Glossary

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Glossary / Evaluation and implementation guide

OLTP (Online Transaction Processing)

OLTP (Online Transaction Processing) describes databases built for many small, fast transactions: create a contact, update a deal stage, log an order. They store full rows together and optimize for concurrent writes and point lookups.

Analytical attribution queries that aggregate millions of rows are the opposite workload, usually called OLAP.

A practical example

Example: a sales rep updates an opportunity and the CRM commits in milliseconds.

The same database asked to join a year of deals with web sessions across millions of rows slows for every user, because that is not what it is built for.

What to evaluate before investing

  • Ask vendors whether reporting is explicitly discouraged on the transactional database, and what concurrency limits apply.
  • Check that your integration extracts data on a schedule or stream instead of querying the CRM directly for analytics.
  • Confirm the CRM's API and query layer are not your planned path for large historical aggregations.

Limitations and tradeoffs

Extracting to a warehouse adds latency between a CRM change and its appearance in reports, which your dashboard expectations must account for.

Plan your next step with MeshLine

Connect this decision to your automation, organic marketing and customer lifecycle management. In a MeshLine demo, discuss your existing tools, the scope you need and how to measure the result.