Abstract: This white paper aims to provide an in-depth analysis of ETL (Extract, Transform, Load) testing, comparing the two primary testing methodologies: white box testing and black box testing. The paper discusses the key differences between the two approaches, their advantages and disadvantages, and provides guidance on the appropriate application of each methodology in various… Continue reading ETL Testing – White Vs Black Box Testing
In any data pipeline testing activity, ETL plays a quintessential role. It essentially ensures the data pipeline from heterogeneous sources to the data warehouses is free of bad data, with strict adherence to transfer rules. For years, ETL testing was done manually, which is labor-intensive and made the entire process very error-prone. However, organizations have… Continue reading Go to Market Faster with Automated ETL Testing
The fast-paced digital revolution has compelled companies to switch to digital work setups. However, switching to digital is not the only solution to this cut-throat competition. Companies now need to transfer their data from legacy systems to the cloud. Since the data is scattered and separated in silos, moving it to the cloud system is… Continue reading 5 Most Common Challenges in Data Migration Testing
ETL is a process that gathers and transforms various kinds of data. Once the data transformation is over, ETL sends the data over to the data warehouse. With ETL, you can move multiple pieces of data all across different sources, locations, and evaluation tools. It is why ETL has become a force to reckon with… Continue reading Difference between ETL and Database testing
Data profiling is used to get the details about data from an existing data source and collect the statistics or summaries. This activity helps in: a. Find out the possible purposes of data. b. Accurately tag data for discoverability c. Know the expectations on quality d. Know the expectations and challenges of joining the data… Continue reading Data fit to purpose comparative analysis of data
This is a real success story and that’s how you migrate from money guzzlers to open-source money saver stack..g at the results they want to achieve. They made incremental changes in the code to arrive at the same result as the “tools” were producing. Once you compare the results coming in parallel for a month,… Continue reading How a successful migration to open source stack happens?
We worked on a Medical Records Analytics project and processed a monthly batch of data that arrived as CCDA files, very complex XMLs. You don’t want to analyze these using traditional excel because even parsing the file format would take a proprietary parser. We used many complex parsing techniques in java and stored the cleansed,… Continue reading How we saved a ton of effort on data pipe code migration!
The data validation process ensures that imported and processed data is accurate and of high quality. It’s also a type of data purification. Validating your data ensures that it is unique and falls within the expected set of variables. ETL (Extract, Transform, and Load) procedures include data validation as an aspect of the data transmission… Continue reading What is data validation, and why is it important?
Since various organizations have begun to use ETL to combine and relocate data, ETL testing needs to be carried out to confirm the authenticity of the process. However, ETL testing can prove difficult sometimes because of the sheer amount of data that it works with. Moreover, its diverse characteristics always make other aspects of ETL… Continue reading What is etl testing? Know all about it
Data quality is a significant concern for companies and organizations of all sizes, and it’s only going to worsen as the digital economy grows. Data quality management tools help simplify data management by reducing manual tasks and improving consistency across your company. It also reduces costs, enhances analysis speed and customer engagement. Read to learn… Continue reading Why do you need data quality automation?
