Can Azure stream analytics lose data?

Question

~~ I have encountered two instances recently where data was lost in Azure Stream Analytics (ASA). The first instance occurred when I was using ASA to store data in Parquet format, which was sourced from IoT Hub. I had set the minimum batch count to 20,000 records and the maximum time interval to 1 hour. However, due to the nature of the data, there were cases where only a few records were received within an hour, resulting in data loss. When I adjusted the maximum time interval to 5 minutes, the data was successfully stored in ADLS.

The second instance involved using ASA with the TOPONE operator (partition by ...), which also resulted in data loss. Similarly, the data loss might have occurred due to fewer records being recorded.

Is there any way to prevent such data loss from occurring? Or is it due to my own configuration settings?~~

Answer 1

@jane - Thanks for the question and using MS Q&A platform.

Azure Stream Analytics is designed to process and analyze data in real-time, and it is built to handle high volumes of data. However, there are certain scenarios where data loss can occur.

In your first instance, the data loss occurred because the maximum time interval was set to 1 hour, and there were cases where only a few records were received within that hour. This resulted in the data not being processed and stored in ADLS. By adjusting the maximum time interval to 5 minutes, you were able to successfully store the data in ADLS. This is because the data was processed and stored more frequently, reducing the chances of data loss.

In your second instance, the data loss might have occurred due to fewer records being recorded. The TOPONE operator is used to select the top record from each partition based on a specified criteria. If there are fewer records in a partition, then there is a chance that the data might be lost.

To prevent data loss in Azure Stream Analytics, you can take the following steps:

1. Adjust the configuration settings: You can adjust the configuration settings such as the batch count and time interval to ensure that data is processed and stored more frequently.
2. Monitor the job: You can monitor the job to ensure that it is running smoothly and that data is being processed and stored correctly.
3. Use backup and recovery options: You can use backup and recovery options such as checkpointing and event hubs to ensure that data is not lost in case of a failure.
4. Use redundancy: You can use redundancy options such as multiple outputs and multiple instances to ensure that data is not lost in case of a failure.

In summary, while Azure Stream Analytics is designed to handle high volumes of data, there are certain scenarios where data loss can occur. By adjusting the configuration settings, monitoring the job, using backup and recovery options, and using redundancy, you can prevent data loss and ensure that your data is processed and stored correctly.

Hope this helps. Do let us know if you any further queries.

---

If this answers your query, do click Accept Answer and Yes for was this answer helpful. And, if you have any further query do let us know.