Operational sources
Establish trusted inputs, ownership, and clear access boundaries.
TECHNOLOGY / DATA
Bring analytical and AI workloads onto a governed data foundation.
ARCHITECTURE GUIDE / 01HOW THE TECHNOLOGY CONNECTS
REFERENCE DESIGNTHE PRACTICAL VIEW
Fragmented warehouse and lake workloads need common quality, access, and lineage.
Batch ingestion, change data capture, curated data layers, open table formats, and workload isolation. The right design depends on data characteristics, operating constraints, risk, and the people who will maintain it.
Establish source contracts; land and validate data; publish curated datasets; monitor freshness and cost.
ARCHITECTURE RESPONSIBILITIES
Establish trusted inputs, ownership, and clear access boundaries.
Make this responsibility explicit, observable, and independently testable within the overall workflow.
Make this responsibility explicit, observable, and independently testable within the overall workflow.
Make this responsibility explicit, observable, and independently testable within the overall workflow.
Define acceptance checks and ownership of the resulting information or action.
TECHNOLOGY OPTIONS
Delta Lake, Apache Iceberg, Apache Spark, Databricks, Snowflake
Examples describe architectural options, not Travinto partnerships, certifications, or confirmed integrations.
Does the pattern solve a specific workflow problem?
Can access, quality, and source evidence be verified?
Can the team observe, recover, and manage the system?
Can usefulness and cost be evaluated against a baseline?
START WITH YOUR CONTEXT
Explore the opportunity, assess your current landscape, and agree a focused scope with clear success criteria.
Discuss Data lakehouse & open tables ↗