Clear, owned outputs.
- Lake or lakehouse architecture
- Storage and table structure
- Ingestion into raw and refined layers
- Catalogue and lineage set-up
- Governance and retention policies
A data lake stores large volumes of raw data — structured, semi-structured and unstructured — at low cost. Without design it becomes a swamp. We build organized lakes and lakehouses with clear layers, open table formats, catalogues and governance, ready for analytics and machine learning.
The problems Data Lakes solves.
Scalable lake and lakehouse design with medallion layers, open tables and catalogues.
Files are dumped into storage with no structure or ownership.
Nobody can find or trust the data in the lake.
Machine learning teams need raw data the warehouse doesn't hold.
Storage and compute costs grow without control.
What Data Lakes covers.
Lake architecture
Storage layout, zones and naming conventions.
Medallion layers
Bronze, silver and gold layers from raw to business-ready.
Open table formats
Delta Lake and Apache Iceberg for reliable tables.
Cataloguing
Unity Catalog, Glue or Purview for discovery and lineage.
Security and governance
Access controls, encryption and data classification.
Lifecycle management
Retention, tiering and clean-up policies.
Lake, warehouse or lakehouse?
The terms overlap and vendors blur them further. The practical question is what kind of data you hold and who needs to use it.
| Pattern | Best when | Watch out for |
|---|---|---|
| Data lake | You keep large volumes of raw, semi-structured or unstructured data for data science and later use. | Without a catalogue and quality rules it becomes a swamp nobody trusts. |
| Data warehouse | Most use is reporting on structured, modelled data by analysts and BI tools. | Less suited to raw files, images or very large ML training sets. |
| Lakehouse | You want one copy of data for both BI and machine learning, using open table formats such as Delta Lake or Apache Iceberg. | Needs disciplined table design, compaction and access control to perform like a warehouse. |
Why it's worth it.
Flexibility
Store any data now and model it when it's needed.
Discoverability
Catalogues help people find and understand data.
AI-ready
Raw and refined data available for machine learning.
When Data Lakes is the right move.
- Combining logs, events and documents with transactional data.
- A lakehouse on Databricks for analytics and ML.
- Organizing an existing lake that has become hard to use.
Technologies & platforms
- Amazon S3
- Azure Data Lake Storage
- Google Cloud Storage
- Delta Lake
- Apache Iceberg
- Databricks Unity Catalog
- AWS Glue
- Microsoft Purview
How Data Lakes work runs.
- 01
Discover
Sources, consumers, data quality, volumes and the decisions the data must support.
- 02
Design
Architecture, data model, pipelines and governance agreed in writing.
- 03
Build
Pipelines, models and tests delivered in increments.
- 04
Validate
Reconciliation against source systems and sign-off by data owners.
- 05
Operate
Monitoring, data quality alerts, documentation and handover.
Data Lakes questions.
What is a lakehouse?
A lakehouse stores data in an open format on low-cost storage while adding warehouse-style features such as transactions, schemas and governance, so one platform serves BI and ML.
Delta Lake or Iceberg?
Both are open table formats. Delta Lake is native to Databricks; Iceberg has broad support across engines. We choose based on your platform and tools.
How do you stop a lake becoming a swamp?
With structure, ownership, cataloguing, quality checks and retention policies set up from the start.
Talk to us about data lakes.
Describe the environment and what needs to change. We'll reply with the questions we would answer first and a sensible first step.