DatriseAI-first ETL

Insightly Amazon S3 Data Lake

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

How Datrise loads Insightly into Amazon S3 Data Lake

Datrise syncs Insightly's contacts, organizations, opportunities, projects, and delivery milestones 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

Insightly: CRM and lightweight project delivery.

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

How Insightly entities map to Amazon S3 Data Lake

Insightly entityAmazon S3 Data Lake objectNotes
contactsinsightly_contactsid PK · custom fields → nested struct/map fields in Parquet
organizationsinsightly_organizationsid PK · linked to insightly_contacts
opportunitiesinsightly_opportunitiesid PK · linked to insightly_contacts
projectsinsightly_projectsid PK · linked to insightly_contacts

FAQ

How does Datrise handle Insightly'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 Insightly 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

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