DatriseAI-first ETL

Pipeliner CRM ClickHouse

AI-first ETL from Pipeliner CRM into ClickHouse. Governed entities, incremental sync, typed landing tables.

How Datrise loads Pipeliner CRM into ClickHouse

Datrise syncs Pipeliner CRM's visual pipeline records, account context, and sales execution activity into ClickHouse as a MergeTree table per source entity. Flexible or custom fields land in JSON or Map columns, and timestamps such as created, updated, and status changes are typed as DateTime64.

Sync is incremental: Datrise uses inserts into a ReplacingMergeTree keyed on stable id, so the latest version wins on merge, so re-runs update only what changed. Partition by month and order by (entity id, updated-at) for fast range scans. ClickHouse deduplicates asynchronously on merge, so Datrise uses ReplacingMergeTree and FINAL-safe queries rather than assuming immediate upserts.

Ideal for high-volume event analytics that need sub-second aggregation.

Endpoints

Pipeliner CRM: Visual pipeline CRM for complex sales motions.

ClickHouse: Columnar OLAP engine for fast aggregations.

How Pipeliner CRM entities map to ClickHouse

Pipeliner CRM entityClickHouse objectNotes
visual pipeline recordspipeliner_visual_pipeline_recordsid PK · custom fields → JSON or Map columns
account contextpipeliner_account_contextid PK · linked to pipeliner_visual_pipeline_records
sales execution activitypipeliner_sales_execution_activityDateTime64 events

FAQ

How does Datrise handle Pipeliner CRM's custom fields in ClickHouse?

Flexible values are stored as JSON or Map columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native ClickHouse types.

How does the Pipeliner CRM to ClickHouse sync stay up to date?

It runs incrementally — Datrise uses inserts into a ReplacingMergeTree keyed on stable id, so the latest version wins on merge.

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