Services / Data lakes
A foundation for data in every form.
Bring structured, semi-structured and unstructured data into a central repository for analytics.
Discuss your prioritiesBring structured, semi-structured and unstructured data into a central repository for analytics.
Keep the original formats
A data lake brings information together without requiring every source to share a fixed structure at ingestion. It can support dashboards, big data processing, real-time analytics and machine learning.
Build around the use case
Operational databases, business applications, mobile apps and connected devices can contribute different kinds of information. The useful design depends on who will access it and what they need to understand.
Plan cataloging and governance early
A “data swamp” develops when cataloging, quality and access controls are neglected, leaving information difficult to find or trust. Metadata management and partitioning also matter as a lake grows.
What to discuss
- Source formats and ingestion cadence
- Cataloging, ownership and access
- Analytics users and workload requirements
Before the first conversation.
What is a useful starting point?
A description of your data sources, the people who use them and the question the platform should help answer. Avoid sharing confidential datasets in an initial inquiry.
How are scope and timing confirmed?
Discuss the requirements directly with Dignitas. This preview does not offer a fixed package, delivery guarantee or price.