“When an ETL process can go wrong, it would go wrong” – Murphy on Data Integration.
It is a reality that ETL processes breakdown regularly unless constantly maintained, leaving developers to put together the broken pieces again and again Of course, that costs you precious man hours that could have been used to add value in more important areas of the enterprise. .
Let’s take a scenario. You are done setting up the dataflow. The data will load from the data mart to your designated data warehouse. But, you decide not to test your ETL extraction process because it’s a simple migration of data from point A to point B. Should work out as planned right? So, you decide to neglect it for the time being. WRONG MOVE!
A few weeks after you’ve built the ETL pipeline, your boss calls you to ask why this month’s sales figures are so overstated when compared to the established trend. It’s only after you take a look at the data that you realise you’ve been picking up duplicate datasets from your CRM the whole time.
Now, you’ve got your manager and the entire sales team breathing down your neck! You are in a deep mess.
But guess what? You can avoid all this hassle, by simply running ETL testing tools in advance before the actual process takes place.
Testing the ETL process flow ensures that the data being moved from the source is not only accurate but also complete. In a way, it helps you verify that the data you are trying to load to the warehouse for BI or product insights is actually the right data. Ultimately, that means it can form a reliable foundation for smarter business decisions both within and outside of your organization.
ETL Migration Checklist Varies for Each process
Just like in any other business process, ETL does not follow a one-size-fits-all approach. So, your ETL extraction process for acquiring sales data may not be optimal for acquiring marketing reports. Even if it is, you will have to add more transformations, separate certain values, and remove sales-focused data to make it more applicable for the marketing function. .
Sometimes you may even have to create custom testing protocols for your ETL processes depending on the nature of data models you are dealing with.
ETL Testing Checklist: Stages for a Perfect ETL Job
To help you understand the ETL testing in detail, we have segmented it into different stages. Consider each stage as a step that you will have to go through to make sure that the ETL testing process works according to your expectations and help you make the most of your ETL job.
Set an Objective
The first step is always to set an objective about what you want to accomplish with your ETL job. Remember, it’s always better to connect the dots moving backwards, then to come up with a process completely from scratch.
Let’s say you want to acquire product data on pricing and how it has affected user purchase behaviour at your stores. This is a logical ETL model. You now know what you want to extract – which in this case is information on products and their prices and the order volume of those products. Your foreign key for the above example will be the product ID.
In a similar way, each ETL job will have a different set of objectives. Understanding from the start how the job will progress, will help you make it more efficient, error-free, and guarantee a usable output for your decision-makers.
Data Preparation & Cleansing
Now that you have an objective in mind, the next step is to clean the data that you want to load. Let’s continue the same example we discussed above. We will require the information from three different tables. The first two tables will provide us the product names and their prices. We will get this data from our inventory data mart. The last table will include order ID and product ID, and we will get it from our sales data mart.
So, we will design a data model where the data is acquired from both sources and then transformed and joined together into a single table that we can use for insights.
Now let’s assume that the data in the inventory data mart is available in Excel sheets and the sales data is in barcode format. This barcode data is either in EAN or UPC format. If the data is already separated, good for you. A few join transformations will do the job. Otherwise, you will have to first add joiners to find out the actual number of orders, create a separate data for order volume and product IDs and then extract it.
Now your data is cleansed and prepared for the final job.
But, let’s not forget the duplicates that can mess up your ETL job. So, the next thing you need to check is for duplicate errors. You can fix that by adding another transformation and then applying a quality rule to it to ensure that irregular entries are not passed through to your reporting.
A simple ETL migration checklist about what you have to do for data preparation & cleansing:
- Prevent data loss. Always keep a backup
- Check data for compatibility, consistency, and accuracy
- Ensure de-duplicacy and completeness
Ensure Quality Rules are Met
Finally, the last step is to make sure that all the six quality rules of data integration are met. The best way to ensure that is by testing the data model you just created. This will bring to front any errors in your process. You can then remove them by readjusting the model or adding more transformations.
ETL Testing Checklist: How the Process Flows?
Based on the stages we described above, here is the basic structure of an ETL process flow for data validation.
Creating a Data Model: So, first of all you will need to create a data model that identifies the elements involved in your dataflow pipeline, how they relate to each other, and the mappings that will be formed between them.
Analyzing the Data Sources: Ensure that the data from sources is in structured format. If there are more than one sources, make sure that every source is accessible.
Running Test Cases: Next, test the ETL model you just created. The trial will help you know the total time the job takes to complete and if there were any complexities during the process.
Measure and Report Outcome [Optional]: Finally, you can create a report where you add all your takeaways from this planning phase including the complete process, the data models, the sources and destinations, and the errors and their solutions. This will help your ETL team in carrying out future projects of similar nature with much more ease.
Types of ETL Testing Processes
Here are certain types of ETL process tests that you can perform on your selected data sets.
Metadata Testing: Metadata test is done to ensure that the selected data table complies with the data model and application specifications.
Data Completeness Test: The data completeness test ensures that data conforms with data completeness checks. It includes compare and validate, count, and aggregate tests.
Data Quality Test: Quality checks ensure that data ported to the new system passes all data quality rules.
ETL Performance Test: ETL performance tests are run to reduce ETL process time and improve throughput.
ETL Integration Test: Data integrations tests such as unit and component tests are carried out to ensure that the source and destination systems are properly integrated with the ETL tool. It also checks for firewalls, proxies, and APIs.
Making ETL Process Testing Easy
While the ETL testing is a cumbersome process, you can improve it by using self-service ETL tools. These data integration tools can help you create data models through drag-and-drop features. You can use them to extract, transform, and load data, all in a single go; or create workflows to completely automate your ETL processes.
One data integration tool that can help you improve your ETL processes is Astera Centerprise. You can use it to optimize your ETL migration checklist, create proper data maps and automate jobs, all using a code-free environment. Download the Centerprise trial version today and experience the platform for yourself.