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Agent this, harness that. Here’s the entire AI stack broken down, plus a whole new open source layer introduced by @databricksinc last week called Omnigent that changes the game entirely #databricks #ai #omnigent #databrickspartner
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databricksinc
Agent this, harness that. Here’s the entire AI stack broken down, plus a whole new open source layer introduced by @databricksinc last week called Omnigent that changes the game entirely #databricks #ai #omnigent #databrickspartner
Databricks co-founder and CEO Ali Ghodsi announces Genie Ontology - an automatic context layer, at #DataAISummit. Genie Ontology extracts snippets of knowledge from tables, queries, dashboards, pipelines, and connected apps, and organizes that knowledge into a living graph of how a company works and what the data inside actually means. Genie has context about where to look, what to trust, and how to answer in a way that reflects how the company actually uses its data. That includes metric definitions, business terms, unique calculations, and the relationships between concepts, metrics, tables, and teams. Watch the full keynote. Link in bio 🔗
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databricksinc
Databricks co-founder and CEO Ali Ghodsi announces Genie Ontology - an automatic context layer, at #DataAISummit. Genie Ontology extracts snippets of knowledge from tables, queries, dashboards, pipelines, and connected apps, and organizes that knowledge into a living graph of how a company works and what the data inside actually means. Genie has context about where to look, what to trust, and how to answer in a way that reflects how the company actually uses its data. That includes metric definitions, business terms, unique calculations, and the relationships between concepts, metrics, tables, and teams. Watch the full keynote. Link in bio 🔗
The story of how Databricks became a $134B company.
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databricksinc
The story of how Databricks became a $134B company.
A new layer of the AI stack got added. Here’s how it stacks up, from bottom to top: 𝗟𝗮𝘆𝗲𝗿 𝟭: 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀 The LLM itself. GPT, Claude, Gemini. Raw intelligence with no structure around it. 𝗟𝗮𝘆𝗲𝗿 𝟮: 𝗧𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝗛𝗮𝗿𝗻𝗲𝘀𝘀 Tools, memory, planning, sandbox, guardrails. All the software wrapped around the model that turns a chatbot into an agent. 𝗟𝗮𝘆𝗲𝗿 𝟯: 𝗧𝗵𝗲 𝗠𝗲𝘁𝗮-𝗛𝗮𝗿𝗻𝗲𝘀𝘀 (𝗢𝗺𝗻𝗶𝗴𝗲𝗻𝘁) A layer that sits above individual harnesses and makes them interoperable. Compose multiple agents, govern them with policies, and collaborate on live sessions with teammates. Here is how Omnigent works, in two parts: 𝘙𝘶𝘯𝘯𝘦𝘳 Wraps any single agent (Claude Code, Codex, Pi, or your own custom agent) in a sandboxed session and exposes it through one uniform API. Whatever the agent does internally, the interface is the same: messages and files in, text streams and tool calls out. 𝘚𝘦𝘳𝘷𝘦𝘳 Sits above the runners. It enforces policies (cost budgets, contextual security rules, permissions), enables live sharing, and exposes every session across the terminal, desktop app, mobile, and web APIs. One agent, many interfaces, full governance. Think polyglot orchestration: different models, different harnesses, different specialties, one unified workflow, with controls that live at the meta-harness layer instead of buried in prompts. Databricks open-sourced omnigent. 🔗 This Github has over 5,000 ⭐ github.com/omnigent-ai/omnigent Save and share with others! ______ Hi 👋 I’m Viktoria, Principal AI Evangelist at Databricks and former Partner Solutions Architect at AWS. I turn complex AI and data concepts into content people actually understand. Follow for more! #AgenticEngineering #Databricks #Omnigent #Opensource
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databricksinc
A new layer of the AI stack got added. Here’s how it stacks up, from bottom to top: 𝗟𝗮𝘆𝗲𝗿 𝟭: 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀 The LLM itself. GPT, Claude, Gemini. Raw intelligence with no structure around it. 𝗟𝗮𝘆𝗲𝗿 𝟮: 𝗧𝗵𝗲 𝗔𝗴𝗲𝗻𝘁 𝗛𝗮𝗿𝗻𝗲𝘀𝘀 Tools, memory, planning, sandbox, guardrails. All the software wrapped around the model that turns a chatbot into an agent. 𝗟𝗮𝘆𝗲𝗿 𝟯: 𝗧𝗵𝗲 𝗠𝗲𝘁𝗮-𝗛𝗮𝗿𝗻𝗲𝘀𝘀 (𝗢𝗺𝗻𝗶𝗴𝗲𝗻𝘁) A layer that sits above individual harnesses and makes them interoperable. Compose multiple agents, govern them with policies, and collaborate on live sessions with teammates. Here is how Omnigent works, in two parts: 𝘙𝘶𝘯𝘯𝘦𝘳 Wraps any single agent (Claude Code, Codex, Pi, or your own custom agent) in a sandboxed session and exposes it through one uniform API. Whatever the agent does internally, the interface is the same: messages and files in, text streams and tool calls out. 𝘚𝘦𝘳𝘷𝘦𝘳 Sits above the runners. It enforces policies (cost budgets, contextual security rules, permissions), enables live sharing, and exposes every session across the terminal, desktop app, mobile, and web APIs. One agent, many interfaces, full governance. Think polyglot orchestration: different models, different harnesses, different specialties, one unified workflow, with controls that live at the meta-harness layer instead of buried in prompts. Databricks open-sourced omnigent. 🔗 This Github has over 5,000 ⭐ github.com/omnigent-ai/omnigent Save and share with others! ______ Hi 👋 I’m Viktoria, Principal AI Evangelist at Databricks and former Partner Solutions Architect at AWS. I turn complex AI and data concepts into content people actually understand. Follow for more! #AgenticEngineering #Databricks #Omnigent #Opensource
⚡What are Declarative Pipelines in < 90 seconds? Let’s walk with Michael Armbrust, Distinguished Engineer at @databricksinc who led the design & development of Declarative Pipelines + created Spark SQL, Structured Streaming, Delta Lake, & much more as he breaks it down! 💡 TLDR: instead of telling the system HOW to do every step, you just tell it WHAT you want to exist. Spark figures out the how. The technology itself is open source in Apache Spark, and Databricks offers a managed version on top called Lakeflow Spark Declarative Pipelines. 💻 Want to get started with Declarative Pipelines? Comment ✨Databricks✨ & I’ll send you the resources: an intro hands-on tutorial + Michael’s recorded deep dive talk from the Data + AI Summit 2026. It was such a blast getting to chat with Michael! Did you know he was also the 9th employee at Databricks?! As always, Happy Building! 😊 #DatabricksPartner
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databricksinc
⚡What are Declarative Pipelines in < 90 seconds? Let’s walk with Michael Armbrust, Distinguished Engineer at @databricksinc who led the design & development of Declarative Pipelines + created Spark SQL, Structured Streaming, Delta Lake, & much more as he breaks it down! 💡 TLDR: instead of telling the system HOW to do every step, you just tell it WHAT you want to exist. Spark figures out the how. The technology itself is open source in Apache Spark, and Databricks offers a managed version on top called Lakeflow Spark Declarative Pipelines. 💻 Want to get started with Declarative Pipelines? Comment ✨Databricks✨ & I’ll send you the resources: an intro hands-on tutorial + Michael’s recorded deep dive talk from the Data + AI Summit 2026. It was such a blast getting to chat with Michael! Did you know he was also the 9th employee at Databricks?! As always, Happy Building! 😊 #DatabricksPartner
What Is a Meta-Harness for AI Agents? Omnigent explained in 60 seconds ↓ AI agents are getting more powerful, but the way we run them is still fragmented. Every agent has its own harness, context, permissions, interface, and workflow. That’s why Databricks introduced Omnigent: a new layer above individual agents and a meta-harness for agent orchestration. Instead of managing agents one by one, Omnigent gives you a common layer to compose, control, and collaborate across agents. With Omnigent, you can: 💻 Switch between and combine agent harnesses, like Claude Code + Codex 🚀 Orchestrate multiple agents and run them from one window 🛠️ Build custom agents and swap models or harnesses 🛡️ Manage policies, sandboxing, and cost in one place 💬 Collaborate on live agent sessions with colleagues Have you tried Omnigent? The adoption has been wild over 6000 stars in less than a month 🌟🚀 Try it, build with it, and help shape the future of agent orchestration.
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databricksinc
What Is a Meta-Harness for AI Agents? Omnigent explained in 60 seconds ↓ AI agents are getting more powerful, but the way we run them is still fragmented. Every agent has its own harness, context, permissions, interface, and workflow. That’s why Databricks introduced Omnigent: a new layer above individual agents and a meta-harness for agent orchestration. Instead of managing agents one by one, Omnigent gives you a common layer to compose, control, and collaborate across agents. With Omnigent, you can: 💻 Switch between and combine agent harnesses, like Claude Code + Codex 🚀 Orchestrate multiple agents and run them from one window 🛠️ Build custom agents and swap models or harnesses 🛡️ Manage policies, sandboxing, and cost in one place 💬 Collaborate on live agent sessions with colleagues Have you tried Omnigent? The adoption has been wild over 6000 stars in less than a month 🌟🚀 Try it, build with it, and help shape the future of agent orchestration.
⚡ Lakebase vs Lakehouse in < 90 seconds. Why do you need both? Michael Armbrust, Distinguished Engineer at @databricksinc , is back for another tech walk! 💡 TLDR: Lakehouse solved analytics. Lakebase does the same thing for the transactional side. Two workloads, one open format. Back in the day, analytics over massive data meant a proprietary engine and a proprietary format. You loaded your data in and you were trapped. Lakehouse changed that: separate compute and storage, keep the data in an open format, and any engine can read it. But analytics is only one kind of workload. The apps behind things like RAG don’t run huge queries. They run tiny operations that need answers in milliseconds. That’s a latency problem, and it’s a different type of data. The interesting part: Lakebase applies the same open-format unlock to OLTP that Lakehouse applied to analytics, with open source Postgres underneath. 💻 Want to get hands-on? Everything here is testable on Databricks Free Edition Comment ✨LAKE✨ & ill DM you the link
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databricksinc
⚡ Lakebase vs Lakehouse in < 90 seconds. Why do you need both? Michael Armbrust, Distinguished Engineer at @databricksinc , is back for another tech walk! 💡 TLDR: Lakehouse solved analytics. Lakebase does the same thing for the transactional side. Two workloads, one open format. Back in the day, analytics over massive data meant a proprietary engine and a proprietary format. You loaded your data in and you were trapped. Lakehouse changed that: separate compute and storage, keep the data in an open format, and any engine can read it. But analytics is only one kind of workload. The apps behind things like RAG don’t run huge queries. They run tiny operations that need answers in milliseconds. That’s a latency problem, and it’s a different type of data. The interesting part: Lakebase applies the same open-format unlock to OLTP that Lakehouse applied to analytics, with open source Postgres underneath. 💻 Want to get hands-on? Everything here is testable on Databricks Free Edition Comment ✨LAKE✨ & ill DM you the link
Databricks just launched the Context Engineer Associate cert! 💻 It assesses your ability to design, assemble, and govern the information AI agent systems receive at inference time: retrieval, memory, tool integration, context-window management, and governance. 💡This is a crucial skill because the quality of an AI system depends not just on the model, but on the context it receives. The exam is in beta and debuts at Data + AI Summit where you can be one of the first to take it. It’s walk-in at the cert room with one free attempt on-site. The learning material itself is free, the hands-on labs may vary, and the other Databricks certs will be 50% off on-site as well. You can also tune-in to the summit virtually for free ✨ Comment ‘context’ and I’ll send you the exam guide and cert info LMK if you will be there in person! Let’s meetup! #DatabricksPartner @databricksinc
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databricksinc
Databricks just launched the Context Engineer Associate cert! 💻 It assesses your ability to design, assemble, and govern the information AI agent systems receive at inference time: retrieval, memory, tool integration, context-window management, and governance. 💡This is a crucial skill because the quality of an AI system depends not just on the model, but on the context it receives. The exam is in beta and debuts at Data + AI Summit where you can be one of the first to take it. It’s walk-in at the cert room with one free attempt on-site. The learning material itself is free, the hands-on labs may vary, and the other Databricks certs will be 50% off on-site as well. You can also tune-in to the summit virtually for free ✨ Comment ‘context’ and I’ll send you the exam guide and cert info LMK if you will be there in person! Let’s meetup! #DatabricksPartner @databricksinc
⏳90 second crash course on Databricks’ biggest launches across the Data + AI stack It all came back to one thing: even if an agent can reach your data, it still doesn’t understand your business. Here’s how the new stack addresses that, layer by layer. 🗄️ 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗗𝗮𝘁𝗮  LTAP puts operational and analytical data on a single copy in open formats, so there’s no pipeline between Lakebase and the Lakehouse to break or go stale. 🛡️ 𝗨𝗻𝗶𝗳𝗶𝗲𝗱 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲  Unity AI Gateway runs every agent request through one governance layer with hard spend caps and intelligent routing, so simpler tasks go to smaller, cheaper models instead of everything hitting the biggest one. 🧠  𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲  Genie Ontology continuously builds a live graph of your enterprise knowledge in the background, so agents get your business context automatically. 🧰 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 Omnigent, now open source under Apache 2.0, is a meta-harness that runs your coding agents together in one place, so they share context and controls instead of sitting in separate silos. The through-line Ali Ghodsi laid out: Context so agents understand your business, Control over what they can do, Cost you can actually manage, and Choice of any data, model, or harness with no lock-in. So much more was announced at the 2026 Data + AI summit! Ali Ghodsi also gave a fantastic 5 minute mind map of the announcements at the end of Day 2 keynote 🙌 Comment ✨Databricks✨ for the links to Ali Ghodsi’s 5 minute mind map + announcement blogs
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databricksinc
⏳90 second crash course on Databricks’ biggest launches across the Data + AI stack It all came back to one thing: even if an agent can reach your data, it still doesn’t understand your business. Here’s how the new stack addresses that, layer by layer. 🗄️ 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗗𝗮𝘁𝗮  LTAP puts operational and analytical data on a single copy in open formats, so there’s no pipeline between Lakebase and the Lakehouse to break or go stale. 🛡️ 𝗨𝗻𝗶𝗳𝗶𝗲𝗱 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲  Unity AI Gateway runs every agent request through one governance layer with hard spend caps and intelligent routing, so simpler tasks go to smaller, cheaper models instead of everything hitting the biggest one. 🧠  𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗘𝗻𝗴𝗶𝗻𝗲  Genie Ontology continuously builds a live graph of your enterprise knowledge in the background, so agents get your business context automatically. 🧰 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 Omnigent, now open source under Apache 2.0, is a meta-harness that runs your coding agents together in one place, so they share context and controls instead of sitting in separate silos. The through-line Ali Ghodsi laid out: Context so agents understand your business, Control over what they can do, Cost you can actually manage, and Choice of any data, model, or harness with no lock-in. So much more was announced at the 2026 Data + AI summit! Ali Ghodsi also gave a fantastic 5 minute mind map of the announcements at the end of Day 2 keynote 🙌 Comment ✨Databricks✨ for the links to Ali Ghodsi’s 5 minute mind map + announcement blogs
Databricks CEO and co-founder Ali Ghodsi joined @Bloomberg Tech to discuss the launch of Genie Code, an autonomous AI agent built specifically for data teams.
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databricksinc
Databricks CEO and co-founder Ali Ghodsi joined @Bloomberg Tech to discuss the launch of Genie Code, an autonomous AI agent built specifically for data teams.
We're bringing millisecond performance directly to the lakehouse. Databricks Co-founder and Chief Architect Reynold Xin introduces Lakehouse//RT, Databricks’ new real-time data warehouse designed for operational analytics, BI and app serving, and observability workloads. Lakehouse//RT is powered by Reyden, a breakthrough new engine for real-time workloads that require immediate responsiveness at high concurrency.
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databricksinc
We're bringing millisecond performance directly to the lakehouse. Databricks Co-founder and Chief Architect Reynold Xin introduces Lakehouse//RT, Databricks’ new real-time data warehouse designed for operational analytics, BI and app serving, and observability workloads. Lakehouse//RT is powered by Reyden, a breakthrough new engine for real-time workloads that require immediate responsiveness at high concurrency.
Behind the scenes at #DataAISummit, as our partner ecosystem comes together for attendees at the Expo! 🏗️
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databricksinc
Behind the scenes at #DataAISummit, as our partner ecosystem comes together for attendees at the Expo! 🏗️
“All we know” is that The Chainsmokers are headlining Data After Hours at #DataAISummit 2026! Not registered yet? Head to the link in bio and secure your spot. You won’t want to miss this.
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databricksinc
“All we know” is that The Chainsmokers are headlining Data After Hours at #DataAISummit 2026! Not registered yet? Head to the link in bio and secure your spot. You won’t want to miss this.
What if the longer you drove your car, the better it got? @rivianofficial’s Director of Big Data & AI, Michael Flynn, explains how the company streams tens of thousands of telemetry, sensor, and video data points from its vehicles into the lakehouse. Using machine learning and Genie, the team learns from that data and applies lessons from one vehicle across the fleet to improve the driving experience over time. Explore how Rivian uses data and AI to help its vehicles get better the more they’re driven.
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databricksinc
What if the longer you drove your car, the better it got? @rivianofficial’s Director of Big Data & AI, Michael Flynn, explains how the company streams tens of thousands of telemetry, sensor, and video data points from its vehicles into the lakehouse. Using machine learning and Genie, the team learns from that data and applies lessons from one vehicle across the fleet to improve the driving experience over time. Explore how Rivian uses data and AI to help its vehicles get better the more they’re driven.
Databricks Co-Founder and CEO Ali Ghodsi has been named to the 2026 TIME100 AI list! TIME highlights Ali in their AI leaders category saying, “as AI agents change how organizations work, Databricks is giving those agents access to secure, governed enterprise data on which to operate." Congratulations to Ali and all of this year's #TIME100AI honorees!
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databricksinc
Databricks Co-Founder and CEO Ali Ghodsi has been named to the 2026 TIME100 AI list! TIME highlights Ali in their AI leaders category saying, “as AI agents change how organizations work, Databricks is giving those agents access to secure, governed enterprise data on which to operate." Congratulations to Ali and all of this year's #TIME100AI honorees!
This morning's #DataAISummit keynote brought major announcements, live demos, and conversations with Databricks leaders, customers and AI visionaries. Here are a few of the biggest announcements: • Lakehouse//RT: Real-Time Performance on a Unified Lakehouse • Genie One, Genie Agents, and Genie Ontology • Genie ZeroOps: Put your data and AI operations on autopilot • CustomerLake: The Agentic CDP embedded in Databricks • & much more! Catch up on everything announced today —🔗 in our bio.
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databricksinc
This morning's #DataAISummit keynote brought major announcements, live demos, and conversations with Databricks leaders, customers and AI visionaries. Here are a few of the biggest announcements: • Lakehouse//RT: Real-Time Performance on a Unified Lakehouse • Genie One, Genie Agents, and Genie Ontology • Genie ZeroOps: Put your data and AI operations on autopilot • CustomerLake: The Agentic CDP embedded in Databricks • & much more! Catch up on everything announced today —🔗 in our bio.
Databricks co-founder and CEO Ali Ghodsi recaps everything you need to know from Tuesday's keynote in 60 seconds. We introduced the agentic data foundation: Lakehouse//RT, Lakebase Disaster Recovery Vector Search, LTAP, Lakeflow. The context layer: Genie Ontology, Unity AI Gateway, OpenSharing, Omnigent OSS, Agent Bricks. Tools for agentic dev work: Genie One, Genie Code, and Genie Agents. And apps: Lakewatch for your SIEM and Customer Lake for your CDP. Watch the full keynote. Link in bio 🔗
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databricksinc
Databricks co-founder and CEO Ali Ghodsi recaps everything you need to know from Tuesday's keynote in 60 seconds. We introduced the agentic data foundation: Lakehouse//RT, Lakebase Disaster Recovery Vector Search, LTAP, Lakeflow. The context layer: Genie Ontology, Unity AI Gateway, OpenSharing, Omnigent OSS, Agent Bricks. Tools for agentic dev work: Genie One, Genie Code, and Genie Agents. And apps: Lakewatch for your SIEM and Customer Lake for your CDP. Watch the full keynote. Link in bio 🔗
Databases are shifting from being designed for humans to being designed for AI agents. Traditional databases take time to provision, configure, and duplicate for testing. That works when a human sets up a database occasionally. But AI agents operate at machine speed. Lakebase changes the architecture by separating compute from storage, so compute can spin up instantly without copying or moving data. With database branching, agents can create isolated environments in seconds, experiment safely, and roll back instantly.
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databricksinc
Databases are shifting from being designed for humans to being designed for AI agents. Traditional databases take time to provision, configure, and duplicate for testing. That works when a human sets up a database occasionally. But AI agents operate at machine speed. Lakebase changes the architecture by separating compute from storage, so compute can spin up instantly without copying or moving data. With database branching, agents can create isolated environments in seconds, experiment safely, and roll back instantly.
Databricks ranked #2 on the 2026 Enterprise Tech 30 in the Giga Stage—a recognition of the companies redefining enterprise technology and how the world works. Chosen by 90+ top VCs and industry leaders, this honor reflects the momentum we’re driving behind data + AI innovation and the teams building what’s next. Grateful to Wing Venture Capital and Eric Newcomer for the recognition!
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databricksinc
Databricks ranked #2 on the 2026 Enterprise Tech 30 in the Giga Stage—a recognition of the companies redefining enterprise technology and how the world works. Chosen by 90+ top VCs and industry leaders, this honor reflects the momentum we’re driving behind data + AI innovation and the teams building what’s next. Grateful to Wing Venture Capital and Eric Newcomer for the recognition!
We’re #SuperExcited about our new products and innovations announced at #DataAISummit this year: • Lakehouse//RT: Real-Time Performance on a Unified Lakehouse • Genie One, Genie Agents, and Genie Ontology • Genie ZeroOps: Put your data and AI operations on autopilot • CustomerLake: The Agentic CDP embedded in Databricks • Managed version of our open-source meta-harness Omnigent • App Spaces, Genie App Builder and Serverless Micro Apps bring governed vibe coding of apps to Databricks • Agents for ML engineering, our deep learning platform, and new capabilities for real-time ML • and much more We can’t wait to see what you build!
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databricksinc
We’re #SuperExcited about our new products and innovations announced at #DataAISummit this year: • Lakehouse//RT: Real-Time Performance on a Unified Lakehouse • Genie One, Genie Agents, and Genie Ontology • Genie ZeroOps: Put your data and AI operations on autopilot • CustomerLake: The Agentic CDP embedded in Databricks • Managed version of our open-source meta-harness Omnigent • App Spaces, Genie App Builder and Serverless Micro Apps bring governed vibe coding of apps to Databricks • Agents for ML engineering, our deep learning platform, and new capabilities for real-time ML • and much more We can’t wait to see what you build!

Databricks (@databricksinc) Instagram Stats & Analytics

Databricks (@databricksinc) has 115K Instagram followers with a 2.55% engagement rate over the past 12 months. Across 148 posts, Databricks received 80.8K total likes and 2.36M impressions, averaging 546 likes per post. This page tracks Databricks's performance metrics, top content, and engagement trends — updated daily.

Databricks (@databricksinc) Instagram Analytics FAQ

How many Instagram followers does Databricks have?+
Databricks (@databricksinc) has 115K Instagram followers as of September 2026.
What is Databricks's Instagram engagement rate?+
Databricks's Instagram engagement rate is 2.55% over the last 12 months, based on 148 posts.
How many likes does Databricks get on Instagram?+
Databricks received 80.8K total likes across 148 posts in the last 12 months, averaging 546 likes per post.
How many Instagram impressions does Databricks get?+
Databricks's Instagram content generated 2.36M total impressions over the last 12 months.