What causes DF-AzureDataExplorer-WriteTimeout?

The data volume being written to ADX in a single run is too large for the ingest operation to complete within the timeout window. The ADX cluster ingestion queue is backlogged, delaying acknowledgment of the write operation. The target ADX table has a complex ingestion policy (e.g., update policies or materialized view policies) that significantly extends write time

How do I fix DF-AzureDataExplorer-WriteTimeout?

Review the data volume being written in a single run — if writing millions of rows, consider partitioning the pipeline to write smaller batches.. Check ADX ingestion health in Azure Monitor for your cluster — look for high queue depth or ingestion latency during the failure window.. Inspect the target table for update policies or materialized view policies that fire on ingest and may be adding write latency.. If using streaming ingest, switch to queued (bulk) ingest for large-volume operations, as queued ingest handles higher throughput.. Increase the write timeout value in the ADF ADX linked service configuration as a temporary measure while investigating the root cause.

Medium severitydata flow

Power BI Refresh Error:
DF-AzureDataExplorer-WriteTimeout

What does this error mean?

The ADF Mapping Data Flow Azure Data Explorer (ADX/Kusto) connector timed out while writing data to the target table. The ingest operation did not complete within the connector's write timeout threshold.

Common causes

1The data volume being written to ADX in a single run is too large for the ingest operation to complete within the timeout window
2The ADX cluster ingestion queue is backlogged, delaying acknowledgment of the write operation
3The target ADX table has a complex ingestion policy (e.g., update policies or materialized view policies) that significantly extends write time

How to fix it

1Review the data volume being written in a single run — if writing millions of rows, consider partitioning the pipeline to write smaller batches.
2Check ADX ingestion health in Azure Monitor for your cluster — look for high queue depth or ingestion latency during the failure window.
3Inspect the target table for update policies or materialized view policies that fire on ingest and may be adding write latency.
4If using streaming ingest, switch to queued (bulk) ingest for large-volume operations, as queued ingest handles higher throughput.
5Increase the write timeout value in the ADF ADX linked service configuration as a temporary measure while investigating the root cause.

Frequently asked questions

Does the data get partially written when a write timeout occurs?

For queued (bulk) ingest, the operation is transactional — it either succeeds fully or not at all. For streaming ingest, some rows may have been written before the timeout.

How do I check whether my ADX ingestion queue is backlogged?

In Azure Monitor, check the 'Ingestion result' and 'Ingestion latency' metrics for your ADX cluster. High latency or many pending operations indicates queue pressure.

When should I use streaming ingest versus queued ingest?

Streaming ingest suits low-latency, low-volume data (< 4 GB/day). For high-volume batch ingest in data flows, queued (bulk) ingest has better throughput and fewer timeout errors.

Official documentation: https://learn.microsoft.com/en-us/azure/data-factory/data-flow-troubleshoot-guide