DataDrift

Data pipeline without issues is like bug-free software. It does not exist. As analytics practitioners, we know spending time investigating data issues is frustrating. That’s why we are happy to introduce lineage drill-down for faster and painless root cause analysis. 1. Add a monitor where your metric is computed. 2. Datadrift understand how your metric is computed and on which upstream tables it depends. 3. When an issue occurs, it pinpoints exactly which rows have been updated and introducing the change. Read more here → https://lnkd.in/dRMnRfAr

This week our Github repo crossed 200 stars. Huge thanks to our users, stargazers and friends 💚 We're just starting to build the open-source metric observability framework. Datadrift monitors your metrics, sends alerts when anomalies are detected and automates root cause analysis. The repo is at https://lnkd.in/e9PwY4uC Sammy & Lucas

Track changes of key datasets with git-like version control. Dataset version control provides an easy and intuitive way to: -> track historical changes -> monitor key metrics updates, like Executive reporting or financial reports -> investigate and troubleshoot data issues Read more here → https://lnkd.in/eS6J4zsr

Trust is a key asset for data teams. Without trust, data adoption remains low and stakeholders have hard time acting on metrics and insights. However, being pro-active on data issues is easier said than done. That’s why we are excited to introduce anomaly detection for your metrics. → Monitor your key metrics → Detect, understand and solve issues before stakeholders notice → Maintain trust Read more here → https://lnkd.in/epbeQrnw

That’s a wrap for 2023. And quite a lot happened since launching this Summer. Here's what’s been really special for us: - Well, launching our open-source metric observability framework - Being used by first users in production (thank you for your trust and valuable feedback 💚) - Getting started with content, just shy of 1M views across LinkedIn, Medium, and our blog Excited to see what 2024 has in stock!

Delivering high-quality data is hard. Data teams know this. They experience it everyday. Yet, it’s difficult to explain the specific reasons that make it so. 🎦 In this video, Lucas explains why data quality is hard. (and what to do about it) => https://lnkd.in/eF-iJe_W

📣 DriftDB, our open-source data observability & troubleshooting tool, gets an upgrade 📣 Lucas shares the features of the latest DriftDB version which include: 🔮 Detection of errors impacting key metrics in your warehouse or BI 📣 Custom alerting to be the first to know about metric issues 🔬Troubleshooting with row-level diff of the actual data (not column or table metadata) Tell us what you think!

🤗 Join our community 🤗 Our Discord community is an inclusive place where anyone interested in Data Quality, Observability & Analytics is welcome! Whether you are a Data Analyst, Data Engineer, Analytics Engineer, or business stakeholder working with Data teams, you will find resources on the latest data space news, tools and best practices. And for our users, this is the place for fast support and feedback 😻 Join our Discord here => https://lnkd.in/eGRnQbX8

Tracking data changes in Git is top trending today on #HackerNews We do just that for your warehouse's key assets, like metrics. Check out our nicely formatted diff just below 👇 => https://lnkd.in/ekFxCHZJ

Checkout our latest blog post 👇 In this (long) article, Lucas shares the complexity behind MRR and its computation: 🤕 getting messy data from billing tools 🤡 dealing with discounts and offers 📅 determining MRR recognition date To analytics team struggling out there with MRR: you are not alone, and now it's clearer as to why this is the case. Curious to hear your personal experiences with MRR 🤗