Integration between streaming data from Spark and Power BI in real-time can be done by the below steps with very low latency:
1. Preparation of Spark Streaming Pipeline
Set Up a Streaming Source: Set Spark up to read directly from a real-time source, such as Kafka socket streams or event logs.
Data Transformation: The data cleaning, transforming, and visualization-ready preparation would go through Spark's DataFrames and Structured Streaming APIs. The data schema should also resemble the structure required for Power BI dashboards.
Output Sink: Now, one needs to decide on an output mechanism. The most common ones are Azure Event Hubs, Azure Cosmos DB-based output, or a custom REST API pushing the data to Power BI.
2. Configuration Azure Event Hubs or Stream Analytics
Send Data to Event Hubs: Add a Spark event hubs library—for instance, maven or sbt dependencies—to add Spark to Event Hubs and configure the Spark application to stream the processed data into Event Hubs.
Set Up Stream Analytics (Optional): Use Azure Stream Analytics to read the Events and send the result to Power BI. Formulate queries to filter and aggregate the data before reaching Power BI.
Three connect with Power BI.
To create a Power BI Streaming Dataset, go to the Power BI service, then click on Streaming Dataset under Create. Choose either the API or Azure Stream Analytics as the input.
Create the dataset schema matching the data accompanying the stream from Spark.
Dashboards must be created by connecting them in Power BI reports or dashboards to the streaming dataset.
Real-time tiles like cards, line charts, and gauges for instant consumption should be applied.
Push Data to be Sent to Power BI:
In contrast to using Azure Event Hubs, configure Spark to push its data directly to the REST API of Power BI. HttpClient will then be used in Spark to send HTTP POST requests, which will have JSON payloads, directly to the dataset's push endpoint.
Batching: Proper batching of the data within Spark to restrict API calls to a bare minimum, with a high degree of real-time updates.
Optimize for Low Latency processing: Minimize the time spent on processing by optimally configuring Spark jobs to reduce delays between transformations and computations. Use cached memory wherever possible.
Lightweight Data Transmission: If the payload size is large, small data formats such as JSON and compresses are preferred.
Monitor and Scale: Monitoring continuously the performance of Spark in order to dynamically scale resources handling spikes of streaming data.
