February 12, 2019
ETL (Extract/Transform/Load) is a process that extracts data from source systems, transforms the information into a consistent data type, then loads the data into a single repository.
ETL testing leads to the process of authenticating, validating, and qualifying information while restricting duplicate records and data loss. It ensures that the exchange of information from independent sources to the central data warehouse occurs with stringent adherence to transformation rules and is in agreement with all validity tests. It varies from data reconciliation used in database testing & in that ETL testing is utilized to data warehouse systems and used to gather pertinent information for business intelligence and analytics.
ETL testing tools enhance IT efficiency and streamline the process of retrieving data from big data to expand insights. The tool itself contains strategies and guidelines for extricating and handling information, excluding the demand for traditional programming approaches that are work concentrated and expensive. Hence, ETL Testing is one of the most essential part of big data testing. Another advantage is that ETL testing tools have built-in compatibility with cloud data repositories, CRM and ERP platforms like Salesforce, Amazon Web Services, Oracle, Google Chrome, NetSuite and many more...
ETL Testing Process
Powerful ETL testing identifies problems with the source information from the start before it is stacked to the data repository as well as discrepancies or uncertainties in business rules designed to manage data transformation and integration. The process can be divided into eight stages.
1. Recognize Business Requirements: Based on customer expectations, you can create the data model, establish the business flow, and can evaluate reporting needs. It’s essential to commence here so the scope of the project is precisely determined, documented, and recognized completely by software testers.
2. Verify Data Sources: Implement a data count check and confirm that the table and column data type fulfills the specifications of the data model. Ensure the check keys are set up and exclude duplicate data. If not done accurately, the entire report could be incorrect or deceptive.
3. Design Test Cases and Test Data: Design ETL mapping scenarios, generate SQL scripts and describe multiple rules. It is imperative to confirm the mapping document as to guarantee it contains all of the valid information.
4. Data Extraction: Based on the market requirements, implement ETL tests. Recognize kinds of bugs or defects confronted amid testing and make a report. It is essential to identify and represent any flaws, report, get the bug, solve, and close bug report – before moving ahead.
5. Data transformation Testing: It strengthens the mapping of objects in the source and the target systems. It also includes examining the functionality of data in the target system.
6. Summarizing Reports: It involves generating reports for data validation. The report summarizing means the ultimate output of any data warehouse system. Reports are tested based on their layout, options, filter and calculated values with the export functionality.
7. Test Closure: File test closure.
Leverage ETL Testing in order to Improve Efficiency and Rapidly Increase Test Coverage.
Challenges of ETL Testing
To recognize the difficulties early in the ETL process can prevent expensive delays and hindrances. Designing a source-to-target mapping document and building up clear business requirements from the inception is necessary. Constant and multiple revisions to requirements expecting ETL testers to improve the logic in scripts can substantially ease back the progress. ETL testers need to have a precise estimation of the data transformation requirements as to have a clear understanding of end-user requirements. Some of the challenges to look out from the earliest point includes:
Future of ETL Testing
Business organizations that depend on in-house testing tools and hand-coded scripts lose productivity and the capability to associate with the present advancing ETL cloud technologies. Rapid moving agile, DevOps teams that produce various software application updates on a regular basis using automated, continuous deployment methods are nowadays turning the standard. As DevOps extends to cloud-based data processes and environments, there is a demand for automated data integration with ETL testing tools that can produce substantial quantities of data independently without looking for human interference in real-time. As we are considering Agile, DevOps, AI and Cloud Technologies as one of the top trends in software testing industry, it is also playing an equivalent role in ETL testing industry.
The waterfall approach i.e. you can recognize a problem in the data stream, set, test pattern, load to the data warehouse, and examine is being substituted with cloud-native agile solutions. Nowadays, data management cloud architectures and AI ‘smart’ data integration associates are developing new trends. To see an illustration of machine learning, determine how to teach the system human decision-making and produce a classification model based on that learning.
TestingWhiz takes dignity in affirming that it extends an in-depth ETL testing services to its customers. Their ETL Testing frameworks and processes are intended to ensure quick and precise outcomes, with the capacity to reuse indexes for its components as high as 60%. TestingWhiz works with the client’s team to learn, analyze, prepare, design and implement the document, and reveal a future-ready test strategy that releases time and guarantees a quicker time to market.