All About ETL Testing, Data Migration & Data Warehouse Testing

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AML Software Implementation & Production Monitoring with iCEDQ DataOps Platform Featured

AML Software Implementation & Monitoring | Blog

AML software is a downstream system that consumes data from multiple sources. AML software analyzes data based on compliance models.  This results in suspicious activity reports. Further, they also monitor data for regulations such as FATCA, trade restrictions, sanctions, and watch list.

However, if the incoming data is not good then these results cannot be relied upon. It is basically at the mercy of upstream systems.

iCEDQ is an in-memory data audit rules engine.  It sits between the data sources and downstream systems such as ALM.  It can validate and reconciliation data coming from multiple data sources. Thus, managing data issues before it affect the downstream system.

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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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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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What is BI Testing and the Importance of BI Report Testing - iCEDQ

BI Testing & Importance of BI Report Testing | Blog

Reports and dashboards are extensively used to make business decisions. These decisions later form the basis for the company’s growth and success. If BI reports are wrong, then decisions taken based on these reports will also be wrong. Inaccurate reports affect the organization’s credibility and also exposes them to compliance and legal issues. It can also lead to hefty fines that organizations can not afford to ignore BI Testing.

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Migrating Database to Redshift, Snowflake, Azure DW and Test with iCEDQ

Migrating Database to Redshift, Snowflake, Azure DW | Blog

The complexity of migration from a database to a new platform and time for implementing it has many challenges; Lack of strategy, incorrect assumptions, lack of tools, and complexity of the environment to name a few.

We at iCEDQ are not involved in choosing your new database platform. Choosing a database platform is beyond the scope of this article. We do want to help you by providing a checklist for your data migration effort and a data migration testing strategy.

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DataOps Implementation Guide - iCEDQ

DataOps Implementation Guide | Blog

The team is usually divided into development, QA, operations and business users. In almost all Data Integration projects, development teams try to build and test ETL processes, reports as fast as possible and throw the code across the wall to the operations teams and business users. However, when the data issues start appearing in production, business users become unhappy.  They point fingers at Operations people, who in turn point fingers at QA people.  The QA group then puts the blame on the development teams.

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