Without big data, companies are blind and deaf, wandering out onto the web like deer on a freeway
Data has become the most widely used resource for decision making in the past few years. The storage, analysis, and actionable insights gained from it have made it possible for organizations to accurately predict future trends and tweak their business decisions accordingly. However, the extensive use of data has brought about new challenges, especially when it comes to data warehouse testing.
What is Data Warehouse Testing?
The data warehouse testing process involves ensuring data quality by running it through various valid test cases. Typically, there are three levels of testing, unit, integration and system testing.
In unit testing, each component and module, like SQL script and program undergo several parameter-based checks. Integration testing involves bringing different modules of the application together and then testing them to see how well they perform after integration. Finally, in system testing, the whole data warehouse application is run and checked against the predefined policies and configurations.
Significance of data warehouse testing
Data warehouses are becoming complex and huge at the same time. Businesses use multiple sources to extract and store data on their consumers and operations, adding sources after sources to build a well-knitted data architecture. While having a considerable amount of data is important, the relevance of this information to the programs for which it has to be applied holds greater priority.
Data reliability influences analytics and business intelligence
While the factors like the quantity of data and sample size may seem important, the quality of data precedents it when it comes to determining the accuracy of analytics. A trusted data set will generate more precise results than a large one with inaccurate data.
Ensuring quality also minimizes the need to hoard data, which, in turn, reduces the processing load on the data warehouse. This means that testing data warehouses make data processes more efficient, less time consuming, and hassle-free for everyone.
Validate data transformation and loading processes
Data warehousing generally involves data flowing through an ETL pipeline, where it is extracted from disparate sources, transformed, and then loaded onto the destination database. During the testing process, this pipeline comes under scrutiny to determine whether it is functioning properly to meet the business requirements.
Since the information in a data warehouse generally comes from a variety of sources, the testing phase ensures that all of it is suitable for processing and quality data is made available for subsequent processes.
Difference between Data Warehouse Testing and ETL Testing?
The terms, data warehouse testing and ETL testing, are mostly used interchangeably. But in reality, they are different concepts. Broadly, data warehouse testing comprises of two parts: ETL testing and business intelligence testing. The two processes combined ensure that the chances of failure are minimized along the data value chain and data transformation and loading take place in line with business rules.
Let’s look into the components of data warehouse testing in detail:
Data Warehouse Testing
Our discussion up till this point has been about data warehouse testing as a whole. It is the process where the reliability and quality of data are put to test and verification checks are created for preventing bad data. Through data warehouse testing, businesses strengthen their analytics and reporting, enabling them to make strategic decisions based on accurate, real-time insights. This process also has a role in building trust in the systems that perform the analysis and report the outcomes.
In ETL testing, data warehouse users ensure that the ETL steps are working according to the parameters given to the system. These parameters can include load methodologies or row-selection criteria.
Every data set has to be run against different models as each of them have their own parameters. For example, the data of a consumer behavior research based on Maslow’s Hierarchy of Needs will require other data fields and parameters as compared to a study that is applying the Sociological model for the same purpose.
The second part of data warehousing testing is Business Intelligence or BI testing. This process involves validating the reliability, formatting, and performance of reports generated from data warehouses, ensuring that they adhere to the data consumers’ needs. Since ETL Testing mainly deals with the transformation and loading of data, BI testing ensures data quality and consistency in the subsequent processes.
Another term thrown into the mix is database testing. ETL testing occurs on data which is inside a data warehouse. Database testing occurs on systems involved in transactions between data sources and the destination repository. In other words, it is done on the data in transit.
Data Warehouse Testing Challenges
Today, data is being used to create Artificial Intelligence algorithms that can replicate human interactions. Since each person has their own personality, there will be thousands of variables involved in creating their AI profile, which in terms of the amount of data will be massive
In order to create algorithms and programs with such precision, experts believe the quality of data trumps quantity. This is why data warehouse testing services are evolving to meet the growing needs of accurate, trusted data. However, they come with their own share of challenges.
Here are some of the common data warehouse testing challenges:
Inadequate planning before testing
Data is no longer just sales volume and inventory turnover. Today, data includes even intangible things like user behavior while browsing an eCommerce website, considering their tastes, preferences, beliefs and other qualitative elements of their personality that impact decision making.
Nowadays, this granular level data comes from a diverse range of sources and in variable forms. In addition, users from different backgrounds are using data for making decisions based on numerous variables and parameters.
Naturally, each data set has to be tested on its specific parameters based on the situation it will be used in. Without these considerations, the results are sure to miss the mark and the subsequent decisions made are likely to fail, leading to substantial losses.
A proper data warehouse testing strategy begins with the basics. One way to move forward is to divide the process into smaller, high-focus areas, like data validation and integration. Other best practices like source-to-target mapping and selecting data models should be followed.
Effective data transformation requires complex SQL queries at times
SQL operations and the role they play in data transformation is extensive. Each dataset coming in from a different source must undergo a set of transformations, which can vary based on where it is to be loaded. Although ETL platforms should allow simple logical transformation to be performed in one statement or procedure, this step introduces greater issues in checkpointing and modifying transformations.
Data warehouse testing methodologies differ for the analysis of various datasets. There are different types of tests and they are used variably depending on the underlying circumstances. Some common types of ETL testing include:
- Data completeness: It ensures that all the necessary data has been loaded from the source. Some data completeness tests are to validate counts and cross-check source and target data.
- Quality testing: The quality of data refers to its relevance to the scenario in which it has to be used. Data profiling is an effective technique for identifying and resolving quality issues.
- Regression testing: The purpose of regression is to ensure the functionality of before and after changes of an ETL output for a given input is intact. This determines the precision of the overall results.
- ETL performance testing: These tests are important to make sure that the ETL process is working effectively. Performance testing requires a sizeable amount of historic data or else the results vary every time
- Metadata testing: It involves a series of tests to determine the database table fields conform with data model specifications. For example, one of the metadata tests is the data type check in which the data model column type could be string, but the database model type may be varchar, leading to errors and flawed reports.
Challenges in building test data
As technology advances, data sources diversify. Cloud servers, social media networks, CRMs, ERP systems, and the web are just a few sources from where organizations receive data streams. This incoming information is not always in a standardized format. For instance, the database managing social media data may have its own formats and structures that differ from the system that handles data in cloud servers.
This is just one example of the issues that organizations face when collecting information and building the data warehouse architecture. Testing teams don’t always have the appropriate access and tools to extract data from sources. Other departments like the development team within the company can be slow in delivering the requested data due to a large number of manual processes involved in the testing process.
Due to resource-intensive and time-consuming testing processes, organizations should resort to modern technologies like data virtualization and data warehouse automation. They offer capabilities for faster access and delivery of data without compromising the quality, streamlining the data integration for testing and prototyping.
Data warehouse testing is crucial to the process of ensuring the quality of information for the best possible results. However, with the increasing data volumes and highly complex data architectures, you need to have a proper data warehousing testing strategy and effective tools for overcoming the barriers and challenges. We will discuss some effective data warehouse testing practices in the next article to help organizations accurately test and implement their centralized data repository.