Table of Contents
Agile Testing
- A Practical Guide for Data Centric Testing: Automated ETL Testing
- Overcome Data Testing Challenges
- Agile Data Warehouse Testing & Data Migration Testing
BI Testing
Data Integration
Data Management
Data Migration Testing
- Migrating Database to Redshift, Snowflake, Azure DW and Test with iCEDQ
- Data Migration Testing Techniques to Migrate Data Successfully
- The Data Migration Process & the Potential Risks
Data Quality Tools
Data Warehouse
- ETL Testing - Unit Testing vs. Quality Assurance for Data Warehouse
- ETL Development & ETL Testing – a Pipeline for Data Warehouse Testing
- ETL Testing and Data Quality Governance Software - The Missing Link
DataOps
- DataOps Implementation Guide
- AML Software Implementation & Production Monitoring with iCEDQ DataOps Platform
- What Are The Challenges Of A Data Factory
ETL Process
ETL Testing
- 3 Reasons Why You Need to Perform ETL Testing
- ETL Testing - Unit Testing vs. Quality Assurance for Data Warehouse
- ETL Testing Vs. Application Testing - The Fundamental Difference
Quality Assurance
Data has become critical to the business. Hence, enterprises are investing time, money and resources in data-centric systems such as data warehouse, MDM, CRM & migration projects. However, all research done by independent agencies indicates that
- There is such a high failure/delays in implementations of data-centric projects
- Users still don’t trust data coming from data warehouses
There are many reasons for the failure/ delay, but the main reason that comes to mind is the mismanagement of data risk and the lack of experience in managing data-centric projects and systems. When was the last time you saw management and architects identify data risk and its mitigation strategy?
If you need further proof, just check the company’s product portfolio for the data-centric system. You will find the two critical components that mitigate data risk are missing.
- ETL testing software, data testing software, data warehouse testing software, and data migration testing… call it by any name
- Data monitoring and data quality governance software
The only way to mitigate the data risk is by having an ETL testing software and data quality governance in production (ETL process will define your data quality). But people are not even aware of it. Hence the chances of success in development or in production are bleak.
Ask yourself, what do you have in place to test ETL processes? And how do you monitor your production data?
