Analytics that scale independently of your production systems
Traditional ETL tools pull data directly from your databases, consuming 2–3 CPUs on the source system. And when competitors demo their solutions, they show 2–3 tables working nicely. But real enterprise environments have thousands of tables; Oracle E-Business Suite alone has thousands.
We take a different approach. Our team deploys a purpose-built lakehouse on your infrastructure, powered by our open-source CDC technology. Near real-time replication captures changes with minimal impact on your databases, with no heavy ETL consuming resources. Data flows continuously into optimized lakehouse storage, so your teams make decisions based on what is happening right now, not what happened yesterday.
We have battle-tested this with Oracle E-Business Suite environments with thousands of tables: not a 2–3 table demo, but real enterprise-scale deployments. Our CDC engine handles the edge cases that break other solutions, such as partial rollbacks, Oracle internal transactions, multi-byte character sets, and data types competitors simply skip. Reports that struggled against your RDBMS can run 10–100× faster against the lakehouse.
Five stages, one pipeline
Connect your databases
We configure near real-time CDC replication from Oracle, Oracle EBS, and other databases with minimal impact on your source systems.
Data lands in your lakehouse
Changes stream continuously into the right store: open table formats on object storage for large analytical tables, a columnar store for aggregates, a relational store for smaller reference tables.
Offload your analytics
Reports and queries run against the lakehouse instead of your production databases. Slow reports become 10–100× faster.
Define your metrics once
The semantic layer, including purpose-built definitions for Oracle EBS, ensures everyone uses the same business terms.
Explore with AI
Ask questions in natural language and get answers backed by your real data, updated in near real-time.
A complete lakehouse, built, deployed, and managed by our team
| No. | Component | Scope |
|---|---|---|
| 01 | Near real-time CDC | Changes are captured from your databases and streamed continuously into the lakehouse. No batch jobs, no stale data: decisions are based on what is happening right now. |
| 02 | Hybrid lakehouse storage | Data lands in the right store for the job: open table formats on object storage for large analytical tables, a columnar (OLAP) store for fast aggregates, and a relational (OLTP) store for smaller reference tables. All open source, and every major analytics engine can read it. |
| 03 | Federated SQL | Query all your data, lakehouse tables, databases, and external sources, through a single SQL interface. No data movement, no duplication. Reports that took minutes in your RDBMS run 10–100× faster against optimized lakehouse storage. |
| 04 | Semantic layer | Define business metrics, dimensions, and relationships once. Analysts, engineers, and executives work from the same definitions. Includes a purpose-built semantic layer for Oracle E-Business Suite. |
| 05 | AI data exploration | Explore your data in natural language. Ask questions, discover patterns, and generate insights, powered by AI that understands your semantic layer and business context. |
| 06 | On-premises or cloud | Deploy the entire platform in your data center, your cloud account, or a hybrid of both. Same platform, same capabilities, your choice of environment. |
One shared definition of every number
The same meaning for people and for the AI. Metrics and dimensions are defined once, owned, and certified, so dashboards, the AI assistant, and board reports read the exact same figure.
From visibility to investigation
Trusted reporting shows what happened and what changed. The agentic AI layer begins where it leaves off: why it happened, where, what evidence supports the conclusion, and what to investigate next. Not another dashboard, but a governed investigation over the certified numbers your dashboards already use.
What changes when the data is current
| Traditional ETL | A2 Lakehouse Solution |
|---|---|
| Batch jobs run nightly; stale data by morning | Near real-time CDC; data is always current |
| Heavy extraction consumes 2–3 CPUs on the source | Minimal impact on source databases |
| Demo looks great with 2–3 tables | Battle-tested with thousands of EBS tables |
| Breaks down at enterprise scale | Built for enterprise scale from day one |
| Decisions based on yesterday's data | Decisions based on what's happening now |
Every lakehouse we deliver is powered by open-source components we created and maintain: oracdc and ora2iceberg. You get the reliability of battle-tested open-source software, deployed and managed by the team that built it.
See it in action
Schedule a demo and we'll walk you through the platform with your use case in mind. Not a 3-table demo, but a real discussion about your environment.