Top 10 Amazon Kinesis Alternatives & Competitors

(26)4.7 out of 5

The Stream Analytics Software solutions below are the most common alternatives that users and reviewers compare with Amazon Kinesis. Other important factors to consider when researching alternatives to Amazon Kinesis include analytics and monitoring. The best overall Amazon Kinesis alternative is Tealium Customer Data Hub. Other similar apps like Amazon Kinesis are HubSpot Data Hub, Spotfire Analytics, Evam, and Apache Kafka. Amazon Kinesis alternatives can be found in Stream Analytics Software but may also be in Event Stream Processing Software or Data Visualization Tools.

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Best Paid & Free Alternatives to Amazon Kinesis

Tealium Customer Data Hub
HubSpot Data Hub
Spotfire Analytics
Evam
Apache Kafka
IBM StreamSets
Confluent
Elastic Stack

Top 10 Alternatives to Amazon Kinesis Recently Reviewed By G2 Community

Browse options below. Based on reviewer data, you can see how Amazon Kinesis stacks up to the competition, check reviews from current & previous users in industries like Information Technology and Services, Consulting, and Business Supplies and Equipment, and find the best product for your business.

Tealium Customer Data Hub

By Tealium

4.3/5

(408)

Product Description

Categories in common with Amazon Kinesis:

By HubSpot

Product Description

Reviewers say compared to Amazon Kinesis, HubSpot Data Hub is:

Easier to admin

Easier to set up

More usable

Categories in common with Amazon Kinesis:

By Spotfire

Product Description

Categories in common with Amazon Kinesis:

Stream Analytics

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Apache Kafka

By The Apache Software Foundation

4.5/5

(126)

Product Description

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.

Categories in common with Amazon Kinesis:

By IBM

Product Description

Categories in common with Amazon Kinesis:

By Confluent

Product Description

Categories in common with Amazon Kinesis:

By Elastic

Product Description

The Elastic Stack, commonly known as the ELK Stack, is a comprehensive suite of open-source tools designed for ingesting, storing, analyzing, and visualizing data in real-time. It comprises Elasticsearch, Kibana, Beats, and Logstash, enabling users to handle data from any source and in any format efficiently. Key Features and Functionality: - Elasticsearch: A distributed, JSON-based search and analytics engine that allows for rapid storage, search, and analysis of large volumes of data. - Kibana: An extensible user interface that provides powerful visualizations, dashboards, and management tools to interpret and present data effectively. - Beats and Logstash: Data ingestion tools that collect and process data from various sources, transforming and forwarding it to Elasticsearch for indexing. - Integrations: A multitude of pre-built integrations that facilitate seamless data collection and connection with the Elastic Stack, enabling quick insights. Primary Value and User Solutions: The Elastic Stack empowers organizations to harness the full potential of their data by providing a scalable and resilient platform for real-time search and analytics. It addresses challenges such as managing large datasets, ensuring high availability, and delivering relevant search results swiftly. By offering a unified solution for data ingestion, storage, analysis, and visualization, the Elastic Stack enables users to gain actionable insights, enhance operational efficiency, and make informed decisions based on their data.

Categories in common with Amazon Kinesis:

Stream Analytics