LivePerson to Redash Integration

How Datrise loads LivePerson into Redash

Datrise syncs LivePerson's conversations, agents, skills, messages, surveys, and engagement metrics into Redash as SQL tables Redash queries and visualizes. Flexible or custom fields land in flattened columns for query results, and timestamps such as created, updated, and status changes are typed as temporal columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts for scheduled queries. Redash caches query results on a schedule, so Datrise keeps tables incrementally fresh so cached dashboards reflect reality.

Ideal for lightweight, query-driven dashboards.

Endpoints

LivePerson: Messaging-first customer conversations and bots.

Redash: Open-source SQL client for queries, visualizations, and dashboards.

How LivePerson entities map to Redash

LivePerson entity	Redash object	Notes
conversations	`liveperson_conversations`	id PK · custom fields → flattened columns for query results
agents	`liveperson_agents`	id PK · linked to liveperson_conversations
skills	`liveperson_skills`	id PK · linked to liveperson_conversations
messages	`liveperson_messages`	id PK · linked to liveperson_conversations

FAQ

How does Datrise handle LivePerson's custom fields in Redash?

Flexible values are stored as flattened columns for query results, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Redash types.

How does the LivePerson to Redash sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

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