Azure Table Storage to Amazon S3 Data Lake Integration
AI-first ETL from Azure Table Storage into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Azure Table Storage into Amazon S3 Data Lake
Datrise syncs Azure Table Storage's tables, entities, partition keys, row keys, properties, and timestamps into Amazon S3 Data Lake as columnar Parquet objects partitioned 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 and compacts small files on a schedule, so re-runs update only what changed. Hive-style path partitioning (entity/date) for engine-agnostic reads. A lake has no schema enforcement, so Datrise writes a schema manifest alongside the data to keep downstream engines consistent.
Ideal for an open, engine-neutral storage layer for Spark, Athena, Trino, or DuckDB.
Endpoints
Azure Table Storage: Key-value NoSQL store on Azure.
Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.
How Azure Table Storage entities map to Amazon S3 Data Lake
| Azure Table Storage entity | Amazon S3 Data Lake object | Notes |
|---|---|---|
| tables | azure_table_storage_tables | id PK · custom fields → nested struct/map fields in Parquet |
| entities | azure_table_storage_entities | id PK · linked to azure_table_storage_tables |
| partition keys | azure_table_storage_partition_keys | id PK · linked to azure_table_storage_tables |
| row keys | azure_table_storage_row_keys | id PK · linked to azure_table_storage_tables |
FAQ
How does Datrise handle Azure Table Storage's custom fields in Amazon S3 Data Lake?
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 Amazon S3 Data Lake types.
How does the Azure Table Storage to Amazon S3 Data Lake sync stay up to date?
It runs incrementally — Datrise uses writes new date partitions and compacts small files on a schedule.
Related pipelines
More destinations for Azure Table Storage
- Azure Table Storage → Azure Data Lake Storage
- Azure Table Storage → Azure Synapse
- Azure Table Storage → Spreadsheets
- Azure Table Storage → Airtable
- Azure Table Storage → CSV Files
- Azure Table Storage → MongoDB
- Azure Table Storage → Supabase
- Azure Table Storage → Neon
- Azure Table Storage → PlanetScale
- Azure Table Storage → Amazon DynamoDB
- Azure Table Storage → Looker
- Azure Table Storage → Looker Studio
More sources for Amazon S3 Data Lake
- Azure Blob Storage → Amazon S3 Data Lake
- Babelforce → Amazon S3 Data Lake
- BigCommerce → Amazon S3 Data Lake
- Google BigQuery → Amazon S3 Data Lake
- Branch → Amazon S3 Data Lake
- Braze → Amazon S3 Data Lake
- Bronto → Amazon S3 Data Lake
- CallRail → Amazon S3 Data Lake
- Campaign Monitor → Amazon S3 Data Lake
- Cart → Amazon S3 Data Lake
- ChartMogul → Amazon S3 Data Lake
- CircleCI → Amazon S3 Data Lake
Early access
Connect Azure Table Storage to Amazon S3 Data Lake the easy way
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