Why You Need ETL Testing and What You Need to KnowLourdes William
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The Importance of Data
Data quality is the key to the success of businesses. Bad data leads to inaccurate information that could incur great loss; this in turn could potentially lead to failure of the business. In order to evade it, data needs to be processed and transformed into quality information, and in turn be reported to the right people at the right time.
Put simply, good data is helpful in providing insight. Businesses, when armed with this, can improve the day-to-day decisions they make. This isn’t just for management; it applies to every level from the ground to the top. However, data is rarely useful in its raw state; it must be processed and presented in a way that works in the respective level, so that it can be utilized appropriately.
If the accuracy of data is low at the beginning of the process, it leads to lack of insight, and hence, the decisions it influences are also likely to be poor. Therefore, organizations must realize the criticality of data and understand that quality is more important that quantity. Most people prioritize only on gathering information without giving importance to the accuracy of information and if/how it could be used for further processing.
Organizations that obtain the greatest ROI are those which measure the impact of poor quality of data as well as the benefits of having improved and enhanced data. Metrics range from shorter processing time, reduced hardware costs, shorter sales cycles, accurate analytics, reduced telemarketing costs, increased return on existing technology investments (such as ETL applications), and higher cross-sell and up-sell volumes and other benefits of improved data quality.
What is ETL Testing
ETL stands for Extract, Transform and Load data, and it is predominantly done by standard software tools like Informatica, Ab Initio, Datastage, OWB, SSIS, etc., available in the market. This helps to build, manage, and maintain the integrated/migrated data.
Extract is the process of extracting the desired data from different homogenous or heterogenous data sources (databases/applications).
The extracted data is then transformed to the required format or structure as per business needs. This process can happen on a separate staging environment. Depending on business needs, the transformation carried out can be basic or advanced.
- Basic Transformation: Data is subjected to Cleansing, Scrubbing, Deduplication, Format revision, and key restructuring.
- Advanced Transformation: Data is subjected to Filtering, Joining, Splitting, Sorting, Transposing, Aggregation, Summarization, Derivation, and Integration.
Finally, the transformed data is loaded into the target destination such as databases or datawarehouses in one full load or in incremental mode as per business requirements.
Need for ETL testing
Transportation of data from extraction to loading could result in human or system errors which would result in poor information conveyed across the enterprise. ETL testing ensures that such errors do not occur, and eliminates/mitigates the bugs based on the following strategies:
- Data Completeness
- Data Correctness
- Data Integrity
- Data Reconciliation
- Data Transformation
- Data Quality
- Performance and scalability of system
List of tests performed:
- Unit testing
- System testing
- System Integration testing
- Regression testing
- Performance testing
- User acceptance testing
Challenges faced by testers in ETL/data warehouse projects
Usually testers in ETL/data warehouse projects face the following challenges:
- Frequent changes to requirements
- Availability of source data only for a certain period
- No access to ETL tools or their code
- Unavailability of source to target mapping documents
- More response time required by SQL query
- Verifying and validating data comes from different sources with varied formats and structures
- Unstable testing environments
- Huge volume of data to test
Strategic approach of Cigniti to help in Data centric projects
Cigniti Technologies understands that accurate data is the key to arrive to important decisions in any business. Hence, Cigniti has helped many clients in Banking, Finance, Insurance, and Retail domain to achieve 100% in data quality. We identify bugs, perform root cause analysis and report them at the early stage of SDLC to reduce the cost and time. Before getting ourselves into ETL testing, we inspect the different systems, their models, processes and business requirements for any inconsistencies or ambiguities. We perform data profiling/data mining to better understand the trends and patterns of data and identify any source data bugs.
Data warehouse testing
We compare data from different data sources having different formats/structures to the target systems as per the business rule. On relational databases, we run extensive SQL queries on huge volumes (terabytes) of data to identify the data anomalies.
- Data Checksum – Source to Target Counts
- Source to Target data testing
- Target to Source data testing
Business Intelligence Reports
We test reports generated by Cognos, Micro strategy, Tableau, SSRS, SAS, Crystal Reports, Pentaho, SAP BO and so on, for accuracy, hierarchy, granularity, security, and performance.
We at Cigniti Technologies understand how a poorly written SQL query can take a toll on databases and degrade its performance. Keeping that in mind, we analyze queries, tune them to achieve accurate results with less response time and propose ideas to eliminate bottlenecks in database performance.
We have helped our clients in –
- Maintaining and managing the data warehouses/DataMarts throughout their operational cycle and
- Deploying automated data warehouse/DataMart testing processes to reduce manual testing efforts.
Cigniti Technologies takes pride in stating that it offers in-depth ETL testing services to its clients. Cigniti’s ETL Testing processes and frameworks are designed to guarantee high-speed and accurate results, with the ability of reusing indexes for its components as high as 70%. Cigniti works with the client’s team to understand, analyze, plan, design, execute, document, and roll out a future ready test strategy that saves time and ensures a faster time to market.
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