Recce

Recce Sandbox is now live to try out! Iterate on a model without materializing or modifying your dbt project... To use Sandbox, Recce can now be launched in any dbt project, no need to prep 2 environments to diff, just: 💻 pip install -U recce && recce server and you can start playing with model code and previewing data impact in the Sandbox, risk-free. Read more about it here: https://lnkd.in/g45S9bPj #dataengineering #dbt #data #analytics #analyticsengineering #impactassessment

"as a code approver, it is quite cumbersome to open a PR, read the notes for intended changes, and then dig into the data to verify that the results match the intended change." Exactly why in Recce we use the Checklist to track diff results. The PR author provides the proof (check results and annotation together) for the PR reviewer or stakeholder to sign off. No need to do the digging themselves, but the option is there, and the complete data environment can be easily replicated with one file.

Risk-free data impact assessment with model diff preview right inside Recce. Now you can have the knowledge to open a PR with the confidence you're not going to merge bad data into production: In this video Dave Flynn shows how to use the new experimental change-preview feature of Recce to see data impact by diffing the data with existing model SQL - without modifying your dbt project! This feature is available in Recce to try out now. Head to the GitHub to try out DataRecce/Recce (link in comments) and don't forget to give us a star ⭐ #dbt #analytics #dataengineering #dataimpact #impactassessment #analyticsengineering #dontbreakprod

Best practices can make or break your the success of your dbt data pipeline. dbt is an incredible tool, but using it effectively requires more than just running models—it’s about applying best practices that drive scalability, collaboration, and efficiency. Check out our latest article about dbt best practices and why they work. ⬇️ #DataEngineering #dbt #DataOps #DataInfrastructure

😵 Ever found yourself asking this while working on data changes? When juggling custom queries and spot checks, it’s easy to lose track, especially when interruptions or other tasks get in the way. “I just wrote this custom query yesterday… but where did I save it?” “I’m sure I checked this already… but what were the results?” Then, when it’s time to prepare for reviews, you’re stuck repeating work and scrambling for evidence. 💡That’s why we built Run History: to log every check, preserve progress, and help you prepare for reviews with confidence—so you can focus on what matters. 🔗 Read the full story here to see how it works. https://lnkd.in/g_ESX4ag

Data preparation is an essential step for any data project. How do you know which is the best tool for the job? Recce's own Even Wei shares how he prepped the data for the open-source TodoFEC-dbt project and made the data available to all. TodoFEC-dbt uses dbt to model US campaign finance data. Even explains the challenges he overcame and the tools he used during data prep: - Parquet - Polars - DuckDB Read on to find out how: https://lnkd.in/gp9Dmx4W Next up, data modeling in dbt Labs! #opensource #data #analytics #datascience #dataprojects #dbt

Live preview dbt data model changes without needing to rebuild the model, sound interesting? You can do it right now in Recce, here’s how… We’re working on a feature that will enable you to preview the data from your model changes with a before/after diff - without the need to rebuild your dbt project. 📺 In the video below, Dave Flynn shows how you can do this right now in Recce. We’re currently working on making this workflow smoother, but the value of the feature is clear: https://lnkd.in/gNgQwHxx 👀 Please take a look and let us know if this would fit into your dbt data modeling workflow and, if not, let us know why. 🏗️ The foundation of Recce is built on improving and streamlining the workflow for analytics engineers and PR reviewers. Let's make data productive together! Get started with Recce in 5 minutes: https://lnkd.in/gGgi-R5F #dbt #dataengineering #dataworkflow #analytics #analyticsengineering

Get fast insight into data impact in dbt projects with Recce multi-node impact analysis. Select groups of nodes in the Lineage Diff either by dbt selector or select them yourself, and the perform: - Row Count diffs - Schema diffs - Value diffs (percentage match per column) on ALL selected nodes at once! ✅ Add the data checks to your Recce Checklist for peer review. ✅ Re-run the checks later. ✅ Automate the same checks in CI to cover your critical models. It’s just one of the ways that Recce helps improve your dbt development workflow, and makes the life of your PR reviewer easier! Try it right now in the online demo (no login required): https://lnkd.in/gaWMUb8R Like what you see? Give us a star and support open-source data tools: https://lnkd.in/gsZDPUVP #data #dbt #dataengineering #analyticsengineering #bestpractices #impactanalysis #impactassessment #sql #analytics #dataworkflow

Did you know Recce isn’t just for validating your work in isolation? You can actually share your complete data validation environment with your team - Validate your work, create your checklist, and then send it to your PR reviewer. They can recreate your Recce environment with one command: 💲 recce server --review pr1_recce.json Take it to the next level with Recce Cloud integration and there’s no need to even share the file! ☁️ Checklists are automatically synced 🌥️ PR merging can be blocked unless Recce checks are approved 🌤️ Recce can be opened online, no need to install locally You have a reproducible data pipeline, now you can get a reproducible data validation environment! ♻️ Read about how to do it here: https://lnkd.in/ghtqSxEK #dataengineering #bestpractices #analyticsengineering #dataops #data #analytics

Data pros, being asked to lead GenAI projects? 🚀 Explore why your existing data engineering skills might outshine purpose-built tools for evaluation. https://lnkd.in/gx9WbqG6 This article dives into treating GenAI as a data problem—offering flexibility, control, and seamless integration. Get ahead in the GenAI game! Shout out to Kent Huang for his work and research on this article. #DataEngineering #GenAI #evaluation #data