Amazon DynamoDB to Azure Data Lake Storage Integration
AI-first ETL from Amazon DynamoDB into Azure Data Lake Storage. Governed entities, incremental sync, typed landing tables.
How Datrise loads Amazon DynamoDB into Azure Data Lake Storage
Datrise syncs Amazon DynamoDB's tables, items, partition keys, sort keys, attributes, and stream records 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
Amazon DynamoDB: Serverless key-value and document store on AWS.
Azure Data Lake Storage: ADLS Gen2 object storage for analytics workloads.
How Amazon DynamoDB entities map to Azure Data Lake Storage
| Amazon DynamoDB entity | Azure Data Lake Storage object | Notes |
|---|---|---|
| tables | source_dynamodb_tables | id PK · custom fields → nested struct/map fields in Parquet |
| items | source_dynamodb_items | id PK · linked to source_dynamodb_tables |
| partition keys | source_dynamodb_partition_keys | id PK · linked to source_dynamodb_tables |
| sort keys | source_dynamodb_sort_keys | id PK · linked to source_dynamodb_tables |
FAQ
How does Datrise handle Amazon DynamoDB'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 Amazon DynamoDB 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 Amazon DynamoDB
- Amazon DynamoDB → Azure Synapse
- Amazon DynamoDB → Spreadsheets
- Amazon DynamoDB → Airtable
- Amazon DynamoDB → CSV Files
- Amazon DynamoDB → MongoDB
- Amazon DynamoDB → Supabase
- Amazon DynamoDB → Neon
- Amazon DynamoDB → PlanetScale
- Amazon DynamoDB → Amazon DynamoDB
- Amazon DynamoDB → Looker
- Amazon DynamoDB → Looker Studio
- Amazon DynamoDB → Microsoft Power BI
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- Taboola → Azure Data Lake Storage
- Tempo → Azure Data Lake Storage
- Teradata → Azure Data Lake Storage
- The Guardian API → Azure Data Lake Storage
- TiDB → Azure Data Lake Storage
- TikTok Ads → Azure Data Lake Storage
- Timely → Azure Data Lake Storage
Early access
Connect Amazon DynamoDB to Azure Data Lake Storage the easy way
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