Top 10 Alternatives to IBM Event Streams Recently Reviewed By G2 Community
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As a cloud-native AI, analytics and data management platform, SAS Viya enables you to scale cost-effectively, increase productivity and innovate faster, backed by trust and transparency. SAS Viya makes it possible to integrate teams and technology enabling all users to work together successfully to turn critical questions into accurate decisions.
A next-generation iPaaS, reimagined for new hybrid data management challenges with a microservices architecture.
Amazon Kinesis Data Streams is a serverless streaming data service that makes it easy to capture, process, and store data streams at any scale.
Red Hat® OpenShift® Streams for Apache Kafka is a managed cloud service that provides a streamlined developer experience for building, deploying, and scaling new cloud-native applications or modernizing existing systems.
Aiven for Apache Kafka is a fully managed streaming platform, deployable in the cloud of your choice. Snap it into your existing workflows with the click of a button, automate away the mundane tasks, and focus on building your core apps.
Apache Kafka is an open-source distributed event streaming platform developed by the Apache Software Foundation. It is designed to handle real-time data feeds with high throughput and low latency, making it ideal for building data pipelines, streaming analytics, and integrating data across various systems. Kafka enables organizations to publish, store, and process streams of records in a fault-tolerant and scalable manner, supporting mission-critical applications across diverse industries. Key Features and Functionality: - High Throughput and Low Latency: Kafka delivers messages at network-limited throughput with latencies as low as 2 milliseconds, ensuring efficient data processing. - Scalability: It can scale production clusters up to thousands of brokers, handling trillions of messages per day and petabytes of data, while elastically expanding and contracting storage and processing capabilities. - Durable Storage: Kafka stores streams of data safely in a distributed, durable, and fault-tolerant cluster, ensuring data integrity and availability. - High Availability: The platform supports efficient stretching of clusters over availability zones and connects separate clusters across geographic regions, enhancing resilience. - Stream Processing: Kafka provides built-in stream processing capabilities through the Kafka Streams API, allowing for operations like joins, aggregations, filters, and transformations with event-time processing and exactly-once semantics. - Connectivity: With Kafka Connect, it integrates seamlessly with hundreds of event sources and sinks, including databases, messaging systems, and cloud storage services. Primary Value and Solutions Provided: Apache Kafka addresses the challenges of managing real-time data streams by offering a unified platform that combines messaging, storage, and stream processing. It enables organizations to: - Build Real-Time Data Pipelines: Facilitate the continuous flow of data between systems, ensuring timely and reliable data delivery. - Implement Streaming Analytics: Analyze and process data streams in real-time, allowing for immediate insights and actions. - Ensure Data Integration: Seamlessly connect various data sources and sinks, promoting a cohesive data ecosystem. - Support Mission-Critical Applications: Provide a robust and fault-tolerant infrastructure capable of handling high-volume and high-velocity data, essential for critical business operations. By leveraging Kafka's capabilities, organizations can modernize their data architectures, enhance operational efficiency, and drive innovation through real-time data processing and analytics.
The Tray Platform empowers anyone to do more, faster by harnessing automation with the leading, low-code general automation platform.
Spark Streaming brings Apache Spark's language-integrated API to stream processing, letting you write streaming jobs the same way you write batch jobs. It supports Java, Scala and Python. Spark Streaming recovers both lost work and operator state (e.g. sliding windows) out of the box, without any extra code on your part.
Cloud Dataflow is a fully-managed service for transforming and enriching data in stream (real time) and batch (historical) modes with equal reliability and expressiveness.
Ably is a realtime data delivery platform providing developers everything they need to create, deliver and manage complex projects. Ably solves the hardest parts so they don’t have to.