Amazon DynamoDB to Amazon S3 Data Lake Integration
AI-first ETL from Amazon DynamoDB into Amazon S3 Data Lake. Governed entities, incremental sync, typed landing tables.
How Datrise loads Amazon DynamoDB into Amazon S3 Data Lake
Datrise syncs Amazon DynamoDB's tables, items, partition keys, sort keys, attributes, and stream records 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
Amazon DynamoDB: Serverless key-value and document store on AWS.
Amazon S3 Data Lake: Object storage landing zone for parquet and snapshots.
How Amazon DynamoDB entities map to Amazon S3 Data Lake
| Amazon DynamoDB entity | Amazon S3 Data Lake 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 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 Amazon DynamoDB 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 Amazon DynamoDB
- Amazon DynamoDB → Azure Data Lake Storage
- 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
More sources for Amazon S3 Data Lake
- SparkPost → Amazon S3 Data Lake
- StackAdapt → Amazon S3 Data Lake
- Statuspage → Amazon S3 Data Lake
- SurveyCTO → Amazon S3 Data Lake
- SurveySparrow → Amazon S3 Data Lake
- Taboola → Amazon S3 Data Lake
- Tempo → Amazon S3 Data Lake
- Teradata → Amazon S3 Data Lake
- The Guardian API → Amazon S3 Data Lake
- TiDB → Amazon S3 Data Lake
- TikTok Ads → Amazon S3 Data Lake
- Timely → Amazon S3 Data Lake
Early access
Connect Amazon DynamoDB to Amazon S3 Data Lake the easy way
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