When people hear about iceDQ, the first reaction is… “Hey, I already have a data quality tool! Why do I need iceDQ?” In this article let’s contrast iceDQ with any other data quality tools available in the market.
What is iceDQ?
iceDQ is a Quality Assurance and Test Automation platform for data-centric projects and processes such as data warehouse, CRM, data migration & conversion, ETL. It certifies the ETL processes or migration by effective ETL Testing and Data Migration Testing. The product can be further used for monitoring the data processes in production. The product emphasizes mainly process quality.
The major difference between iceDQ & other Data Quality tools is the purpose they serve. iceDQ is a test automation platform for process quality whereas other Data Quality tools are a combination of data profiling & fixing correction tool used in production.
Refer to our page to learn about data quality concepts and key dimensions.
A breakdown of all the features is given below:
Items |
iceDQ |
Data Quality Tools |
|---|---|---|
| Type of Tool | Quality Assurance Tool iceDQ is a QA tool, purpose-built for testing data-centric projects and systems such as ETL.Other QA tools: HP Quality Center, QTP, selenium, etc. |
Data Quality Tool It is a data profiling & data correction tool.
Other tools in the class: Trillium, Informatica |
| Environment/Phase in which the tool is used | Non-production environment (Dev/QA Phase) to test, validate and reconcile the data between/ across files and databases. |
Production phase to fix the data on day to day basis in conjunction with the ETL tool. |
| Use cases of the tool | 1. ETL Development/Testing
2. Data Migration & System Conversion Testing. |
1. Data profiling in the early stages of the project.
2. Data fixing in a production environment. |
Features
Items |
iceDQ |
Data Quality Tools |
|---|---|---|
| Requirements and Test Case Management | Yes.
The platform supports requirements and test cases management as well as linking them to physical rules (tests) to define the success or the failure of the ETL Process. |
No |
| Data Profiling | No | Yes.
The tool discovers the patterns in data, their values, and relationships and reports any data anomalies. Used for analysis and understanding of data. |
| ETL process Testing | Yes.
The tool support creation of rules (tests) that can validate/ test an ETL process. This is based on the principle “Incoming data + Transformation rule = Output Data” |
No |
| Data Correction and Standardization | No | Yes.
The tool is used to run the “cleanup” task against the last data sets. It uses different rules to recognize data issues and apply the correct rules based on context to change/ update/ delete the |
| Data Match and Merge | No | Yes.
The tool allows to identify matching records and optionally link them or merge matched records based on survivorship rules. |
| Data Validation Testing | Yes.
The tool allows to identify matching records and optionally link them or merge matched records based on survivorship rules. |
No |
| Data Migration Testing | Yes.
The tool allows to identify matching records and optionally link them or merge matched records based |
No |
Sandesh Gawande
CTO and Founder at iceDQ.
First to introduce automated data testing. Advocate for data reliability engineering.