Lakehouse Design
Delta Lake, Apache Iceberg, or Apache Hudi give you data lake scale with warehouse-style governance and query performance, organized in layers from raw to curated.
Database Solutions
Data Architecture Services
A Blueprint That Fits How Your Data Is Used
Our data architects design how data is stored, processed, and served across your organization, from operational databases to the lakehouse. The result is a technical blueprint sized for your workloads, your cloud platform, and the team that will run it.
What We Deliver
Where We Make the Difference
Architecture choices decide how fast data moves, how much it costs to keep, and how easily analytics and AI can use it. Changing them later is expensive. We start from real workload requirements and adopt patterns such as lakehouse, data mesh, or streaming only where they fit.
Domain Ownership Models
For large organizations with many domains, data mesh patterns hand ownership to domain teams while a shared catalog and central policies keep data findable and governed.
Event Streaming
Apache Kafka, Azure Event Hubs, or Amazon Kinesis carry business events as they happen, feeding operational systems and real-time analytics from the same stream.
Built for Your Cloud
Designs use the managed services of Azure, AWS, or Google Cloud where they reduce operating effort, and avoid them where lock-in or cost outweighs the benefit.
Our Approach
How We Deliver Data Architecture
Step 1: Catalog the Workloads
We document every major workload: how data arrives, how much is stored, how it is queried, how many users rely on it, and what service levels apply.
Step 2: Match Patterns to Needs
We weigh lakehouse, medallion, data mesh, Lambda, and Kappa patterns against your requirements and team skills, and explain why each is chosen or rejected.
Step 3: Draw the Detailed Design
We produce component diagrams, data flows, storage and compute design, security, and networking in enough detail for engineering teams to build from.
Step 4: Keep the Build on Plan
We set up architecture reviews and decision records so implementation stays consistent with the design as teams grow and requirements change.
Do we need a lakehouse, or is a warehouse enough?
If your data is mostly structured and your users mostly run BI reports, a well-designed warehouse is often enough. A lakehouse earns its place when you also have semi-structured data, long history, or machine learning workloads.
Is data mesh right for us?
Data mesh suits organizations with several strong domain teams that can own their data as a product. For smaller or more centralized teams it adds overhead, and we will tell you so.
Can the architecture work with the databases we already run?
Yes. We design around your existing operational databases and integration points, and plan changes in phases rather than asking you to replace everything at once.
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