Apollo → Azure Data Lake Storage
AI-first ETL from Apollo into Azure Data Lake Storage. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apollo into Azure Data Lake Storage
Datrise syncs Apollo's sales intelligence records, account engagement, and outbound activity into Azure Data Lake Storage as partitioned Parquet in ADLS Gen2 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 to the container and compacts on a schedule, so re-runs update only what changed. Hive-style partitioning by load date, readable by Synapse and Databricks. ADLS hierarchical namespace makes folder layout matter, so Datrise keeps a predictable entity/date path your Azure engines mount directly.
Ideal for Azure lakehouse storage shared across Synapse and Databricks.
Endpoints
Apollo: Sales intelligence and engagement platform with account-level activity.
Azure Data Lake Storage: ADLS Gen2 object storage for analytics workloads.
How Apollo entities map to Azure Data Lake Storage
| Apollo entity | Azure Data Lake Storage object | Notes |
|---|---|---|
| sales intelligence records | apollo_sales_intelligence_records | id PK · custom fields → nested struct/map fields in Parquet |
| account engagement | apollo_account_engagement | id PK · linked to apollo_sales_intelligence_records |
| outbound activity | apollo_outbound_activity | ISO-8601 timestamp columns events |
FAQ
How does Datrise handle Apollo's custom fields in Azure Data Lake Storage?
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 Azure Data Lake Storage types.
How does the Apollo to Azure Data Lake Storage sync stay up to date?
It runs incrementally — Datrise uses writes new date partitions to the container and compacts on a schedule.
Related pipelines
More destinations for Apollo
More sources for Azure Data Lake Storage
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Early access
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