DatriseAI-first ETL

MyCase to Google BigQuery Integration

AI-first ETL from MyCase into Google BigQuery. Governed entities, incremental sync, typed landing tables.

How Datrise loads MyCase into Google BigQuery

Datrise syncs MyCase's cases, clients, leads, time entries, invoices, and documents into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

MyCase: Case management and client intake for law firms.

Google BigQuery: Serverless analytics warehouse on GCP.

How MyCase entities map to Google BigQuery

MyCase entityGoogle BigQuery objectNotes
casesmycase_casesid PK · custom fields → JSON or nested/repeated (STRUCT) columns
clientsmycase_clientsid PK · linked to mycase_cases
leadsmycase_leadsid PK · linked to mycase_cases
time entriesmycase_time_entriesid PK · linked to mycase_cases

FAQ

How does Datrise handle MyCase's custom fields in Google BigQuery?

Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.

How does the MyCase to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

Early access

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