DatriseAI-first ETL

Redtail CRM Amazon S3 Data Lake

AI-first ETL from Redtail CRM into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.

How Datrise loads Redtail CRM into Amazon S3 Data Lake

Datrise syncs Redtail CRM's contacts, accounts, deals, activities, and lifecycle events into Amazon S3 Data Lake as columnar Parquet objects partitioned per source entity. Flexible or custom fields land in nested struct/map fields in Parquet, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.

Sync is incremental: Datrise uses writes new date partitions and compacts small files on a schedule, so re-runs update only what changed. Hive-style path partitioning (entity/date) for engine-agnostic reads. A lake has no schema enforcement, so Datrise writes a schema manifest alongside the data to keep downstream engines consistent.

Ideal for an open, engine-neutral storage layer for Spark, Athena, Trino, or DuckDB.

Endpoints

Redtail CRM: Financial advisor CRM for clients, households, and compliance workflows.

Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.

How Redtail CRM entities map to Amazon S3 Data Lake

Redtail CRM entityAmazon S3 Data Lake objectNotes
contactsredtail_contactsid PK · custom fields → nested struct/map fields in Parquet
accountsredtail_accountsid PK · linked to redtail_contacts
dealsredtail_dealsid PK · linked to redtail_contacts
activitiesredtail_activitiesISO-8601 timestamp columns events

FAQ

How does Datrise handle Redtail CRM's custom fields in Amazon S3 Data Lake?

Flexible values are stored as nested struct/map fields in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon S3 Data Lake types.

How does the Redtail CRM to Amazon S3 Data Lake sync stay up to date?

It runs incrementally — Datrise uses writes new date partitions and compacts small files on a schedule.

Related pipelines

Early access

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