Data scrubbing helps improve data quality by identifying and fixing errors, duplicates, and outdated information. It ensures accurate, consistent datasets for better analytics and decision-making.

With proper validation, standardization, and regular updates, businesses can maintain clean data, improve insights, and streamline operations.

Read more: https://www.habiledata.com/blog/data-scrubbing-guide/

#DataScrubbing #DataCleansing #DataQuality #DataValidation

Reliable business insights via b2b data aggregation

Accurate business data supports analytics, outreach, and planning. Data aggregation for b2b companies centralizes information from diverse sources and validates accuracy through structured checks. Continuous enrichment ensures datasets remain reliable and up to date.

Know more: https://www.hitechdigital.com/b2b-data-aggregation

#B2BDataAggregation #DataAggregationServices #DataEnrichment #DataCleansing #DataValidation #B2BDataSolutions #DataQualityManagement

marmelab web developer Thiery Michel shares in this article his use of PostgreSQL features that allow us to draw certain data validation logic away from the application layer, and into the database. The proposed solutions in some instances can be more elegant than a purely application layer data validation approach.

"9 Advanced PostgreSQL Features I Wish I Knew Sooner"

https://marmelab.com/blog/2026/02/23/do-you-know-psql.html

#programming #sql #postgresql #database #datavalidation

Code changes have AI review tools. Data changes don't... until now.

Our own Kent Chen wrote about how the team built a multi-agent system with Claude Agent SDK and MCP that reviews data changes in every dbt PR. Orchestrator + two specialist agents using 6 Recce MCP tools for lineage diffs, schema diffs, row counts, and custom queries.

AI Data Review lands in the PR automatically. No manual queries.

https://blog.reccehq.com/designing-reliable-ai-agents-for-dbt-data-reviews

#dbt #DataEngineering #DataValidation #AI #MCP

Khi làm dự án nhỏ tổng hợp dữ liệu từ API, mình học được:
- Đường dẫn thành công thì dễ, mọi thứ khác thì không
- Những giả định nhỏ về phản hồi API gây ra hầu hết sự cố
- Kiểm tra dữ liệu quan trọng hơn hiệu năng lúc bắt đầu
Dữ liệu thực tế khác xa tutorial!

Bạn từng làm dự án liên quan dữ liệu, điều gì khiến bạn bất ngờ?
#sideproject #apidevelopment #datavalidation #lessonslearned #lậptrình #dựánphụ #xửlýdữliệu

https://www.reddit.com/r/SideProject/comments/1qipi23/what_surprised_