Metaplane

If Snowflake is the iPhone of data platforms, then its Native App Framework is the App Store. Applications can now be built and deployed on Snowflake infrastructure, fulfilling the vision of bringing code to data, rather than data to code. That’s why I’m delighted to announce that Metaplane’s Snowflake Native App is now live in public preview! ?? Now Snowflake customers can: 1. Install data observability directly from the Snowflake Marketplace 2. Pay using credits you’ve already purchased 3. Skip the headache of complex security reviews and long procurement cycles 4. Get ML-powered monitoring and end-to-end lineage running in minutes, with computation within Snowflake’s secure environment If quotes like “???????? ???????????? ?????????? ??????????,” “?????? ???????? ??????’?? ?????????? ?????? ??????????,” or “?? ?????????? ?????? ???????? ???????? ???????????????? ??????????” sound familiar, now you can ensure trust in data with observability built right into Snowflake. Learn more and check it out on the Snowflake Marketplace. Link in the comments below ?? #dataengineering #dataquality #snowflake #dataobservability

Why can design partners backfire? Is PLG choice or destiny? How do you catch the attention of investors like Tobi Coker? Is founder coaching worth it? At TechCrunch Disrupt, I sat down with our investor Tobi to tackle these questions and more in our best imitation of Comedians in Cars Getting Coffee, despite being 0/3 of those things. Here are 8 + 1 key takeaways: ?????????? ?????????????? 1. Working with design partners pre-PMF can be a triple mistake: you miss honest feedback, fail to de-risk GTM, and don’t confirm real pain points. ?????????????? 2. PLG is a good GTM approach but a great crucible to evolve the best product. 3. Product-led vs. sales-led is more destiny than choice, often shaped by the founding team’s DNA. 4. Shipping cadence is necessary but not sufficient; you can get stuck in local optima. ?????? 5. Counter-positioning is attractive to investors like Tobi. Examples include MotherDuck (focus on the small data that is actually valuable) and DatologyAI (train LLMs with 10x fewer data) challenge the “more is better” mindset. 6. When startups discuss “cost center” vs. “revenue driver,” we often forget the goal of improving someone's life at work. 7. Partnering with teams like Ramp and Klaviyo that "live in the future" benefits all customers, particularly those earlier on the adoption curve. ???????????????????? 8. Look for investors who both "give feedback” and “are calibrated” (think 2x2 matrix). ??????????: Many folks post-talk were curious about founder coaching—IMO it's key to building the backbone for making and executing tough calls at every stage. And I’m grateful to work with Felicis, who actually commit 1% of their investment to supporting founder coaching. #startups #dataengineering #techcrunchdisrupt

Kevin Hu, PhD, CEO of Metaplane, shares two mistakes they made when going to market: 1?? Relying on "friendlies" 2?? Trying to iterate toward a paid product He recently sat down with Tobi Coker, Partner at Felicis, to discuss his inspiration for co-founding Metaplane, what he’s learned about growing the business, and how he positions data teams to make an impact. If you’re a founder building in the data or infrastructure space, you’ll want to listen to the lessons Kevin has to share. Link to the full conversation in the comments #founders #startups #data

Is there more business-critical data than payments data at payments companies? The data team at SpotOn is responsible for building and maintaining the pipelines that enable businesses to get paid. Next week at dbt Labs Coalesce in Las Vegas, I'm presenting with long-time partner and friend Daniel Corley on how his team tackled a major migration of these pipelines. We discuss big questions like: 1. What ?????????????? happens behind-the-scenes when you buy a latte at a coffee shop? 2. How the heck do you deal with "positionally defined" data where a transaction can span multiple lines? 3. Why do legacy pipelines become brittle, and how can we re-build them to become production grade? I've learned a lot by working with Dan on this talk, and think you will too. Come through if you're in Las Vegas next week! #dataengineering #dbt #analytics

As we gear up for Coalesce, why not kick off the week with a relaxing evening at Data Uncorked? ?? Join us for a low-key gathering where you can unwind, connect with fellow data thought leaders, and get ready for an exciting week ahead with our friends from Metaplane, Hightouch, Hex, Lightdash, and Atlan. ?? When: Monday, 10/7 from 7-9p ?? Where: Wally's at Resorts World Spots are limited, so RSVP now and kick off Coalesce with us in style! ?? https://ow.ly/cjs150TvbEo #Coalesce24

You can’t properly treat a symptom without understanding its cause. Like a data doctor, you need to look at your data issues from all angles—identifying both the symptoms (what’s visibly wrong in your pipeline) and the causes (the underlying issues leading to those problems). That’s why we launched Fivetran observability to begin with: so you could start understanding which connectors were upstream of data quality issues. Today, we’re rolling out another huge enhancement as part of our Summer of Data Observability: observability on Fivetran metrics like connector duration and freshness. Our new dashboard puts connector performance front and center, so you can catch ETL issues as they happen. Now, you can: ? Explore how long each connector takes ? See how many rows Fivetran synced ? Check for unusual connector performance spikes ? Consolidate alerts in one place Check out the blog post in the comments. Can you guess what we’re launching next? ?? #dataengineering #dataobservability #data

The reviews are in!!! ?? ???? (I know we're not in show business but, cmon, we still get to say that line) Metaplane has been announced again as the G2 Leader for the Data Observability category for Summer 2024. We are also rated #1 for EVERY SINGLE supporting metric: Best Relationship, Best Usability, Best Results, Best Implementability. Enormous thank you to our customers who continue to trust us to monitor their data.

Yesterday, I asked the community, what’s worse? When a stakeholder: 1. Trusts data but it turns out to be wrong 2. Spots a data issue before the data team In both cases, trust is eroded. All because the condition and quality of data aren’t obvious. Until now. ?? Introducing our 2nd Summer of Data Observability drop: the Metaplane Chrome Extension. The Metaplane Chrome Extension shows all users a summary of the metrics you monitor in Metaplane in a convenient side panel. Now, everyone has the context they need in a simplified, actionable view – when and where they need it. That means… ? Stakeholders know the quality of data as they’re using it ? They don’t have to ask the data team if that data can be trusted ? They know your team is on top of it (and have quality checks already in place) Read more about the feature in the blog post in the comments! If you use Metaplane already, you can get it installed and running in <5 minutes.

Data movement needs to be scalable, extensible, and?reliable. As 6,000+ companies move data daily from our 300+ data sources, we have also invested heavily in making Airbyte observable through rich metadata available via an API. Today we’re happy to announce that?Metaplane, the leading data observability platform used by our own data team, launched an Airbyte integration on top of that API. Data teams can use Airbyte and Metaplane to proactively detect issues like long-running syncs, late-arriving data, and syncs that moved an incorrect amount of data from source to destination. These monitors use machine learning trained on historical metadata, so no configuration is needed. You can also see which Airbyte streams load tables in your warehouse, so you can trace end-to-end lineage from source to destination. Read more here: https://lnkd.in/geAtE7Tw #dataengineering #dataquality #dataobservability #dataintegration

Data teams should be the first to know about data issues. That’s why I’m particularly excited to announce the first ???????????? ???? ???????? ?????????????????????????? drop: Metaplane now integrates with Airbyte. Airbyte is the leading open-source data movement platform, helping 6K+ companies move data from 300+ data sources. Now, both Airbyte Cloud and self-managed users can proactively detect issues like: ??. ????????-?????????????? ?????????? (duration) ??. ????????-???????????????? ???????? (last succeeded timestamp) ??. ?????????? ???????? ????????’?? ???????? ?????? ?????????? ???????????? ???? ???????? (rows and bytes synced) Our Airbyte integration monitors all of these metrics out-of-the-box with machine learning, so no configuration is needed. We also extract lineage so you can see the Airbyte streams that feed into warehouse tables and trace dependencies end-to-end from ingestion to consumption. Read more about the integration in the blog post in the comments :). And if you use Metaplane already, you can connect in <5 minutes. #dataengineering #dataquality #dataintegration