Mostly of the ETL testing is done by using SQL scripts and assembles the data in spreadsheets. Thus ETL testing is very slow and time-consuming, error-prone, which is performed on sample data.
Technical Challenge in Manual ETL Testing
ETL test team writes SQL queries to test data in a warehouse system and they need to execute them manually by using a SQL editor. After that place the data into an Excel spreadsheet and compare them manually. This process is inefficient, time-consuming, and resource intensive.
There are several tools which are available in the market to automate this process. The most common testing tools of ETL are QuerySurge and Informatica Data Validation.
It is a data testing solution which is designed for testing ETL process, Big Data, and the Data Warehouses. It can automate the all over process into your DevOps strategy.
The main features of QuerySurge are as discussed below –
- It allows to the user for scheduling tests to run any date or time, immediately, and automatically after an event ends.
- It can compare data from data stores and source files to the target Data Warehouse or Big Data store.
- It has Query Wizards for making test Query Pairs fast and simply without the user having to write any SQL.
- It has a Design Library with reusable Query Snippets and can create custom Query Pairs as well.
- It can compare millions of rows and columns of data in minutes.
- It can make view updates, informative reports, and auto-email results to the team.
For automate the whole process, theETL tool should start Query Surge through command line API after the ETL software completes its load process.
QuerySurge will run automatically, unattended, executing the entire tests and then emailing to each one the team member with results.
Informatica Data Validation – It provides an ETL testing tool also helps to automate the process of ETL testing in the production and development environment. It allows delivering completely, repeatable and auditable test coverage in few times. It has no programming skills.
ETL Testing helps in minimizing the time and cost to perform the testing. This process improves the quality of data which are loaded to the target system which produces high quality dashboards and reports for end-users.
There are few best practices which can be followed for ETL Testing are discussed below −
Examine the Data
It is very important to examine the data for understanding requirements to set up a correct data model. Spending time to understand the supplies and having a accurate data model for the target system which can decrease the ETL challenges. It is also important to note that the data quality, the study the source systems, and build right data validation rules for ETL modules. An ETL strategy should be formulated which is based on the data structure of the source and the target systems.
Find a Compatible ETL Tool
It is one of the common ETL best practices for selecting a tool which is most compatible with the target systems and the source. The capability of ETL tool is to produce SQL scripts for the target system and the source which can decrease the time of processing and resources. It allows one for processing transformation anywhere inside the environment which is most suitable.
Fix Bad Data in the Source System
End-users are generally conscious of data issues, but they have no knowledge how to fix them. It is important to search these errors and to correct them before they reach the ETL system. A general way to solve this is at the ETL execution time and the good thing is to search the errors in the source system and take steps to correct them at the source system level.
Monitor ETL Jobs
There is another best practice in implementation of ETL is monitoring, scheduling, and auditing of ETL jobs to make sure that the loads are performed as per expectation.
It is one of the best practices to ensure the offered ETL solution is scalable. During implementation, one needs to make sure that ETL solution is scalable with the business requirement and its potential growth in future.
Integrate Incremental Data
The d data warehouse tables are better in size and it is not possible to revive them throughout each ETL cycle. Incremental loads make sure that only records are changed as the last update are brought into the ETL process and it puts a enormous impact on the scalability and the time taken to refresh the system.
Generally the source systems don’t have time stamps or a primary key for identifying the changes easily. Such issues can be very costly, if identified at the later stages of the project. It is one of the ETL best practices are to cover such aspects in the initial source system study. This knowledge helps for the team of ETL for identifying changed data capture problems and determine the most suitable strategy.