Capture Event Hubs data in parquet format and analyze with Azure Synapse Analytics

This tutorial shows how you can use the Stream Analytics no code editor to capture Event Hubs data in Azure Data Lake Storage Gen2 in parquet format.

In this tutorial, you learn how to:

  • Deploy an event generator that sends data to your event hub
  • Create a Stream Analytics job using the no code editor
  • Review input data and schema
  • Configure Azure Data Lake Storage Gen2 to which event hub data will be captured
  • Run the Stream Analytics job
  • Use Azure Synapse Analytics to query the parquet files

Prerequisites

Before you start, make sure you've completed the following steps:

Use no code editor to create a Stream Analytics job

  1. Locate the Resource Group in which the TollApp event generator was deployed.

  2. Select the Azure Event Hubs namespace.

  3. On the Event Hubs Namespace page, select Event Hubs under Entities on the left menu.

  4. Select entrystream instance.

    Screenshot showing the selection of the event hub.

  5. On the Event Hubs instance page, select Process data in the Features section on the left menu.

  6. Select Start on the Capture data to ADLS Gen2 in Parquet format tile.

    Screenshot showing the selection of the **Capture data to ADLS Gen2 in Parquet format** tile.

  7. Name your job parquetcapture and select Create.

    Screenshot of the New Stream Analytics job page.

  8. On the event hub configuration page, confirm the following settings, and then select Connect.

    • Consumer Group: Default

    • Serialization type of your input data: JSON

    • Authentication mode that the job will use to connect to your event hub: Connection string.

      Screenshot of the configuration page for your event hub.

  9. Within few seconds, you'll see sample input data and the schema. You can choose to drop fields, rename fields or change data type.

    Screenshot showing the fields and preview of data.

  10. Select the Azure Data Lake Storage Gen2 tile on your canvas and configure it by specifying

    • Subscription where your Azure Data Lake Gen2 account is located in
    • Storage account name, which should be the same ADLS Gen2 account used with your Azure Synapse Analytics workspace done in the Prerequisites section.
    • Container inside which the Parquet files will be created.
    • Path pattern set to {date}/{time}
    • Date and time pattern as the default yyyy-mm-dd and HH.
    • Select Connect

    Screenshot showing the configuration settings for the Data Lake Storage.

  11. Select Save in the top ribbon to save your job and then select Start. Set Streaming Unit count to 3 and then Select Start to run your job.

    Screenshot showing the Start Stream Analytics Job page.

  12. You'll then see a list of all Stream Analytics jobs created using the no code editor. And within two minutes, your job will go to a Running state. Select the Refresh button on the page to see the status changing from Created -> Starting -> Running.

    Screenshot showing the list of Stream Analytics jobs.

View output in your Azure Data Lake Storage Gen 2 account

  1. Locate the Azure Data Lake Storage Gen2 account you had used in the previous step.
  2. Select the container you had used in the previous step. You'll see parquet files created based on the {date}/{time} path pattern used in the previous step. Screenshot of parquet files in Azure Data Lake Storage Gen 2.

Query captured data in Parquet format with Azure Synapse Analytics

Query using Azure Synapse Spark

  1. Locate your Azure Synapse Analytics workspace and open Synapse Studio.

  2. Create a serverless Apache Spark pool in your workspace if one doesn't already exist.

  3. In the Synapse Studio, go to the Develop hub and create a new Notebook.

  4. Create a new code cell and paste the following code in that cell. Replace container and adlsname with the name of the container and ADLS Gen2 account used in the previous step.

    %%pyspark
    df = spark.read.load('abfss://container@adlsname.dfs.core.windows.net/*/*/*.parquet', format='parquet')
    display(df.limit(10))
    df.count()
    df.printSchema()
    
  5. Select Run All to see the results

    Screenshot of spark run results in Azure Synapse Analytics.

Query using Azure Synapse Serverless SQL

  1. In the Develop hub, create a new SQL script.

  2. Paste the following script and Run it using the Built-in serverless SQL endpoint. Replace container and adlsname with the name of the container and ADLS Gen2 account used in the previous step.

    SELECT
        TOP 100 *
    FROM
        OPENROWSET(
            BULK 'https://adlsname.dfs.core.windows.net/container/*/*/*.parquet',
            FORMAT='PARQUET'
        ) AS [result]
    

    Screenshot of SQL script results in Azure Synapse Analytics.

Clean up resources

  1. Locate your Event Hubs instance and see the list of Stream Analytics jobs under Process Data section. Stop any jobs that are running.
  2. Go to the resource group you used while deploying the TollApp event generator.
  3. Select Delete resource group. Type the name of the resource group to confirm deletion.

Next steps

In this tutorial, you learned how to create a Stream Analytics job using the no code editor to capture Event Hubs data streams in Parquet format. You then used Azure Synapse Analytics to query the parquet files using both Synapse Spark and Synapse SQL.