Maximizing Efficiency with Managed Apache Flink on Azure HDInsight on AKS

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This post has been republished via RSS; it originally appeared at: New blog articles in Microsoft Community Hub.

Author: Alexandre Gattiker, Principal Engineer, Industry Solutions, Microsoft.  

Apache Flink, the open-source stream processing framework, has been making waves in the world of big data and real-time analytics. In October 2023, Microsoft introduced the public preview of Apache Flink in Azure HDInsight on AKS. This marks an exciting step forward in empowering organizations to process and analyze real-time data at scale. In this blog post, we'll delve into what Apache Flink brings to the table and explore the benefits of running Flink in Azure HDInsight on AKS. 


Why Apache Flink? 


Apache Flink is designed for processing data in motion, making it ideal for applications that require low-latency data processing, event time handling, and fault tolerance. It supports both batch and stream processing, enabling the development of real-time data applications. 


Key Features of Apache Flink include: 


  • Low Latency Processing: Flink offers sub-second latency, making it suitable for applications that require immediate insights from incoming data. 
  • Event Time Processing: Flink provides built-in support for event time processing, which is essential for correctly handling out-of-order data in streaming applications. 
  • Exactly Once Semantics: Flink ensures data consistency by offering exactly once processing guarantees, making it a robust choice for mission-critical applications. 


Introducing Apache Flink in HDInsight on AKS 


With the public preview of Apache Flink in Azure HDInsight on AKS, organizations can harness the power of Flink in a managed Kubernetes environment. Here are some of the key benefits: 


  • Seamless Integration: Flink in HDInsight on AKS offers tight integration with the Azure ecosystem. You can easily connect it to Azure Data Lake Storage, Azure Event Hubs, and other Azure services. 
  • Scalability: HDInsight on AKS allows you to scale Flink clusters on-demand to handle growing workloads. You pay only for the resources you use, providing cost-effective scalability. 
  • High Availability: Apache Flink in HDInsight on AKS ensures high availability, making your real-time applications resilient to failures. 
  • Security: HDInsight on AKS provides multi-layer security, including VNET isolation, authentication, and authorization integrated with Microsoft Entra ID (formerly Azure Active Directory), to protect your data and workloads. 
  • Monitoring and Management: Azure Monitor, Prometheus, and Grafana are available for monitoring and managing your Flink clusters, providing insights into cluster performance and health. 


Getting Started with Flink in HDInsight on AKS 


If you're ready to explore Apache Flink in HDInsight on AKS, here's how to get started: 

  • Connect to Data Sources: Utilize Flink connectors to ingest data from various sources. Whether it's data lakes, databases, or message queues, Flink can seamlessly integrate with your data ecosystem. 
  • Scale and Monitor: As your data processing needs grow, you can easily scale your Flink cluster. Use Azure Monitor, Prometheus, and Grafana to gain insights into your Flink application's performance and make informed decisions.  




The HDInsight on AKS GitHub repository contains multiple samples for developing Flink workloads and integrating them with Azure resources.


In particular, a comprehensive sample can be deployed with one command, demonstrating high-throughput processing of data at scale in Event Hubs Kafka. 


The sample can deploy several types of stateless and stateful processing jobs. Here is sample code used in a stateful job that raises an alarm if temperature readings are persistently high or if temperature and CO2 readings are above a given threshold. The code is only concerned with business logic, while state management and data partitioning per plant are provided. 






// do some computation on the state Iterator<SampleRecord> iterator = state.recordsIterator(); while (iterator.hasNext()) { SampleRecord r =; if (r.type.equals("TEMP")) { if (r.value >= 70 && r.value < 80) { nbTemperaturesInThe70s++; if (nbTemperaturesInThe70s > 1) { SampleTag tag = new SampleTag(r.deviceId,, r.createdAt, r.eventId, "SeveralTemperaturesIn70s"); if (!state.equivalentTagExists(tag)) { state.addTag(tag); out.collect(tag); } } } } else if (r.type.equals("CO2")) { if (r.value > 80) { someCO2IsGreaterThan80 = true; } } if (someCO2IsGreaterThan80 && nbTemperaturesInThe70s > 1) { SampleTag tag = new SampleTag(r.deviceId,, r.createdAt, r.eventId, "HighCO2WithSeveralTemperaturesIn70s"); if (!state.equivalentTagExists(tag)) { state.addTag(tag); out.collect(tag); break; } }









Apache Flink in HDInsight on AKS is a powerful combination for real-time data processing. It opens up exciting possibilities for organizations looking to gain insights from their streaming data.


With seamless integration, scalability, high availability, and robust security, Flink in HDInsight on AKS provides a compelling platform for developing and running real-time data applications. 


If you're ready to embrace real-time stream processing, give Apache Flink in HDInsight on AKS a try. Unlock the potential of your streaming data and stay ahead in the world of data-driven insights. 


To learn more about Apache Flink and HDInsight on AKS, visit the Preview official documentation and explore the Azure portal for setting up your Flink cluster. 

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