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Azure Data Factory Blog
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General Availability of Time to Live (TTL) for Managed Virtual Network in Azure Data Factory

lrtoyou1223's avatar
lrtoyou1223
Icon for Microsoft rankMicrosoft
Oct 11, 2023

In the fast-paced world of data integration, where seamless and secure data movement is paramount, Azure Data Factory (ADF) stands as a trusted orchestrator of data workflows. Today, we are thrilled to announce a significant enhancement to ADF's capabilities - the General Availability of ADF Managed Virtual Network Time to Live (TTL).

 

What is Managed Virtual Network TTL?

Before we delve into the benefits and use cases, let's understand what Managed Virtual Network TTL is all about.

Time to Live (TTL) is a crucial enhancement for Azure integration runtimes within a Managed Virtual Network. It allows you to specify a TTL value and Data Integration Unit (DIU) numbers required for various data integration activities. The TTL feature helps to manage compute resources more effectively, reduce startup times, and optimize overall performance.

 

Key Benefits of Managed Virtual Network TTL

Now, let's explore the key benefits of Managed Virtual Network TTL and why it's a game-changer for your data integration workflows:

 

  1. Improved Performance

One of the challenges in Managed Virtual Network is managing the startup time of compute resources, especially when dealing with multiple copy activities or complex pipelines. Managed Virtual Network TTL addresses this by keeping computes alive for a certain period after their execution completes. If a new copy activity starts during the TTL time, it will reuse existing computes, significantly reducing startup time and enhancing overall performance.

 

  1. Compute Size Flexibility

With Managed Virtual Network TTL, you have the flexibility to select from pre-defined compute sizes or customize the compute size based on your specific requirements and real-time needs. This customization ensures that your compute resources are optimally sized for the tasks at hand.

 

Pipeline and External Activity

Time to Live (TTL) isn't just limited to copy activities; you can also tailor the compute size and TTL duration for pipeline and external activities, ensuring your data integration processes are finely tuned to your specific requirements.

 

 

Monitoring Your Managed Virtual Network

Azure Data Factory's Managed Virtual Network TTL feature brings a new level of control and efficiency to your data integration workflows. By allowing you to manage compute resources effectively and reduce startup times, it optimizes performance. However, to ensure that your data integration processes are running smoothly within this secure environment, you need visibility and monitoring. In Azure Data Factory, we also provide some new metrics to help you identify the issues and bottlenecks.

 

Learn more about monitoring: Monitor an integration runtime within a managed virtual network - Azure Data Factory | Microsoft Learn

 

Embrace ADF Managed Virtual Network TTL

We are excited to bring you this enhancement to Azure Data Factory, and we look forward to seeing how it transforms your data integration processes. Get started with Managed Virtual Network TTL today and unlock a new level of efficiency and security in your data workflows. 

 

 

 

Updated Oct 10, 2023
Version 1.0
  • David1740's avatar
    David1740
    Copper Contributor

    Good work on these improvements.

     

    Where can we learn more about managed vnet technology?  In particular I'd like to understand how to troubleshoot networking problems that only occur within a managed vnet.  (ie. intermittent occurrences of "connection reset by peer", that don't happen outside the context of a managed vnet).

     

    I wanted to let you know that I experienced a fairly long managed-vnet outage on 2023-10-11 but your CSS organization is not fully equipped to help customers dive deep into any problems arising out of this technology of yours.  You may need to offer up more resources to help with this stuff (PTAs or EEEs or whatever you can spare).