to store data:

   • Public cloud: Use a hosted cloud-service provider, like AWS or Azure. 
   • Private cloud: Hire a third-party provider, or host a cloud on your own hardware. 
   • Hybrid cloud: This can be used in two ways: first, use a mix of on-premise and cloud storage; second, store the data on-premise, and then use cloud capabilities for analytics. 
   • On-premise: Host on a local hardware.

   Whatever you choose, you will be needing three separate environments. These are:

   • Development: This environment holds test data, and can be used by development teams to run tests on new features.
   • Testing: This environment can be used for testing and QA. 
   • Production: This is a live data warehouse that can be accessed by analysts and users. The final changes cannot be implemented, until they have been tested in all other environments as well.

3. Choose the Data Model

   Since your data warehouse will have one schema, you need to pick a model that suits the existing data while allowing you to scale in the future. The main types of schemas include star schema, galaxy schema, snowflake schema and constellation schema.

4. Connect to Sources

   Connecting your sources is a two-step process: extraction and upload.

   The extraction can occur in several ways, such as:

   • API call: This is the most common method, where the transaction is processed by a secure interface.
   • File transfer: Legal systems can export CSV type data files.
   • Data query: In some cases, it is possible to obtain database results using an SQL query.

5. Transform Incoming Data

   The middle part of implementing a data warehouse implementation plan is the transformation of incoming data. This can include other steps like:

   • Validation: This step ensures that all data fits logical constraints, like valid dates and addresses.
   • Cleansing: This step includes removing all corrupt or duplicate data.
   • Harmonization: This ensures that all data follows a single format, such as converting all temperatures to Fahrenheit.
   • Enrichment: Combine records with data from different sources to improve data quality.

6. Create Data Marts

   Data marts are the logical aspect of a warehouse that shows a limited view of all relevant results. These marts are an excellent way for you to deliver targeted results.

   They also help improve data security since they can limit users from accessing relevant information.

7. Configure BI and Analytics

   Business intelligence and analytics tools provide straightforward integration with your data warehouse. These tools rely on the following:

   • Volume: The more data you possess, the more detailed your insights will be.
   • Veracity: High-quality data is vital to provide an accurate assessment of your status.
   • Velocity: Real-time dashboards require fast-moving data streams.

8. Review and Audit

   Once you have everything in place, you can begin taking steps that help ensure the quality of data. The review and audit stage can involve utilizing automated data quality testing programs that measure warehouse data quality.

   You can also do sense checks that let you identify if there are any issues between the raw and stored data.

Conclusion

Data warehouse testing is vital since it helps businesses ensure that the information they obtain from various sources is accurate. That is why organizations should have a data warehouse testing strategy in place to help them tackle the big data testing challenges today.

If you’re unsure how to test a data warehouse, then you need the help of professionals.

At QASource, we can provide you with comprehensive data warehouse testing to ensure that the information you are using for your business is precise and error-free. Our experts are experienced in the various data warehouse testing concepts and can help you every step of the way.

Establish your data warehouse testing process with us, so you can have reliable data at all times. Get in touch today.

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