Replay Archived Stream Data related to Exam: Data Engineering on Microsoft Azure

Replaying archived stream data is a crucial aspect of data engineering on Microsoft Azure. It allows data engineers to analyze and process historical data that has been captured and stored in Azure services. This article will provide an overview of how to replay archived stream data using Azure services.

Prerequisites

Before you begin replaying archived stream data, ensure you have the following prerequisites:

• An Azure account
• Data engineering expertise

1. Store Stream Data in Azure Storage

The first step is to store the stream data in Azure Storage. Azure Blob storage is commonly used for this purpose. You can use Azure Event Hubs as an ingestion service to capture the stream data and then store it in Azure Blob storage.

python
# Python code to store stream data in Azure Blob Storage
from azure.eventhub import EventHubClient, EventData
from azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient
import json

# Replace , with your own values
blob_connection_string = “”
blob_container_name = “”

# Replace and with your own values
event_hub_connection_string = “”
event_hub_name = “”

blob_service_client = BlobServiceClient.from_connection_string(blob_connection_string)
container_client = blob_service_client.get_container_client(blob_container_name)

event_hub_client = EventHubClient.from_connection_string(event_hub_connection_string, event_hub_name)

consumer_group = “$Default”

# Receive events from the Event Hub and store them in Azure Blob Storage
receiver = event_hub_client.create_consumer(consumer_group, partition_id=”@latest”, starting_position=”-1″)
with receiver:
for event_data in receiver.receive():
blob_name = f”stream_data_{event_data.sequence_number}.json”
blob_client = container_client.get_blob_client(blob_name)

json_data = json.loads(event_data.body_as_str())
blob_client.upload_blob(json.dumps(json_data))

The above Python code demonstrates storing stream data in Azure Blob Storage by capturing it using Azure Event Hubs and then uploading it to Azure Blob storage for archiving. Make sure to replace the placeholders with your own values for connection strings, container name, and event hub details.

2. Configure Stream Analytics Job

To replay archived stream data, you need to configure a Stream Analytics job in Azure. Stream Analytics allows you to perform real-time analytics on the archived data.

json
{
“properties”: {
“name”: ““,
“eventsOutOfOrderPolicy”: “adjust”,
“outputErrorPolicy”: “stop”,
“inputs”: [
{
“name”: ““,
“type”: “stream”,
“datasource”: {
“type”: “Microsoft.Storage/Blob”,
“properties”: {
“container”: ““,
“pathPattern”: “stream_data*.json”,
“dateFormat”: “yyyy/MM/dd”,
“timeFormat”: “HH:mm:ss”
}
}
}
],
“outputs”: [
{
“name”: ““,
“type”: “blob”,
“datasink”: {
“type”: “Microsoft.Storage/Blob”,
“properties”: {
“container”: “”
}
}
}
],
“transformation”: {
“query”: “SELECT * INTO FROM ”
},
“identity”: {
“type”: “SystemAssigned”
},
“sku”: {
“name”: “standard”
},
“eventsLateArrivalMaxDelayInSeconds”: 3600
},
“location”: ““,
“tags”: {},
“tags”: {},
“type”: “Microsoft.StreamAnalytics/StreamAnalytics”,
“apiVersion”: “2019-06-01”
}

The above JSON code represents the configuration of a Stream Analytics job. Make sure to replace the placeholders with your own values for job name, input alias, blob container name, output alias, output blob container name, region name, and other necessary details.

3. Start the Stream Analytics Job

After configuring the Stream Analytics job, you can start the job to replay the archived stream data.

powershell
# PowerShell command to start the Stream Analytics job
Start-AzStreamAnalyticsJob -ResourceGroupName “” -Name “”

Replace the placeholders with your own values for resource group name and job name in the PowerShell command.

4. Monitor the Job and Analyze Data

You can monitor the Stream Analytics job to check its progress and ensure that the archived stream data is being replayed successfully.

powershell
# PowerShell command to monitor the Stream Analytics job
Get-AzStreamAnalyticsJob -ResourceGroupName “” -Name “”

Replace the placeholders with your own values for resource group name and job name in the PowerShell command.

Once the job is running, you can analyze the replayed data using various Azure services, such as Azure Databricks or Azure Synapse Analytics, to gain insights and perform further data engineering tasks.

Conclusion

Replaying archived stream data is a valuable capability in data engineering on Microsoft Azure. By following the steps outlined in this article, you can store and replay stream data, configure a Stream Analytics job, and analyze the replayed data using Azure services. This empowers you to derive meaningful insights and drive data-driven decision-making processes.