Databricks' Electric acquisition adds embeddable PostgreSQL
Flush with funding, the data management and AI vendor continues to make purchases that add capabilities aimed at aiding users building and deploying agents.
Though not as historic as Bob Dylan's move away from the acoustic guitar at the 1965 Newport Folk Festival, Databricks on Aug. 11 revealed the acquisition of startup ElectricSQL to add new PostgreSQL database capabilities.
Financial terms of the deal were not disclosed. Electric raised $600,000 in pre-seed funding in 2022.
Databricks first added PostgreSQL database capabilities with its May 2025 acquisition of Neon, which Databricks has subsequently transformed into the core of its Lakebase platform. The acquisition of Electric allows Databricks to extend PostgreSQL database capabilities beyond data lakehouses to edge devices, including agents, additional AI applications and mobile devices.
Perhaps the biggest benefit of the acquisition is that Databricks will enable agents to more quickly and easily fix themselves when they take missteps, given that the data they need to do so will be more readily available than before, according to Mike Leone, an analyst at Moor Insights & Strategy.
"[The acquisition] definitely matters, but it's more of a specific fix than a big swing," he told TechTarget, adding that agents need to keep notes as they operate to see when they go wrong and correct any errors. "Electric shrank Postgres down small enough to run beside the agent instead of across a network. That makes it fast, and it lets the agent undo a step cleanly instead of leaving a half-finished mess."
Stephen Catanzano, an analyst at Omdia, a division of TechTarget, called Databricks' acquisition of Electric significant. Like Leone, he noted that it addresses a specific need in agentic AI development and deployment.
"The acquisition addresses a critical infrastructure gap in the emerging world of agentic applications, where AI agents need both ultra-low latency access to local data in sandboxed environments and real-time synchronization with centralized systems," he told TechTarget. "Electric … extends Databricks' existing Postgres capabilities from the centralized lakehouse to the edge where agents actually execute."
San Francisco-based Databricks' purchase of Electric is the latest in a series of acquisitions Databricks has made over the past few years to accelerate its expansion beyond data management into AI development.
Creating a competitive edge
PostgreSQL, an open source format for storing disparate data types, has emerged as a popular means of managing data for agents and other AI applications. By 2024, PostgreSQL had become the most popular database, ahead of MySQL, Microsoft SQL Server, MongoDB and Redis.
Versatility -- storing geospatial, time series, JSON and vector database workloads -- is one reason for PostgreSQL's popularity. A large, vibrant community that contributes to its evolution is another.
The acquisition addresses a critical infrastructure gap in the emerging world of agentic applications, where AI agents need both ultra-low latency access to local data in sandboxed environments and real-time synchronization with centralized systems.
Stephen CatanzanoAnalyst, Omdia
Given PostgreSQL's growing role in the AI pipeline, Databricks, Snowflake and Redpanda all acquired PostgreSQL database capabilities in 2025. Now, by adding Electric's embeddable PostgreSQL databases, Databricks is extending such capabilities beyond traditional data and AI pipelines. In addition, Electric founders James Arthur and Kyle Matthews are joining Databricks as part of the acquisition.
Based in Berkeley, Calif., Electric is the developer of PGlite, a lightweight WebAssembly (WASM) version of a PostgreSQL database small enough to be embedded into agents and other applications. With PGlite embedded, applications can access appropriate context where they run rather than having to call back to traditional databases and other data repositories.
In addition, Electric provides a real-time sync engine that connects data between agents and a centralized PostgreSQL architecture so that agents can access an organization's data of record in the cloud, as well as the local context that enables them to deliver outputs based on relevant data.
Once Electric's capabilities are integrated with Lakebase, Databricks users will be able to deploy swarms of agents that stay current while working alongside one another.
"Lakebase already made Postgres fast for agents," Devin Pratt, an analyst at IDC, told TechTarget. "Electric moves it the last few inches, right into the sandbox itself."
Meanwhile, though Databricks competitors such as AWS, Snowflake and Microsoft provide PostgreSQL databases, the acquisition of Electric adds embeddable PostgreSQL capabilities that distinguish Databricks from its peers, Pratt continued.
"Snowflake bought its Neon, [but] nobody's bought their Electric yet," he said. "That's the gap Databricks just closed on itself."
Catanzano similarly noted that adding Electric's capabilities will help Databricks stand apart from competitors. While other providers offer managed PostgreSQL and data synchronization services, they are designed for traditional database workloads rather than the unique requirements of agents that access the data they need at runtime, operate in distributed sandboxed environments, and require both individualized context and a shared state.
"Electric's combination of embeddable WASM Postgres with real-time sync architecture specifically designed for agent collaboration represents a more specialized solution for this emerging use case, positioning Databricks ahead of the curve … rather than simply keeping pace," Catanzano said.
Leone, however, noted that while Electric's capabilities provide some differentiation, they aren't completely unique. For example, startup Turso similarly provides each agent with its own small database.
"What's less common is Databricks insisting the small copy next to the agent be the exact same database as the big one in the middle, not a lookalike," he said.
Additive acquisitions
While acquiring Electric adds capabilities that help distinguish Databricks among the vendors racing to add tools that help customers build AI tools, it's just the latest in a long line of acquisitions Databricks has made over the past few years to quickly add capabilities that fuel AI development.
In addition to Neon and Electric, Databricks acquisitions include MosaicML, which forms the foundation of the Mosaic AI development platform, along with Arcion, BladeBridge, Einblick, Lilac AI, Mooncake and Tabular to add capabilities that complement Mosaic AI and Lakebase.
"They've been incredibly smart, and unusually consistent about it," Leone said. "Databricks buys things it would otherwise spend years building itself, and it buys the people along with the technology, which is why the same names keep showing up. Electric fits that perfectly."
In Electric's case, engineering a full database to run in a small workspace such as an agent's sandbox is different than engineering a database to run in the cloud, he continued.
"Electric has already solved it," Leone said. "The Electric team is joining the same group that came over with Neon, so that's two database companies now folded into one team on purpose."
Pratt likewise noted that Databricks' acquisition strategy has been effective to date. In particular, MosaicML and Neon have become key parts of the vendor's platform, though Tabular's capabilities, which added support for Apache Iceberg storage, have been slower to integrate, he continued.
"Databricks isn't buying new products, it's buying pieces that already have a home waiting for them," Pratt said.
Looking ahead
With Databricks continuing to raise capital -- the vendor revealed on Thursday that it added $5 billion in venture capital funding that would bring its total funding to over $30 billion -- more acquisitions are possible.
Leone, however, advised Databricks to concentrate at least some resources on delivering new capabilities it publicly revealed in recent months, but hasn't yet made generally available. In addition, making its growing platform easier to navigate would be wise, Leone continued.
"A lot of what Databricks announced this year is still labeled beta or preview, meaning it isn't fully released," he said. "Getting that work done is worth more to current customers than another round of new features. I'd also want them to say plainly which tool to use for which job, because the platform has gotten big enough that its size is now the problem."
Pratt, meanwhile, suggested that Databricks continue to ensure that the capabilities it is acquiring get integrated in a manner that makes them both additive as well as easy to use with the rest of the vendor's platform.
"Neon to Lakebase took about a year -- that's the pace the rest of the portfolio should be held to," he said.
Eric Avidon is a senior news writer for Informa TechTarget and a journalist with more than three decades of experience. He covers analytics and data management.
