DatriseAI-first ETL

BoomTown to Amazon Athena Integration

AI-first ETL from BoomTown into Amazon Athena. Governed entities, incremental sync, typed landing tables.

How Datrise loads BoomTown into Amazon Athena

Datrise syncs BoomTown's leads, contacts, listings, opportunities, property alerts, and agent interactions into Amazon Athena as partitioned Parquet in S3 exposed as an Athena table. Flexible or custom fields land in struct/map columns in Parquet, and timestamps such as created, updated, and status changes are typed as timestamp.

Sync is incremental: Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog, so re-runs update only what changed. Hive-style partitioning by load date so Athena scans only new data. Athena bills per byte scanned and small files hurt, so Datrise compacts to right-sized Parquet rather than many tiny objects.

Ideal for serverless SQL over an S3 lake without a running warehouse.

Endpoints

BoomTown: Real estate lead generation with predictive agent alerts.

Amazon Athena: Serverless SQL over S3 data lake tables.

How BoomTown entities map to Amazon Athena

BoomTown entityAmazon Athena objectNotes
leadsboomtown_leadsid PK · custom fields → struct/map columns in Parquet
contactsboomtown_contactsid PK · linked to boomtown_leads
listingsboomtown_listingsid PK · linked to boomtown_leads
opportunitiesboomtown_opportunitiesid PK · linked to boomtown_leads

FAQ

How does Datrise handle BoomTown's custom fields in Amazon Athena?

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

How does the BoomTown to Amazon Athena sync stay up to date?

It runs incrementally — Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog.

Related pipelines

Early access

Connect BoomTown to Amazon Athena the easy way

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