What are the basics of ETL in testing?
ETL in testing helps organization migrate their historical data from various sources to a centralized platform. The ETL process is essential across industries for its ability to ingest data quickly and transform transactional data in an organized format for future access and analyzation. ETL is used to bring data from multiple different sources to a centralized database, typically a data warehouse. You must be wondering about the value-add of ETL testing. Well, ETL (extract, transform, load) helps in eliminating manual errors and inaccurate information, thereby easing the process of data transformation and data loading.
Sample ETL in healthcare helps in improving care and manage patient records in a more harmonized manner. Guess what, you can use ETL in testing across other industries too, like automotive manufacturing, telecommunications, life science, and financial services. It helps you make predictions with transformed data, understand sales patterns, scale marketing campaigns, and follow up on customer leads.
How ETL works?
Wondering how exactly ETL works? I am going to explain the entire process of how ETL functions. It provides the foundation for data analysis and machine learning. It cleanses and organizes data in a way that addresses specific business intelligence needs.
ETL is often used by organizations to-
Extract – It involves extracting data from the legacy systems. During extraction, raw data is exported from source location to staging area. Volumes of data can be extracted from different data sources, including –
- Legacy systems or existing databases- Sales and marketing applications
- CRM and ERP systems
- Mobile devices and apps
- Data storage platforms
- Analytics tools
- SQL or NoSQL servers
- Web pages and emails
Transform – In the staging area, raw data undergoes data processing. It involves standardizing and cleansing of data to improve data quality and establishing consistency. It improves data integrity and ensures that raw data reaches its new destination and is ready to use. Data transformation involves –
- Cleansing - Inconsistencies and missing values are resolved- Deduplication – Redundant data is excluded
- Verification – Unusable data is removed
- Sorting – Data is organized as per its type
- Other tasks – Any additional rules can be applied to improve data quality
Load – In this stage, data is moved from staging area to target database either at once (full load) or at scheduled intervals (incremental load).
Full loading – Data from the transformation assembly line goes into a new record in the data warehouse. It is useful for research purpose but can become difficult to maintain.Incremental loading – It compares the incoming data with what’s already available and produces additional records if new information is found.
Let’s dive deep to understand the stages of ETL testing.
What are the stages of ETL testing?
The process of ETL testing can be broken down into eight stages:
Identify business requirements – State the scope of the project and ensure that it is understood by the testers. Define business flow, design the data model, and assess reporting needs based on client’s expectations.
Validate data sources – Verify that the table and column data type meet the specifications of data model. Perform a data count check and ensure that check keys are in place. If data sources are not validated, it might create an inaccurate or misleading aggregate report.
Design test cases – Create an outline of ETL mapping scenarios, create SQL scripts, and define transformational rules. Validate the mapping document to ensure it contains all the data.
Extract data from source systems – Execute ETL tests as per business requirements. Identify bugs and defects during testing and generate a report.
Apply transformation logic – Ensure data is transformed to match the schema of target data warehouse. Check data alignment and validate data flow.
Move data into the selected warehouse – Track records before and after data is loaded from staging to the data warehouse. Confirm that default values are accepted, and invalid data is rejected.
Summary report - Verify the layout, options, and export functionality of the summary report. It provides all details to stakeholders about the testing process.
Test closure – File test closure.
These eight stages of ETL testing provide a consolidated view of historical data and help improve accuracy and productivity. Opting for the right ETL tool for your business as per its needs matters a great deal. Allow me to tell you about certain factors that will surely help you decide on the best ETL tool.
How to select the right ETL tool?
Do think about the following factors while choosing the right ETL tool for your business:
Use case – You need to find tools that can automate the entire data flow and can be used in a variety of different use cases.
Capabilities – An ETL tool should be flexible to read and write data regardless of whether it is on-premises or on cloud.
Data sources – The key consideration for choosing an ETL tool is the type of data source. Some organization may need to work on simple structured data; others may need to work on high-dimensionality, structured or unstructured data.
Integration – The integration factors for determining which ETL tool works best for a company are the scope and the frequency of the integration efforts.
Budget – An important consideration while choosing the ETL tool is budget. ETL tools requiring manual coding and data mapping have added costs. Choose ETL tools as per your business requirement and budget.
Business goals – Business needs are the most important considerations while selecting ETL tools.
Make sure that you consider the above-mentioned factors while choosing an ETL tool for your business. The most prominent ETL tools are MarkLogic, Oracle, Sybase, CloverETL, and Amazon RedShift. These are the instruments that do the heavy lifting of the data integration. Now that you are well acquainted with the concept of ETL in testing and how you can choose the best suited tool for your business, let’s dwell on its benefits too.
What are the benefits of ETL tools?
You must be pondering on why I am emphasising on ETL tools so much. Well, here are some benefits that I am sure you would like to avail-
- Make better business decisions based on data quality
- Eliminate unnecessary data and improve the quality of data
- Automate certain processes and reduce unessential tasks
- Enhance the legibility of data
With these basics you can easily get an overview of your historical data and make better decisions by analysing and computing the new reformed data. In my next blog I will present the types of ETL testing and the future of ETL.
If your organization has a revolutionary vision, and you’d like to explore more detailed possibilities of how ETL testing can add value to IT, feel free to write to us or visit us at Nitor Infotech.
