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Running Apache Kafka® at the Edge Requires Confluent’s Enterprise-Grade Data Streaming Platform

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Modern edge computing is transforming industries including manufacturing, healthcare, transportation, defense, retail, energy, and much more—pushing data management to far-reaching data sources to enable connected, low latency operations and enhanced decision making. These new use cases shift workloads to the left—requiring real-time data streaming and processing at the edge, right where the data is generated. But collecting and processing data at the edge presents many challenges, particularly at scale. In this blog, we'll explore common challenges faced when attempting to deploy Apache Kafka® at the edge and how Confluent’s enterprise-grade data streaming platform can cost-effectively address these issues and unlock business-required results.

Implementing Apache Kafka at the edge is expensive, introduces power constraints, and often depends on reliable cloud connectivity

Leveraged by more than 80% of the Fortune 100, Apache Kafka has become the de facto open source standard for data streaming—allowing for the collection and stream processing of data, persistently, in real time from a variety of devices at the edge. However, running open source Kafka at the edge can introduce significant challenges, largely—but not exclusively—due to the remote nature of such deployments. These challenges include:

  • Managing open source Kafka at the edge is expensive

  • Processing capabilities are limited due to far edge hardware constraints

  • Reliably connecting the edge to your datacenter or cloud is complex

Costs of managing Kafka: Running Kafka at the edge involves complex deployment and management tasks, requiring robust remote monitoring tools, scalability features, load balancing, storage safeguards, and more. Deployments of this type demand highly skilled Kafka experts to build and support highly scalable environments with necessary features such as support for non-Java programming languages, comprehensive data integrations, advanced stream processing capabilities, robust security controls, and data quality management tools. Without these, organizations face increased total costs, delayed time to value, and lower ROI.

Far edge power constraints: Edge devices, typically leveraging ARM32, x86, and other processors, often have limited power and processing capabilities. This limitation can hinder the performance of Kafka, making it challenging to implement. Upgrading or supplementing edge devices to meet Kafka’s demands can be prohibitively expensive, raising significant cost-efficiency concerns for organizations, and limiting processing capabilities in far edge or remote locations.

Reliable cloud connectivity: Edge computing often requires data movement to a centralized system—like a central datacenter or the cloud—for advanced processing and global distribution of data products. Ensuring seamless connectivity and synchronization between multiple Kafka clusters for mission-critical use cases is complex, requiring highly reliable data sharing and consistent replication across environments. Efficiently transferring all transactional data for large-scale processing adds another layer of difficulty, further complicating edge deployments.

Historically, these challenges have forced many businesses to either ignore, simplify, or drop their use of data streaming at the edge. As a result, many enterprises find themselves unable to meet customer expectations for “real-time everything,” dependent upon data sourced from everywhere a business spans. This gap hinders their ability to fully leverage the potential of edge computing and stay competitive in a fast-paced market.

Avoid complexities and accelerate innovation with a complete, enterprise-grade distribution of Apache Kafka

Built by the original creators of Apache Kafka, Confluent Platform provides a complete, enterprise-grade data streaming platform that allows businesses to reduce the complexities of open source Kafka and accelerate projects dependent upon real-time data, wherever they are run—at the edge, on premises, in the cloud, or anywhere in between. With Confluent, teams can easily connect, process, and react to all their data in real time while maintaining focus on what matters most: innovation for the business.

Confluent Platform provides a data streaming platform that completes Apache Kafka and allows teams to work both efficiently and confidently with real-time data at the edge

With Kafka at its core, Confluent offers a holistic set of enterprise-grade capabilities that come ready out of the box to accelerate time to value and reduce TCO for data streaming. Rather than needing to spend costly development cycles building and maintaining foundational tooling for Kafka internally, you can immediately leverage features built by the world’s foremost Kafka experts to hit production quickly and confidently. That means freeing your teams from undifferentiated heavy lifting in order to focus on developing the real-time applications built on top of Kafka that drive your business forward.

Confluent brings a cloud-native experience to your private, self-managed edge environments. You can easily meet and manage any demand with elastically scaling clusters that automate partition rebalances. With tiered storage, you can retain infinite data right within Kafka while cost-effectively separating storage from compute. Additionally, minimize downtime costs and business disruption with clusters deployed across multiple regions.

Confluent comes packed with a set of tools to enhance engineering agility and enable more of your teams to easily build real-time applications dependent upon data streaming. While other Kafka solutions only support the Java client, Confluent provides a broad set of battle-tested, non-Java clients for popular programming languages, including Python, C/C++, Go, and .NET. Data integrations are made easy with an ever-expanding portfolio of 120+ pre-built connectors. Available later this year, Confluent Platform for Apache Flink®—a Flink distribution fully supported by Confluent—will allow Confluent customers to easily leverage stream processing for edge, on-prem, or private cloud workloads with long-term expert support.

Confluent reduces risk and resource investments for Kafka, simplifying operational tasks with enterprise-grade features for DevOps and platform teams. This includes GUIs like Health+ for management and monitoring, Metrics API for custom infrastructure monitoring solutions, infrastructure-as-code tools for automating lifecycle tasks (leveraging Kubernetes-native tooling where applicable), and improvements to Kafka’s performance and elasticity for diverse data streaming workloads.

Health+: Prevent cluster outages with intelligent alerting and cloud-based monitoring tools

Additionally, Confluent provides enterprise-grade security features for role-based access control, auditing, and encryption to properly protect all of your data flowing through Kafka, along with governance features to programmatically ensure the quality and consistency of that data. We also provide several state-of-the-art disaster recovery and high availability features to dramatically reduce downtime and data loss beyond what is possible with open source replication tools.

Bring committer-led Kafka support to your business through Confluent’s team of data streaming experts

Confluent provides not only a robust feature set for Kafka practitioners but also a complete, enterprise support and engagement model to ensure business success from initial projects to creating a central nervous system across multiple lines of business. As data flows through Kafka, our committer-led team with more than 1M Kafka development hours logged helps customers transition from supporting a few use cases to successfully leveraging the platform organization-wide, identifying new use cases, and following best practices to maximize revenue, reduce costs, and minimize risk. Expert-led support from data streaming experts ensures these costs are minimized, and top talent can focus on the most high-value tasks.

Run Confluent workloads on low-power ARM64 processors

With the recent Confluent Platform 7.6 update, Confluent Platform can now be run on ARM64 Linux architectures. ARM architecture improves your price-to-performance ratio on compute resources, a benefit we've experienced firsthand by migrating our entire AWS fleet for Confluent Cloud to ARM-based images.

Confluent Server enables you to collect data from any source, persist it, process it, and replicate it across the WAN using cluster linking. Now, you can deploy this in production on low-cost, small-footprint ARM64 architecture infrastructure at the edge, addressing both performance and cost-efficiency concerns. Additionally, with 7.6, we’ve extended our operating system support to include Rocky Linux, allowing you to choose your preferred OS and safely run Confluent Platform in production.

Synchronize edge devices to your datacenter or cloud with Confluent Cloud for serverless Apache Flink stream processing and global distribution 

Data from edge devices is often relayed to a central system in order to ensure global data consistency, centralize data storage and processing, and to facilitate easier management and analysis of the information. Centralization also enhances security and enables better decision-making through comprehensive data aggregation.

Aggregate and process data streams at and from the edge before moving to Confluent Cloud for distribution across 120+ pre-built source and sink connectors

Provided as a fully managed, cloud-native service on AWS, Azure, and Google Cloud, Confluent Cloud is the common destination for data streams from the edge requiring advanced processing and global replication. Powered by the Kora Engine, Confluent Cloud comes packed with GBps elastic scaling, infinite storage, a 99.99% uptime SLA, 120+ pre-built connectors, stream processing, stream governance, and more.

Specifically designed for data replication between different Kafka environments including Confluent Platform and Confluent Cloud, cluster linking simplifies the management of data streams between edge and mothership systems, ensuring consistency, high availability, and efficient data transfer without the need for complex, custom solutions. This includes replication from locations with limited or unreliable network connections, such as those at the edge—if connectivity is interrupted, cluster linking simply picks up where it left off once restored. This ensures reliable, real-time data availability across the entire business, facilitating centralized analytics, monitoring, and decision-making while maintaining the flexibility and efficiency of edge processing. Additionally, Confluent integrates with AWS Wavelength—leveraging 5G connectivity from Verizon to support low-latency edge computing workloads for real-time applications such as industrial IoT and machine learning. 

But most processing will not take place at the edge, of course. Fully integrated with Kafka on Confluent Cloud, serverless Apache Flink allows businesses to easily build high-quality, reusable data streams with data from edge devices or any other source.

Flink is now generally available across all three major cloud service providers, providing customers with a true multicloud solution and the flexibility to seamlessly deploy stream processing workloads everywhere their data and applications reside. Backed by a 99.99% uptime SLA, Confluent ensures reliable stream processing with support and services from the leading Kafka and Flink experts.

Get started with Confluent today

Whether you’ve been running Kafka for years or are just getting started with the transformational technology, Confluent’s complete and secure enterprise-grade distribution of Apache Kafka helps accelerate and maximize the value that you get from setting data in motion throughout your organization at the edge, your datacenter, cloud provider, and beyond.

Contact our OEM sales team to learn more.

Ready to try Confluent? Get started for free today or check out the Confluent Platform Quick Start Guide.

  • Greg Murphy is the Staff Product Marketing Manager focused on developing and evangelizing Confluent’s technology partner program. He helps customers better understand how Confluent’s data streaming platform fits within the larger partner ecosystem. Prior to Confluent, Greg held product marketing and product management roles at Salesforce and Google Cloud.

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