Framework for AWS S3 Data Lake Validation
Overcome the Limitations of a Rules-Based Approach of the Likes of Deequ, Griffin, and Great Expectations
With the accelerating adoption of AWS S3 as the data lake of choice, the need for autonomously validating data has become critical.
While solutions like Deequ, Griffin, and Great Expectations provide the ability to validate AWS S3 data, these solutions rely on a rule-based approaches that are not scalable for 100’s of data assets and often prone to rules coverage issues
Deep Dive on Data Quality Automation- 3 Tools to Consider
Eckerson Group Report, May 2022
This report profiles three tools that each take a different approach to reduce the burden of ensuring data quality. It gives data leaders context to better understand the strategies for data quality automation and to choose the approach best suited to the requirements of their organizations.
Read this report to learn:
- The components of data quality
- How companies can build trust in their data
- Why automated data quality tools are the answer to ensuring reliability at scale