DatriseAI-first ETL

JobNimbus Amazon Athena

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

How Datrise loads JobNimbus into Amazon Athena

Datrise syncs JobNimbus's contacts, accounts, deals, activities, and lifecycle events 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

JobNimbus: Construction CRM for jobs, estimates, and contractor pipelines.

Amazon Athena: Serverless SQL over S3 data lake tables.

How JobNimbus entities map to Amazon Athena

JobNimbus entityAmazon Athena objectNotes
contactsjobnimbus_contactsid PK · custom fields → struct/map columns in Parquet
accountsjobnimbus_accountsid PK · linked to jobnimbus_contacts
dealsjobnimbus_dealsid PK · linked to jobnimbus_contacts
activitiesjobnimbus_activitiestimestamp events

FAQ

How does Datrise handle JobNimbus'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 JobNimbus 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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