Zero-ETL Warehouse for Fast Teams

Cut months of pipeline work. Automatically connect any data source and query with psql.
BemiDB
Built using firsthand insights from top tech companies
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Zero engineering fuss

1-click cloud analytics

Connect and automatically sync all your data sources to a scalable data analytics platform without any engineering complexity.

All-in-one serverless platform

Fast, scalable, and cost-effective

Automatically centralize data from any source in a single platform. Leverage the optimized analytics query engine with predictable usage-based pricing.

Works with Postgres ecosystem

Connect your favorite database libraries, BI dashboards, and notebooks. No new new setup or SQL syntax.

No vendor lock-in

Store everything in open table format. Bring Iceberg-compatible big-data tools to access your data when needed.

Use cases

Powerful analytics

Centralized data store
BI and ad-hoc queries
Postgres in-app analytics
Centralize data without pipelines
Centralize data without pipelines
Combine data from multiple sources into one unified store. Simplify your analytics and reporting with a consolidated and unlimited dataset.
Testimonials

Join the community

One of the fastest-growing open source data analytics projects
  • "After some initial trials that has proven BemiDB to be simply awesome, I was looking forward to try it out in the wild."
    Grzegorz Brzezinka
  • "Query Engine: embeds the DuckDB to run analytical queries. Storage Layer: uses the Iceberg table format to store data in columnar compressed Parquet files. Smart."
    Alex N.
  • "This is probably the most streamlined/all-inclusive solution out of all that I've seen."
    Dan Goodman
  • "Really cool! I have an IoT use-case where I ingest data, I want to keep like the last 3 months or so in Postgres, and then store the old data as parquet files on S3."
    oulipo
  • "After some initial trials that has proven BemiDB to be simply awesome, I was looking forward to try it out in the wild."
    Grzegorz Brzezinka
  • "Query Engine: embeds the DuckDB to run analytical queries. Storage Layer: uses the Iceberg table format to store data in columnar compressed Parquet files. Smart."
    Alex N.
  • "This is probably the most streamlined/all-inclusive solution out of all that I've seen."
    Dan Goodman
  • "Really cool! I have an IoT use-case where I ingest data, I want to keep like the last 3 months or so in Postgres, and then store the old data as parquet files on S3."
    oulipo
  • "After some initial trials that has proven BemiDB to be simply awesome, I was looking forward to try it out in the wild."
    Grzegorz Brzezinka
  • "Query Engine: embeds the DuckDB to run analytical queries. Storage Layer: uses the Iceberg table format to store data in columnar compressed Parquet files. Smart."
    Alex N.
  • "This is probably the most streamlined/all-inclusive solution out of all that I've seen."
    Dan Goodman
  • "Really cool! I have an IoT use-case where I ingest data, I want to keep like the last 3 months or so in Postgres, and then store the old data as parquet files on S3."
    oulipo
  • "After some initial trials that has proven BemiDB to be simply awesome, I was looking forward to try it out in the wild."
    Grzegorz Brzezinka
  • "Query Engine: embeds the DuckDB to run analytical queries. Storage Layer: uses the Iceberg table format to store data in columnar compressed Parquet files. Smart."
    Alex N.
  • "This is probably the most streamlined/all-inclusive solution out of all that I've seen."
    Dan Goodman
  • "Really cool! I have an IoT use-case where I ingest data, I want to keep like the last 3 months or so in Postgres, and then store the old data as parquet files on S3."
    oulipo
FAQ

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