What AWS Released
AWS made three Amazon Redshift Data API capabilities generally available on July 29. The features are long polling, ListSessions, and flexible batch execution with AUTO_COMMIT mode. All three are live now for Amazon Redshift Provisioned and Amazon Redshift Serverless in every AWS commercial and AWS GovCloud (US) Region that supports the Data API.
Each feature targets a distinct friction point that teams hit when running SQL through the Data API.
Long Polling: Fewer Round Trips to Check Query Status
By default, Data API operations return an immediate response. If a statement is still running, the caller must poll repeatedly until it reaches a terminal state. That loop adds latency and burns API quota.
With long polling, you set WaitTimeSeconds to a value between 1 and 30. The API holds the connection open and returns as soon as the statement finishes or the wait window closes. If the statement completes within the wait time, the response includes the terminal status or result data. If the wait expires before the statement finishes, the response returns the current in-progress status. The caller then decides whether to wait again.
Long polling is supported on DescribeStatement, ExecuteStatement, BatchExecuteStatement, GetStatementResult, and GetStatementResultV2. Omitting the parameter preserves the existing asynchronous behavior, so adoption is not forced.
For pipelines that poll on a tight loop, the documentation cites short queries as one example use case where waiting for completion in a single call avoids repeated status checks. The announcement does not state the exact call reduction teams should expect. That depends on query duration and polling frequency in each application.
ListSessions: Session Visibility Without External Tracking
Session reuse has been available in the Data API since September 2024, letting callers retain temporary tables, variables, and other session state across multiple queries. The operational problem was that teams had to track session identifiers themselves.
ListSessions lets applications enumerate active sessions and filter by status, compute target, or database, eliminating the need to track session identifiers externally. ListSessions is now part of the Data API's supported operation set.
For platform owners managing multi-tenant applications or long-running analytical workflows, this closes a monitoring gap. Before ListSessions, identifying which sessions were still active required application-level bookkeeping. Now the API surface itself exposes that state.
Flexible Batch Execution: AUTO_COMMIT Mode and Shared Parameters
Previously, all SQL statements in a BatchExecuteStatement call ran as a single transaction by default. If any statement failed, all work was rolled back. That all-or-nothing behavior is the right default for operations that must be atomic. It is the wrong default for ETL pipelines and administrative scripts where partial completion is acceptable.
BatchExecuteStatement now supports an ExecutionMode parameter with AUTO_COMMIT mode. Each SQL statement is committed individually. A failure of one statement does not affect the others. The original TRANSACTION mode remains the default, so existing batches keep their current behavior unless you explicitly pass the new parameter.
For ETL and data engineering teams, this change may carry the most immediate architectural consequence of the three. The second batch improvement is shared parameterization. BatchExecuteStatement now accepts an array of SqlParameter, enabling parameter reuse across all statements in a batch. Define a parameter once and reference it in any statement, eliminating repeated literal values embedded in each query.
What Changes for Your Architecture and Controls
These three features change specific decisions for platform owners, application teams, and anyone operating ETL at scale.
Polling loops need a review. If your Lambda functions, Step Functions workflows, or application code poll DescribeStatement on a fixed interval, adding WaitTimeSeconds may reduce API call volume and lower latency for short queries. The announcement does not state pricing implications for long-poll requests versus standard requests. Verify current Data API quota and cost behavior before changing high-volume pipelines.
Each API in the Redshift Data API has a transactions-per-second quota before throttling. The documentation does not describe any exemption from the per-API TPS quota for long-poll requests; a ThrottlingException still applies if the request rate exceeds it.
Session management logic is now redundant for many teams. If your application maintains a session ID cache or a separate datastore to track active Redshift sessions, ListSessions can replace that layer. The filter options by status, compute target, and database are useful for multi-workgroup environments.
AUTO_COMMIT semantics require an explicit decision per pipeline. The default TRANSACTION mode does not change automatically. But every existing batch pipeline now has a decision in front of it: is atomic rollback the right behavior, or has the team been living with it because there was no alternative? ETL pipelines that load independent dimension tables, run daily maintenance tasks, or execute administrative scripts are strong candidates for AUTO_COMMIT.
Workflows that require atomicity — such as those moving money or updating order state — are likely poor fits for AUTO_COMMIT mode.
The combination of AUTO_COMMIT mode and shared SqlParameter objects may be most valuable in pipelines that currently split a logical batch into multiple single-statement API calls to avoid rollback risk. If your team made that architectural choice, revisit it. A single BatchExecuteStatement call with AUTO_COMMIT may reduce API overhead and simplify error handling.
Governance teams should note the audit trail difference. AUTO_COMMIT commits each statement independently. A partial run will leave some statements committed and others not. Make sure your pipeline logging captures per-statement outcomes, not just batch-level success or failure. The announcement does not describe changes to Data API logging or CloudWatch integration for this mode.
Where to Start
Start by identifying which pipelines poll on a tight loop. Test WaitTimeSeconds on representative query durations in a non-production environment. Then audit each batch pipeline for AUTO_COMMIT eligibility. For each job, ask whether a partial commit is acceptable or dangerous before switching modes. The Amazon Redshift Data API developer guide covers long polling and flexible batch execution details.