--- title: "Metadata Lakehouse: An Introduction and Demo" url: "https://atlan.com/demos/metadata-lakehouse-introduction/" description: "Unify your metadata with Atlan's Metadata Lakehouse, powered by Apache Iceberg, making it queryable in Snowflake and Databricks for unparalleled analysis and action." format: "Video" video: "https://videos.ctfassets.net/nwa1c00rtgxb/6in5crunLCPcnBLPRmZFX1/87eb023a3a6808c480cce15d8e1ffbd0/Introducing_the_Atlan_Metadata_Lakehouse.mp4" content_purpose: ["Product Overview"] target_persona: ["General", "Data Scientist", "Data Analyst"] journey_stage: ["S2 - Discovery", "C1 - Onboarding", "C4 - Expansion", "C5 - Optimization"] use_case_context: ["Training"] product: ["Enterprise Data Graph - Integration"] content_type: "video transcript" transcript_source: "contentful" --- # Metadata Lakehouse: An Introduction and Demo Transcript of the video at https://atlan.com/demos/metadata-lakehouse-introduction/ Introducing the Atlan Metadata Lakehouse Speaker: [00:00] We are really proud to introduce Context Stores powered by Lakehouse Architecture. So we are building this from the ground up with an open table format like iceberg, which makes it versioned open by design and interoperable. You can bring your favorite compute to query, run analytics, run your AI workloads. You can bring your own model on top of this. It's, and it's not just a store, it has out of box capabilities with things like Symantec and hybrid search. Like we always do, we keep it open and extensible with your frameworks, APIs, and apps that you can build on top of this. And we are also really proud to partner with amazing partners in the ecosystem, just like with all the pillars to help accelerate this for our customers. Tom, do you wanna take this and make it real for everybody here? I would love to. Let me take the screen. Boom. [01:00] All right. AI is only as powerful as the metadata that fuels it. And until now, metadata has been locked away in catalogs hard to access and even harder to put to work. Atlan changes that we've built, A metadata lakehouse, powered by Apache Iceberg and made it queryable, both in Snowflake and Databricks. That means your metadata is no longer just documentation, it's data you can analyze, visualize, and act on. Let me show you. Here in Snowflake, you'll see the metadata lakehouse exposed. Just like any other database, I can explore the tables and if I want it, I can even run a query. For example, I'm gonna check the description, quality on tables using snowflake cortex. Metadata isn't abstract anymore, it's insanely queryable. Now I'll hop over to Databricks because you know what? Same metadata, same tables, same query. But with [02:00] Databricks syntax available natively here too. That's the power of using an open table format like iceberg. No lock-in, no silos, just one consistent foundation for all your metadata. And because metadata is now data, you can analyze it anywhere like in a Sigma dashboard, built directly on top of the metadata lakehouse. This is one design for a chief data officer who wants to know how ready are my domains for text to SQL use cases, which domains are well documented and govern. Which need investment. This is just one example. The truth is you can build any custom dashboard or any metric you can imagine. Operational efficiency, governance, adoption, AI readiness. The use cases are endless. That's the vision of the metadata. Lakehouse your metadata stored in an open format, queryable and Snowflake and Databricks powering whatever use case your business needs. As a matter of fact, [03:00] Riddick, would you mind taking over the screen and showing what our launch Partner Sigma built on top of the metadata lakehouse? Hello everyone. I'm David Lyons with Sigma Computing. I'm a senior solution engineer here and Sigma's thrilled to be a launch partner of Atlas Metadata Lakehouse, built on Iceberg. Today we're gonna show a prebuilt Sigma application that offers a few representative examples around popular use cases such as compliance and storage utilization. Now let's go ahead and get started. So in this particular view, I'm actually looking at compliance where we're wanting to manage our tables and views and manage those that do not have tags. In this case, actually see 31% actually don't have tags, which did a large number. In addition, the Sigma platform allows us to utilize a graphical visualization. Here we're wanting to look at our storage utilization and identify [04:00] those large tables that may not be utilized. In this case, I can actually see our dim company has a popularity score of zero, but it's a fairly significantly, uh, large table. Other types of views we may wanna look at is a historical analysis where we're looking at tables without a description. Here, we can see that over time and kind of track this information, maybe some training for our data stewards to make sure that they are always adding a description when they add new objects in. So here, very quickly, I just showed a few representative examples of the power of Sigma on top of your metadata. Lakehouse built on iceberg. Being able to not only visualize data, but also take action on that.