DatriseAI-first ETL

HubSpot Service Hub Amazon S3 Data Lake

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

How Datrise loads HubSpot Service Hub into Amazon S3 Data Lake

Datrise syncs HubSpot Service Hub'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

HubSpot Service Hub: Customer service platform with ticket and conversation context.

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

How HubSpot Service Hub entities map to Amazon S3 Data Lake

HubSpot Service Hub entityAmazon S3 Data Lake objectNotes
contactshubspot_service_contactsid PK · custom fields → nested struct/map fields in Parquet
accountshubspot_service_accountsid PK · linked to hubspot_service_contacts
dealshubspot_service_dealsid PK · linked to hubspot_service_contacts
activitieshubspot_service_activitiesISO-8601 timestamp columns events

FAQ

How does Datrise handle HubSpot Service Hub'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 HubSpot Service Hub 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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