Delta Lake Streaming

Delta Lake Streaming

Azure Data Engineering

April 19, 2024

The Salient Features of Azure Delta Lake Streaming Service

As data analytics scenarios increasingly involve streaming data that must be processed in near real time, the need for efficient and reliable streaming services has become more crucial. This is where the Azure Delta Lake streaming service comes in. In this blog post, we will explore the key features of this service and how it can benefit Azure learners.

Spark Structured Streaming

The Azure Delta Lake streaming service is built on top of Spark Structured Streaming, an API that allows for processing of streaming data in a boundless dataframe.

This means that the data can be constantly read from a source, processed, and written to a sink in near real time.

Spark Structured Streaming supports various streaming sources such as:

Streaming with Delta Lake Tables

One of the major advantages of the Azure Delta Lake streaming service is its ability to use Delta Lake tables as both a source and a sink for streaming data.

This means that you can capture a stream of real-time data from an IoT device and write it directly to a Delta Lake table, enabling you to query the table to see the latest streamed data.

Alternatively, you can also read a Delta Lake table as a streaming source, allowing you to constantly report new data as it is added to the table.

Using Delta Lake Tables as a Streaming Source

To use a Delta Lake table as a streaming source, you can simply load the table into a streaming dataframe and use the Spark Structured Streaming API to process it.

This allows for various operations such as aggregation and grouping of data, which can then be sent to downstream processes for visualization.

It is important to note that only append operations can be included in the stream when using a Delta Lake table as a source.

Using Delta Lake Tables as a Streaming Sink

On the other hand, you can also use a Delta Lake table as a streaming sink, where a stream of data is read from a source and written to a Delta Lake table.

This is particularly useful when dealing with IoT devices, as new data can be added to the stream whenever a file is added to a folder.

The use of a checkpoint file ensures that the stream processing can be recovered from failure at the point where it left off.

Benefits of Azure Delta Lake Streaming Service

The Azure Delta Lake streaming service offers several benefits for Azure learners, including:

Conclusion

In conclusion, the Azure Delta Lake streaming service is a powerful tool for processing streaming data in near real time. Its integration with Spark Structured Streaming API and support for Delta Lake tables make it a valuable asset for Azure learners. With its efficient and reliable streaming capabilities, the Azure Delta Lake streaming service is a must-have for any data analytics scenario involving streaming data.

References

https://learn.microsoft.com/en-us/training/modules/use-delta-lake-azure-synapse-analytics/5-use-delta-lake-streaming-data

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