DatriseAI-first ETL

Hellobaton to Amazon Athena Integration

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

How Datrise loads Hellobaton into Amazon Athena

Datrise syncs Hellobaton's projects, tasks, milestones, companies, users, and time entries 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

Hellobaton: Client onboarding and implementation project tracking.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Hellobaton entities map to Amazon Athena

Hellobaton entityAmazon Athena objectNotes
projectshellobaton_projectsid PK · custom fields → struct/map columns in Parquet
taskshellobaton_tasksid PK · linked to hellobaton_projects
milestoneshellobaton_milestonesid PK · linked to hellobaton_projects
companieshellobaton_companiesid PK · linked to hellobaton_projects

FAQ

How does Datrise handle Hellobaton'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 Hellobaton 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

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