OPEN DATA LAKEHOUSE
Open lakehouse architecture for enterprise data
We build an open data lakehouse around your workloads, combining portable table formats, governed access and a phased migration from existing systems.
- Warehouse-grade transactions and schema on open, low-cost storage
- One governed copy read by BI, ML, and streaming alike
- Open table formats to reduce dependence on a single vendor
The lakehouse wins by refusing the compromise.
You get transactions, schema enforcement, and fast SQL on the same open files your data scientists and streaming jobs read directly. No second copy, no export tax, no format lock-in. The lakehouse is the substrate for the rest of the Data and Analytics practice, and it pairs with an Agentic Data Foundation to make that one governed copy ready for AI as well as reporting. The lakehouse works because of open table formats, Apache Iceberg, Delta Lake, and Apache Hudi, which add a metadata and transaction layer over plain files. Iceberg suits large analytic tables and broad engine support; Delta brings mature ACID deeply integrated with Spark and Databricks; Hudi handles streaming upserts and incremental processing. We match the format to your engines and portability needs, not a vendor default.
Data warehouse
Strong governance, ACID, and excellent SQL, but high cost at scale, usually a proprietary format, and limited streaming and data science. Governed and fast, but closed and expensive.
Data lake
Low cost and open format, good for ML, but weak governance, poor SQL, and streaming that has to be built and maintained by hand. Cheap and open, but ungoverned and hard to trust.
Open lakehouse
Strong governance and ACID, low cost, open format, excellent BI, good ML, and native streaming, all serving one copy for every workload. The warehouse's rigor on the lake's economics.
One governed copy for every workload. No export tax, no format lock-in.
Your open data lakehouse foundation.
- Table-format design, Iceberg, Delta, or Hudi, matched to your workloads and engines.
- Medallion architecture for refined, trustworthy bronze-to-gold data.
- A governance layer: catalog, lineage, access control, and quality on the lakehouse.
- Multi-engine access, so BI, ML, and streaming read one governed copy.
- A phased migration path from your existing warehouse or lake, built to de-risk.
Migrate in phases, validate as you go.
Assess
Map the current warehouse and lake estate, the workloads that run on it, and the engines your teams depend on.
Choose
Select the open table format that fits your workloads and portability goals, so the choice is not a permanent lock-in.
Build
Stand up the medallion architecture and governance on open storage, refining bronze to gold.
Enable
Open multi-engine access to one copy, retiring the redundant copies and pipelines that duplicated it.
Migrate
Move in phases, proving value on an anchor workload first, then broadening as trust is earned.
A layered lakehouse on open storage.
A representative stack by layer. We build cloud-native on the platform you already run and use your existing tooling where it is sound.
We build cloud-native on your platform, including Databricks, and feed it from data platform services.
The services that sit on the lakehouse.
Data Platforms
The platform services that provision, run, and scale the lakehouse across teams.
Agentic Data Foundation
The AI-ready layer that makes the lakehouse's one governed copy safe for agents.
Data Engineering
The pipelines that land and refine data into bronze, silver, and gold.
Data Streaming and Real-Time Analytics
Native streaming on the same open tables, no separate system to maintain.
Data Management and Governance
The catalog, lineage, and access controls that keep the one copy trustworthy.
Infrastructure and Platforms
The parent category this lakehouse practice belongs to.
What data leaders ask us first.
It depends on your engines and workloads: Iceberg for broad engine support and large analytic tables, Delta for deep Spark and Databricks integration, Hudi for streaming upserts. We match the format to your reality, not a vendor default.
No. Many organizations keep a warehouse for specific reporting and migrate broader workloads to the lakehouse in phases. Open formats let both coexist during the transition.
For most workloads, yes. It delivers warehouse governance and performance on open, low-cost storage, serving BI, ML, and streaming from one copy.
Build an open lakehouse that serves every workload from one governed, low-cost copy.
Open table formats, a medallion architecture, and governance, so BI, ML, and streaming read the same trusted data.