惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

J
Java Code Geeks
小众软件
小众软件
博客园 - 叶小钗
宝玉的分享
宝玉的分享
博客园_首页
Hugging Face - Blog
Hugging Face - Blog
人人都是产品经理
人人都是产品经理
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
S
SegmentFault 最新的问题
B
Blog RSS Feed
Engineering at Meta
Engineering at Meta
N
Netflix TechBlog - Medium
Google DeepMind News
Google DeepMind News
U
Unit 42
F
Fortinet All Blogs
IT之家
IT之家
Y
Y Combinator Blog
Martin Fowler
Martin Fowler
T
The Blog of Author Tim Ferriss
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
The GitHub Blog
The GitHub Blog
Stack Overflow Blog
Stack Overflow Blog
Blog — PlanetScale
Blog — PlanetScale
酷 壳 – CoolShell
酷 壳 – CoolShell

Databricks

Using AI_Functions in Your Data Warehouse: Top Use Cases How Scottish Water Made Its Capital Investment Data Conversational With Databricks Genie What are AI Hallucinations? Smart Routing in Unity AI Gateway: Match frontier quality with 30%+ lower cost per task Databricks Network Configuration delivery to Tens of Millions of Serverless VMs How Amtrak is building the data backbone for its largest transformation in over 50 years How a major freight railroad scaled pipeline creation with Genie Code The Future of Data Analytics: Why AI is rewriting the Analyst’s Job Description Taking AUTO CDC to the next level: Solving the hardest real-world use cases Open-sourcing Metals v2: Databricks’ Java and Scala language server for multi‑million line codebases Modern Risk Demands a Real-Time Foundation: The CRO’s Mandate How to ground Genie Agents in both structured data and documents without losing governance Innocent until combined: Blocking the lethal trifecta with Omnigent Contextual Policies Introducing FILE type: a native column type for multimodal data Managing AI Coding Costs at Scale What is an AI Assistant? What are Agentic Workflows? What is Tool Calling? Kimi K3 from Moonshot AI is now available on Databricks through Unity AI Gateway Introducing OfficeQA Pro V2: A New Benchmark for Enterprise Grounded-Reasoning BigQuery to Databricks: A Strategic Framework for Modern Migration Unity AI Gateway is Generally Available Granular Usage Attribution for dbt Pipelines with Query Tags - Cloned Databricks joins the Open Secure AI Alliance to advance AI safety and security The New Monday Morning Report: How Generative AI can deliver the insights your executives need. Ingest semi-structured data faster and more efficiently with Variant - Now Generally Available Databricks Completes Acquisition of Panther: Accelerating the Security Lakehouse Era Backstage with Lakebase, part 3 Foundations for an AI-forward healthcare organization Agentic media buying cannot scale without the right foundation. See how buyers and sellers get there on Databricks.
Electric joins Databricks to bring WASM Postgres to AI ag...
Stas Kelvich, Yan Leshinsky, Nikita Shamgunov, Reynold Xin · 2026-08-11 · via Databricks

Together, Lakebase and Electric extend Databricks' Postgres capabilities from the lakehouse to the edge

by Stas Kelvich, Yan Leshinsky, Nikita Shamgunov and Reynold Xin

Today, we’re excited to welcome Electric to Databricks. The world is building a new era of agentic applications which require distributed state and real-time data synchronization between teams of agents working in sandboxes. Electric is leading the way in pioneering data primitives purpose-built for agents: PGlite gives every agent its own lightweight Postgres right where it runs, providing ultra-low latency access to local context, and Electric’s real-time sync engine synchronizes distributed state back to a central Lakebase, enabling teams of agents to collaborate without losing track of shared context. That vision now continues at Databricks as we bring WASM Postgres to AI agent sandboxes, extending Databricks’ Postgres capabilities from the lakehouse to the edge.

Agents don’t behave like traditional applications
Developers are moving from building traditional apps to building agentic applications, and the assumptions underlying traditional infrastructure are shifting with them. A traditional application has a known shape: its queries are written in advance, its data access patterns are predictable, and it runs in a place its architects chose. One managed Postgres database serves it well.

The agents in agentic applications add a second surface. Alongside the durable, governed state every application needs, an agent generates a fast-moving context set. Agents differ in three ways that matter for a database:

  • They decide what data they need at runtime, deciding their next move and updating their context several times a second. That inner loop wants data in the same process; the results it produces belong in a durable store.
  • They run wherever the work is, often in sandboxed environments, where databases are only reachable via a network connection to the cloud.
  • They work in groups, requiring fast local context and a shared, current view of what others have done to avoid duplicating work, acting on stale state, or arriving at conflicting conclusions.

Introducing Electric
The Electric team built data primitives for the era of agentic applications.

They created PGlite to push Postgres to the edge, creating a WASM Postgres database small enough to run inside the application or agent itself - in an agent sandbox, browser tab, or user’s device - rather than on a separate server. PGlite has grown from 1M to 13M weekly downloads in just twelve months, enabling developers to build a new class of distributed Postgres applications and agents.

But local execution is just part of the challenge. Agents also need a shared, up-to-date view of changing information. Electric’s real-time sync engine continuously synchronizes data between distributed agents and centralized Lakebase infrastructure, enabling agents to securely share information with the definitive record in the cloud while keeping fast local context. The real-time sync architecture powers collaborative apps like Google Docs, Figma and Notion, and it turns out to be exactly what a fleet of agents needs to stay current while working in parallel.

Shared Postgres DNA
Both Electric and Lakebase are built on Postgres, the open-source database technology that has become the default foundation for AI agents. In fact, PGlite was built on the foundational WASM Postgres work of Stas Kelvich, who co-founded Neon. Electric took that proof of concept and turned it into the embeddable Postgres that millions of projects run every week. Bringing these teams together reunites two halves of the same idea and strengthens Databricks’ leadership at the center of innovation for modern databases as demand for agents accelerates.

Databricks + Electric
Lakebase delivers Postgres at production scale. PGlite brings WASM Postgres into the agent's own sandbox. Sync keeps the two consistent. By combining the power of Lakebase Postgres with the local execution of PGlite, agents get the lightweight databases they need to move fast and the infrastructure required to deploy at any scale.

For developers, this means:

  • Building collaborative, agentic applications on a single Postgres standard.
  • Running Postgres directly inside the agent sandbox, giving each agent a lightweight database it needs to move fast.
  • Keeping teams of agents in sync to instantly share context and act on fresh data, while centralizing control in Lakebase Postgres on cheap, durable object storage.

We’re thrilled to have the Electric team join Databricks, and can’t wait to see what our customers build.

Check out the Neon blog, and hear directly from Electric’s founders about why they’re excited to join forces.