Confluent Kafka vs. AWS Kinesis vs. Azure Event Hubs

onfluent-kafka-vs-aws-kinesis-vs-azure-event-hubs-feature-image.

Confluent Kafka vs. AWS Kinesis vs. Azure Event Hubs

confluent-kafka-vs-aws-kinesis-vs-azure-event-hubs

Written by Imran Abdul Rauf

Technical Content Writer

February 22, 2022

Distributed log technologies like Amazon Kinesis, Confluent Kafka, and Azure Event Hubs have grown quite well through time. In addition, the tools have newly incorporated solutions for using data across different use cases. This article compares the three data streaming platforms, compatibility with multi-cloud systems, managed service, availability, and monitoring tendencies.

Confluent Kafka

Confluent Kafka has much more to offer than Apache Kafka, i.e., a primary platform for data in motion. But self-supporting the open-source project also requires the IT personnel to manage low-level data infrastructure. Hence, businesses often prefer Confluent, which keeps their focus on aspects required. Driven by Kafka, Confluent offers a cloud-native platform and works to set your data in motion regardless of its location and applications used.

  • Fully managed: As a fully managed service, Confluent Cloud employs enterprise-level Apache Kafka for real-time data streaming across any cloud system, besides the 120+ fully managed data connectors for seamless, real-time cloud ETL, data pipelines, and integration.
  • Multi-cloud strategy: Companies use Confluent Kafka to achieve a robust multi-cloud strategy, which means no more problems regarding cluster sizing, over-provisioning, failover design, and infrastructure management. With a cloud-native service for Apache Kafka, enterprises can leverage multi-cloud management at scale with complete security and efficiency. The platform ensures that workloads are automated throughout the on-prem, cloud, and multi-cloud environments.

Amazon Kinesis

Amazon Kinesis is a serverless streaming data service used to collect, process, and analyze data and video streams in real-time, promptly. The platform provides ample power to process and stream data at any scale and allows you to choose the features that best fit your tool’s requirements. Moreover, users can process and analyze data upon receiving and promptly respond without waiting until all the data is collected.

Kinesis employs plenty of use cases like building video and analytics applications, transitioning from batch to real-time analytics, creating real-time tools, analyzing IoT data, etc.

  • Real-time: Kinesis allows users to ingest, buffer, and sort streamlining data to acquire valuable insights in seconds or minutes.
  • Fully managed: Kinesis is fully managed and runs streaming tools without requiring any manual monitoring and interventions.
  • Scalable: The tool can handle any load of streaming data and can process data from numerous sources without limiting the performance through high latencies.

Azure Event Hubs

Azure Event Hubs is a big data streaming and event ingestion service mainly used for application logging, anomaly detection, live dashboarding, transaction processing, archiving data, and telemetry processing and streaming purposes. Event Hubs works as a fully managed platform-as-a-service (PaaS), supports real-time batch processing, collects event data, and is scalable for a fully-equipped ecosystem.

The platform provides a distributed stream processing system capable of low latency and seamless integration, alongside data and analytics features all around Azure to create your entire big data pipeline.

  • Fully managed PaaS: Event Hubs requires minor configuration and management overhead, which permits the users to focus on business solutions. Hubs for Apache Kafka produces PaaS Kafka experiences without incurring any management, configuration, and running your clusters issues.
  • Real-time for batch processing: Events Hubs uses a partitioned consumer model for facilitating multiple applications to process streams concurrently and giving users the handle the processing speed.
  • Scalable: Event Hubs can start the streams in megabytes and further scale up to gigabytes and terabytes.
  • Event Hubs for Apache Kafka: The platform enables Apache Kafka tools and clients to communicate to Event Hubs. In addition to that, users don’t need to set up, configure, and manage their Zookeeper and Kafka clusters or even utilize a Kafka-as-a-service feature not relevant for Azure.

Thoughts

Different business owners and IT personnel will have different viewpoints and comments on the use and performance of Azure Event Hubs, Amazon Kinesis, and Confluent Kafka.

For example, reviewers feel that Event Hubs is better for business and quality of ongoing product support than Confluent. While Confluent gets the edge over Azure Event Hubs for feature updates and roadmaps.

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