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Announcements

Meet Recce at Coalesce 2024 and The Data Renegade Happy Hour

The Recce team will be joining Coalesce 2024 in Las Vegas! Meet our founder, CL Kao, and product manager, Karen Hsieh, who has also been hosting the Taipei dbt meetups. As a company focused on helping data teams prevent bad merges and improve data quality, we believe Coalesce is the perfect venue to connect with fellow data professionals, share insights, and gain fresh perspectives.

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We are attending Coalesce 2024

At Recce, our mission is to transform the data PR review process, ensuring that data pipelines not only run smoothly but also deliver accurate, validated results. We believe that data should be correct, collaborative, and continuously improved. Coalesce 2024 offers an ideal platform for these crucial conversations, gathering experts across the field to discuss the future of data management. Whether it’s gaining new insights into best practices or forging valuable partnerships, Coalesce is where we aim to make an impact.

From DevOps to DataOps: A Fireside Chat on Practical Strategies for Effective Data Productivity

Top priorities for data-driven organizations are data productivity, cost reduction, and error prevention. The four strategies to improve DataOps are:

  1. start with small, manageable improvements,
  2. follow a clear blueprint,
  3. conduct regular data reviews, and
  4. gradually introduce best practices across the team.

In a recent fireside chat, CL Kao, founder of Recce, and Noel Gomez, co-founder of Datacoves, shared their combined experience of over two decades in the data and software industry. They discussed practical strategies to tackle these challenges, the evolution from DevOps to DataOps, and the need for companies to focus on data quality to avoid costly mistakes.

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Data Productivity - Beyonig DevOps & dbt

Next-Level Data Validation Toolkit for dbt Data Projects — Introducing Recce

Build the ultimate PR comment to validate your data modeling changes
Recce: Data Validation Toolkit for dbt

Validating data modeling changes and reviewing pull requests for dbt projects can be a challenging task. The difficulty of performing a proper ‘code review’ for data projects, due to both the code and data needing review, means the data validation stage is often omitted, poorly implemented, or drastically slows down time-to-merge for your time sensitive data updates.

How can you maintain data best practices, but speed up the validation and review process?