Athena can query DynamoDB data in two main ways: use its DynamoDB federated connector to run SQL against a table, or export the table to Amazon S3 and query the resulting files. Use the connector when direct access suits the workload and scan costs are acceptable; use an export when you need a repeatable snapshot or an analytical dataset. For near-real-time change capture, AWS recommends DynamoDB Streams or Kinesis Data Streams instead.
Choose the right way to query DynamoDB
| Approach | What it does | Best fit | Main trade-off |
|---|---|---|---|
| Athena DynamoDB connector | Runs federated SQL queries against a DynamoDB table through a Lambda-based connector. | Direct queries, including workflows that join DynamoDB data with other sources. | Setup and permissions are required; scan-heavy queries can consume DynamoDB read capacity and become costly. |
| DynamoDB export to S3, then Athena | Writes a full snapshot or incremental changes to S3 for analysis. | Repeatable snapshots or analytical datasets that do not need live table access. | Requires point-in-time recovery (PITR); exports are asynchronous, and S3 storage and request charges are additional. |
| Streams or Kinesis Data Streams | Captures table changes for downstream processing. | Near-real-time change-data capture (CDC). | Requires a downstream integration and consumer planning. |
The practical decision is about freshness and workload, not a universal winner. If you need SQL against the table as it exists now, consider the connector and estimate the effect of scans. If you can analyze exported data, S3 separates the export from the query workload. If changes must flow downstream near real time, use a change-capture approach rather than repeatedly scanning the table.
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Query a DynamoDB table directly with Athena
Athena’s DynamoDB connector uses federated queries: Athena submits work through a Lambda function, and the connector reads data from DynamoDB. AWS Prescriptive Guidance also describes joining DynamoDB data with other sources. This path is useful when SQL access to the table matters more than creating a separate analytical copy.
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Set up access and query results storage
Plan the permissions and supporting resources before running queries. The connector needs permission to read DynamoDB and to access the AWS Glue Data Catalog. It also needs write access to an S3 bucket to spill results from large queries; Athena query results likewise require an S3 destination. Follow the Athena DynamoDB connector documentation and AWS Prescriptive Guidance for querying DynamoDB tables with Athena for the connector’s deployment and configuration details.
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Keep scans narrow
Federated queries can consume DynamoDB read capacity when they scan data. The connector supports parallel scans and attempts predicate pushdown, but that does not make broad queries free. AWS warns that full table scans on tables larger than a few gigabytes can incur high cost. Start with a constrained query, use a LIMIT where it is appropriate, and check whether simple predicates are pushed down before running broad analysis. A LIMIT or filter may reduce scanned data and execution time when supported and pushed down; do not assume every SQL condition will do so.
For an exploratory query, begin with the smallest useful result and expand deliberately. For recurring analysis, consider whether the connector’s direct reads remain appropriate or whether exporting data would better isolate analytical work from the live table.
Export DynamoDB data to S3 for Athena
DynamoDB export creates data in S3 that Athena and other AWS services can analyze. A full export is a snapshot at a selected point in time; an incremental export captures changes over a specified period within the point-in-time recovery window. The destination bucket can be in another AWS account or Region when the required permissions are in place. Supported export formats include DynamoDB JSON and Amazon Ion.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteEnable PITR and choose the export type
- Enable point-in-time recovery. DynamoDB export requires PITR on the table. Confirm the recovery window covers the period you need for an incremental export.
- Choose full or incremental export. Select a full export for a snapshot, or an incremental export for changes in the eligible period.
- Set the S3 destination and format. Check bucket permissions, including cross-account or cross-Region access if applicable, and choose DynamoDB JSON or Amazon Ion.
- Wait for the asynchronous export to complete. Completion time varies; Amazon Web Services says there is no service-level agreement guaranteeing export completion times. Treat the finished S3 export as the input to downstream Athena analysis.
Exports do not consume DynamoDB read capacity units and, according to AWS documentation, do not affect table performance or availability. They are asynchronous, however, so they are not a substitute for an immediate query against current table contents.
Account for export and storage charges
A full-export charge is based on the table data and local secondary index size at the selected point in time. An incremental-export charge is based on the data processed from continuous backups and has a 10 MB minimum charge. S3 storage and PUT requests add charges. Rates vary by location and usage, so check current AWS pricing for your Region and estimate the total lifecycle cost rather than treating the export charge as the only cost.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Use Streams or Kinesis for near-real-time changes
If a downstream system needs changes as they happen, AWS recommends DynamoDB Streams or Kinesis Data Streams. Incremental export is an option when near-real-time capture is not necessary. AWS cautions, “Don’t use scans to detect changes.” Scanning for differences is not a reliable change-capture design and can create unnecessary read work.
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Plan the consumers as part of the integration. AWS notes that generally only two simultaneous consumers can use a DynamoDB stream, so check the current service guidance when designing multiple downstream readers. The appropriate choice depends on the downstream integration and constraints such as event ordering or item size.
Quick Recap
A workload-based decision checklist
- Need current table data through SQL? Evaluate the Athena connector, then assess expected scans, predicates, and read-capacity impact.
- Need a stable analytical snapshot or a batch dataset? Export to S3 and query the export with Athena; enable PITR first.
- Need changes delivered near real time? Use Streams or Kinesis Data Streams rather than scans; use incremental export when that freshness is not required.
- Comparing total cost? Include connector reads and supporting S3 use, or export charges plus S3 storage and requests, as applicable.
- Need repeatable results? Prefer an exported snapshot when analyses should operate on a defined point-in-time dataset rather than changing live table contents.
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