A Practical Guide for Data Centric Testing Automated ETL Testing - iCEDQ

Practical Guide for Data Centric Testing | Blog

Information is the oil of the 21st century, and analytics is the combustion engine”- Peter Sondergaard, Senior Vice President, Gartner
Big Data and Business Intelligence are becoming an increasingly important source of statistical information which is used as a vital part of the critical decision-making process of all businesses. Bernard Marr, in his article titled “4 Ways Big Data Will Change Every Business” reiterates the industry-belief that “big data and its implications will affect every single business -from Fortune 500 enterprises to mom and pop companies – and change how we do business, inside and out.

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Overcome Data Testing Challenges Featured Image - iCEDQ

Overcome Data Testing Challenges | Blog

Today all decisions in an organization are being made on the data available to them. Hence it has become critical to ensure that the data is free of any issues or defects. The way to ensure that there are no data issues and it is fit for the business, is to test, validate and compare it regularly.

Some of these organizations are either testing the data manually or not testing at all. We all are aware of the issues and challenges of testing anything manually but data testing has its own set of unique challenges on top of that. Below are some of the data testing challenges every organization encounters. The challenges mentioned below are for data testing which translates into Big Data Testing, ETL Testing, Data Migration Testing and few others.

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Agile Data Warehouse Testing & Data Migration Testing Featured - iCEDQ

Agile DW Testing & Data Migration Testing | Blog

Development of a data warehouse, ETL, data migration or conversion always faces an ever-decreasing timeline. These implementations can take years to complete and users are not ready to wait that long.

The waterfall development model has been discarded in favor of the agile or development model. However, this has changed only one component of the Data Development Life Cycle. This does not mean that the quality of these processes or the data they produce is of high quality.

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