ETL Testing Training in Hyderabad

Stages ETL testing takes you into -

Effective ETL testing detects problems with the source data early on—before it is loaded to the data repository — as well as inconsistencies or ambiguities in business rules intended to guide data transformation and integration. The process can be broken down into eight stages.

• Identify business requirements — Design the data model, define business flow, and assess reporting needs based on client expectations. It’s important to start here so the scope of the project is clearly defined, documented, and understood fully by testers.
• Validate data sources — Perform a data count check and verify that the table and column data type meets specifications of the data model. Make sure check keys are in place and remove duplicate data. If not done correctly, the aggregate report could be inaccurate or misleading.
• Design test cases — Design ETL mapping scenarios, create SQL scripts, and define transformational rules. It is important to validate the mapping document as well, to ensure it contains all of the information.
• Extract data from source systems — Execute ETL tests per business requirement. Identify types of bugs or defects encountered during testing and make a report. It is important to detect and reproduce any defects, report, fix the bug, resolve, and close bug report — before continuing to Step 5.
• Apply transformation logic — Ensure data is transformed to match schema of target data warehouse. Check data threshold, alignment, and validate data flow. This ensures the data type matches the mapping document for each column and table.
• Load data into target warehouse — Perform a record count check before and after data is moved from staging to the data warehouse. Confirm that invalid data is rejected and that the default values are accepted.
• Summary report — Verify layout, options, filters and export functionality of summary report. This report lets decision-makers/stakeholders know details and results of the testing process and if any step was not completed i.e. “out of scope” and why.
• Test Closure — File test closure.

A final step is for the ETL tester to test the tool, its functions, and the ETL system.

ETL testing tool making testing Easy

• ETL testing tools increase IT productivity and simplify the process of retrieving information from big data to gain insights. The tool itself contains procedures and rules for extracting and processing data, eliminating the need for traditional programming methods that are labor-intensive and expensive.
• Another benefit is that ETL testing tools have built-in compatibility with cloud data warehouse, ERP and CRM platforms such as Amazon Web Services, Salesforce, Oracle, Kinesis, Google Cloud Platform, NetSuite, and more.

Capabilities to look for when comparing ETL testing tools include:

• Graphical interface to simplify the design and development of ETL processes.
• Automatic code generation to speed development and reduce errors.
• Built-in data connectors that can access data stored in file format, a database, packaged application, or legacy system.
• Content management facilities that enable context switching for ETL development, testing and production environments.
• Sophisticated debugging tools that let you track data flows in real-time and reports on row-by-row behaviour.

Open source ETL testing

• ETL testing is a multi-level, data-centric process. It uses complex SQL queries to access, extract, transform and load millions of records contained in various source systems into a target data warehouse.
• ETL testing tools handle much of this workload for DevOps, eliminating the need for costly and time-intensive development of proprietary tools.
• Extensive ETL testing gives an enterprise confidence in the integrity of its big data and the business intelligence gained from that data, and lowers business risk. Talend Open Studio for Data Integration is an industry-leading leading open source ETL development and testing tool.
• Subscription-based Talend Data Integration includes the same ETL testing functionality as well as enterprise class continuous delivery mechanisms to facilitate teamwork, run ETL testing jobs on remote systems, and an audit tool for qualitative and quantitative ETL metrics.

The future of ETL testing: AI and the cloud

• Enterprise organizations that rely on hand-coded scripts and in-house tools lose efficiency and the ability to scale with today’s evolving ETL cloud technologies. Fast-paced, agile DevOps teams that churn out multiple software application updates daily—using automated, continuous deployment practices—is becoming the norm.
• As DevOps expands to cloud-based data environments and processes, there is a need for automated data integration with ETL testing tools that can process larger amounts of data autonomously — without need for human intervention — in real-time.
• The waterfall approach (identify a problem in data stream, fix, test schema, load to data warehouse, and analyze) is being replaced with cloud-native, agile solutions.
• Data management cloud architectures and AI ‘smart’ data integration assistants are emerging new trends.