Data & AI Practice
Data Lake Services
A data lake stores large volumes of raw and engineered data on open, low cost storage, ready for analytics, data science and AI. Erpvora builds governed lakes and lakehouses that avoid the swamp, applying structure, cataloguing and access control so the lake stays usable as it grows.
Build governed data lakes and lakehouses on open storage that hold raw and engineered data for analytics, data science and AI.
The business challenge
The classic failure of a data lake is the data swamp. Files land with no catalogue, no schema enforcement and no ownership, until nobody knows what is there, whether it is current, or who is allowed to use it.
Without governance and open table formats, lakes also struggle with reliability. Partial writes, schema changes and concurrent updates corrupt results, and teams cannot trust the lake enough to build serious workloads on it.
Our approach
We structure the lake into zones, from raw landing to cleaned and curated layers, with clear promotion rules. Open table formats provide transactions, schema enforcement and time travel, so the lake behaves reliably under concurrent use.
We catalogue and secure from day one. Every dataset has metadata, lineage and an access policy, so the lake is discoverable and governed rather than an opaque dumping ground, and it can serve both exploratory data science and production workloads.
Capabilities
- Zoned lake architecture from raw to curated
- Open table formats with transactions and schema enforcement
- Cataloguing, metadata and lineage
- Fine grained access control on lake data
- Ingestion of structured, semi structured and unstructured data
- Lakehouse serving for analytics and machine learning
How we deliver
- 01
Zone design
We define landing, cleaned and curated zones with clear rules for how data is promoted between them.
- 02
Table format
We adopt open table formats so the lake supports transactions, schema enforcement and time travel.
- 03
Ingest
We build ingestion for structured, semi structured and unstructured sources into the appropriate zones.
- 04
Catalogue and secure
We register datasets with metadata and lineage and apply fine grained access policies.
- 05
Serve workloads
We enable analytics and data science to consume curated data reliably from the lakehouse.
Typical use cases
- Storing high volume raw data cost effectively for later use
- Building a lakehouse that serves both analytics and data science
- Holding semi structured and unstructured data alongside tables
- Avoiding a data swamp through zones, cataloguing and governance
- Providing reliable data science inputs with versioned datasets
- Supporting AI workloads that need large, varied training data
Business impact
- Low cost storage for large and varied data
- Reliable reads and writes through open table formats
- Discoverable, catalogued and governed datasets
- One foundation for analytics, data science and AI
- Fine grained control over sensitive data in the lake
- A lake that stays usable rather than becoming a swamp
Frequently asked questions
What stops a lake becoming a swamp?
Governance from the start: zones with promotion rules, a catalogue with metadata and lineage, schema enforcement through open table formats, and access policies on every dataset.
Lake, warehouse or lakehouse?
A lakehouse combines open lake storage with warehouse style reliability and serving. For many enterprises that blend is the right answer, and we design the split around your workloads.
Can the lake hold unstructured data?
Yes. Lakes are well suited to documents, images, logs and other unstructured data alongside structured tables, which is valuable for AI workloads.
How is data in the lake secured?
We apply fine grained access control, cataloguing and lineage so sensitive data is protected and every access is governed and traceable.