# Memgraph Zero

**Zero ETL. Query data where it lives.**

Memgraph Zero is a product line from Memgraph built on a simple idea: your data
should stay where it is, and you should still be able to query it as a graph.
No Extract, Transform, Load pipelines. No moving terabytes into a new silo. No
waiting for batch jobs to finish before you can ask a question.

Instead, Memgraph Zero connects directly to the systems you already run: graph
databases, relational warehouses, data lakes, and streaming platforms, and lets
you query across them using standard graph query languages. You get graph
intelligence without the engineering tax of maintaining another data pipeline.

> **Info**
>
> Help shape the Memgraph Zero roadmap — [share your feedback in our short
>   survey](https://forms.gle/2MLfWp24uwbJpsey8).

## Powered by MemGQL

The first component of Memgraph Zero is **[MemGQL](https://memgraph.com/docs/memgraph-zero/memgql)**, a
federated GQL query engine that translates ISO-standard graph queries into the
native languages of each backend. Whether your data lives in PostgreSQL,
ClickHouse, DuckDB, or Iceberg, you name it, MemGQL speaks the protocol, pushes
down the work, and returns unified results. One query layer. No new language to
learn. No data movement required.

MemGQL is just the beginning. Memgraph Zero is designed as an extensible
platform. Several advanced capabilities are already available through MemGQL,
with additional components for real-time graph computation coming in future
releases.

## Use Cases

Memgraph Zero solves specific problems that teams face when data is scattered
across systems and compliance boundaries:

- **[Public-Private Data](https://memgraph.com/docs/memgraph-zero/memgql/use-cases/public-private)** —
  Keep sensitive data sovereign while querying it alongside public knowledge
  graphs. Private customer records stay in PostgreSQL; public catalogs live in
  Memgraph. One GQL query joins both without moving regulated data.

- **[Enterprise Context Sharing](https://memgraph.com/docs/memgraph-zero/memgql/use-cases/enterprise-context)** —
  Share canonical context across departments without forcing every team into the
  same database.

- **[Distributed Compute](https://memgraph.com/docs/memgraph-zero/memgql/use-cases/distributed)** —
  Spread graph computation across multiple nodes for workloads that exceed a
  single instance.

- **[Agentic Data Access](https://memgraph.com/docs/memgraph-zero/memgql/use-cases/agentic)** —
  Give AI agents a single semantic layer to discover and query any data in the
  organization using standard GQL.

See all [use cases](https://memgraph.com/docs/memgraph-zero/memgql/use-cases) for working examples and
Docker Compose setups you can run yourself.

## Why Zero ETL matters

The industry has spent the last decade building ever-more-complex data
pipelines. Memgraph Zero takes the opposite approach: connect, don't collect.
Query in place, don't copy. This means:

- **No pipeline maintenance** - eliminate the engineering hours spent keeping
  ETL jobs alive
- **No data staleness** - query the live source, not yesterday's extract
- **No storage bloat** - stop duplicating data into yet another warehouse
- **Faster time to insight** - ask graph questions across your existing
  infrastructure today

Memgraph Zero enters a market category that is already primed for this
approach. The difference is in the execution: a real-time graph engine, GQL
standards compliance, and a query layer that unifies relational, graph, and
lakehouse systems under one familiar interface.