Cloud-native lakehouse that unifies transactional and analytical processing
Data Lakehouse eliminates the need for separate OLTP and OLAP systems. Run real-time analytics on live transactional data with full SQL compatibility and elastic storage-compute separation.
Execute transactional writes and complex analytical queries on the same dataset simultaneously — no ETL pipelines, no stale data, no compromises.
Scale compute and storage independently. Spin up dedicated compute clusters for different teams or workloads without duplicating data.
Query freshly ingested data with sub-second latency. Materialized views and intelligent caching keep dashboards and reports always current.
Standard SQL with support for complex joins, window functions, CTEs, and stored procedures. Migrate existing workloads with minimal changes.
Stream or batch-load data from databases, APIs, message queues, and files into a unified storage layer.
The engine selects row or column storage, builds indexes, and partitions data based on actual query patterns — no manual tuning required.
Run point lookups, ad-hoc analytics, and scheduled reports against the same tables using standard SQL.
Add or remove compute nodes in seconds. Workload isolation ensures one team's heavy query never impacts another's performance.
One system replaces separate OLTP databases and OLAP warehouses, removing sync delays and consistency issues.
Analyze data the moment it arrives. No waiting for nightly ETL jobs to complete before making decisions.
Elastic scaling and unified storage mean you only pay for the resources you actually use.
Standard SQL compatibility and built-in connectors let teams start querying production data in minutes, not months.
Data Lakehouse is built on a cloud-native, shared-nothing architecture with separated storage and compute tiers. The storage layer uses a columnar format on object storage for cost efficiency, while compute nodes can be elastically provisioned per workload.
An in-cabin assistant improves only as fast as its training data. eBanma aggregates the fleet's daily voice and language volume onto MOI, standardised on a common event schema, with audio, transcripts, embeddings and labels in one engine — so a training corpus is queried rather than hunted for.
Extreme Vision built a unified AI feature data platform on MatrixOne, cutting model development cycles by 30% and boosting feature reuse by 40%.
Experience a warehouse that handles transactions and analytics in one engine. No ETL, no data duplication, no compromises.