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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.
Before you begin replaying archived stream data, ensure you have the following prerequisites:
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
blob_connection_string = “
blob_container_name = “
# Replace
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.
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
},
“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.
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 “
Replace the placeholders with your own values for resource group name and job name in the PowerShell command.
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 “
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.
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.
35 Replies to “Replay archived stream data”
This is very helpful for my DP-203 preparation. Appreciate it!
Does anyone know how to set up an event hub for streaming data?
You can refer to the Azure documentation, it has a step-by-step guide on setting up an event hub.
Make sure you configure partitions appropriately based on expected load.
Expert advice: Monitor the latency when replaying large volumes of archived data.
Good point, latency can indeed be an issue with large datasets.
I believe the questions below are one and the same “Which service in Azure allows you to schedule the replay of archived stream data?
Which Azure service provides a distributed, scalable, and reliable platform for replaying archived stream data?”
Therefore the answer should be Azure stream analytics.
I found this very helpful. Thanks for sharing!
What are some best practices for managing archived data?
Use lifecycle policies to move data to cooler storage tiers as it ages.
Regularly clean up/archive data that doesn’t need to be in hot storage to save costs.
Is there any impact on performance when archiving stream data?
Make sure you are balancing the workload and using the right service tier.
From my experience, there’s a minimal performance hit if you configure your resources properly.
How does the cost of archiving compare to hot storage?
Archived storage is significantly cheaper than hot storage, but retrieval costs can be higher.
It’s generally cost-effective for data you access infrequently.
Great to see a discussion on vital topics for the DP-203 exam.
Great insights on replaying archived stream data for DP-203 exam preparation!
Very detailed explanation, love it!
Can anyone explain how archiving works in Azure Stream Analytics?
In ASA, archiving means you can store your incoming stream data into Blobs or SQL databases for future use and analysis.
It’s really useful for maintaining historical data which can be replayed later for testing or re-analysis.
Thanks for helping me understand replay stream data concepts.
Can archived data be replayed into another stream for transformation?
Yes, you can replay archived data into Azure Stream Analytics for further transformations.
Just ensure your input specifies the archived blob storage or SQL database.
Any specific permissions needed for accessing archived data?
You need read permissions on the storage account where the data is archived.
I’m taking the DP-203 next month, thanks for this!
Thanks for the detailed post!
Informative post, thanks!
I honestly think more practical examples would help.
Expert tip: Make sure the data schema is consistent when replaying archived data.
Absolutely! Schema consistency is crucial to avoid processing errors.