Data Manager

Serverless data warehouse designed for Big Data analytics.

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    All the data you love

    Store data for analytics at petabyte scale without managing infrastructure

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    No limit to projects

    Build data products without the cost, complexity, and development time

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    Enter the data era

    Turn data across databases and regions into actionable insights in seconds

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    Not a simple data warehouse

    Centralize and organize a poly-engine data warehouse in one unified interface

Who is this for?

Cloud Architects

Integrate a highly-scalable data warehouse in a cloud-based analytics stack.

Data Engineers

Store raw data in a structured format at petabyte scale in a data lake, and manage a curated data mart for BI and analytics.

Data Manager features


Store any data in a fully serverless cloud data warehouse

Data Manager is a serverless data warehouse and data lake, featuring a peta-scale high-performance engine as well as a unified governance and management layer. It offers a fully serverless experience and scales automatically to address clients with analytics or machine learning (ML) use cases requiring to store and process gigantic datasets, such as medical or IoT-type data. Storage of data and metadata is done in an open format (Apache Iceberg) in cloud-standard object storage and is fully decoupled from the compute power — which allows petabyte-level scalability, schema governance and support for safe concurrent read & write.

Organize datasets and attributes at the logical level

Using the management user interface, manage and access all tables, attributes and databases in the same way: at an abstracted logical level. In this unified framework, create, query, update and share datasets, manage data types, specify relationships, monitor lineage, and many other data management operations in a single collaborative environment. The production data environment is always protected as you work at the logical level, until changes are built at the physical level.


Query datasets across clouds, databases and regions

An ANSI-SQL interface is available to query datasets for ad-hoc analytics. ForePaaS Data Manager supports joining tables that are across different engines, and even regions, on the fly in the same query. A graphical user interface is available to perform all operations inside the product. Create new datasets and tables in DDL like you would do in any other service.

Remotely control external datasets powered by other engines

ForePaaS Data Manager is compatible with multiple different engines at once, including external DBMS such as PostgreSQL and Snowflake. This allows to create external datasets and span a data warehouse across multiple different storage engines to adapt to the use case efficiently – from the small curated data marts to the large-scale ML feature stores.


The ForePaaS difference


maximum time to create a new data source


the average number of sources for an analytics project


streaming through our network at all time

Our featured connectors

Power your pipeline with any data source

We support the format you use

Whether it is legacy or new data format, structured, semi-structured or unstructured, ForePaaS accepts them all. Our Data Manager includes both a data warehouse for classic relational databases and an object store whose flexibility allows you to store any type of data.

Ready to get started?

Contact us