Integrate a highly-scalable data warehouse in a cloud-based analytics stack.
Store raw data in a structured format at petabyte scale in a data lake, and manage a curated data mart for BI and analytics.
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.
maximum time to create a new data source
the average number of sources for an analytics project
streaming through our network at all time
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.