Top 10 Amazon Kinesis Data Analytics Alternatives & Competitors

Top 10 Alternatives to Amazon Kinesis Data Analytics Recently Reviewed By G2 Community

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SAS Viya
By SAS Institute Inc.
4.3/5
(729)
Product Description
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.
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Reviewers say compared to Amazon Kinesis Data Analytics, SAS Viya is:
Better at meeting requirements
More expensive
Better at support
Categories in common with Amazon Kinesis Data Analytics:
Event Stream Processing

HubSpot Data Hub
By HubSpot
4.5/5
(572)
Product Description
HubSpot Operations Hub allows you to keep all your contacts in 2-Way, Real Time Sync no matter if you use (Gmail/Outlook, Salesforce, Pipedrive, Constant Contact, Prosperworks, HubSpot, MailChimp or ActiveCampaign to name a few).
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Reviewers say compared to Amazon Kinesis Data Analytics, HubSpot Data Hub is:
Better at meeting requirements
Better at support
More usable
Categories in common with Amazon Kinesis Data Analytics:
Stream Analytics

Tealium Customer Data Hub
By Tealium
4.3/5
(415)
Product Description
Tealium AudienceStream™ is the market-leading Customer Data Platform, combining robust audience management and data enrichment capabilities resulting in unified customer profiles and the ability to take immediate, relevant action.
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Reviewers say compared to Amazon Kinesis Data Analytics, Tealium Customer Data Hub is:
Better at support
Better at meeting requirements
More expensive
Categories in common with Amazon Kinesis Data Analytics:
Stream Analytics
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Aiven for Apache Kafka
By Aiven
4.3/5
(245)
Product Description
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.
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Reviewers say compared to Amazon Kinesis Data Analytics, Aiven for Apache Kafka is:
Better at meeting requirements
Better at support
More expensive
Categories in common with Amazon Kinesis Data Analytics:
Event Stream Processing

Evam
By EVAM
4.8/5
(210)
Product Description
evamX is a real-time customer engagement platform designed to help businesses create personalized, context-aware journeys across digital and offline channels. With its no-code scenario designer, AI-powered decisioning, and advanced stream analytics, evamX enables marketing, CX, and digital teams to act instantly on customer behavior and data. Whether it’s triggering next-best actions, sending relevant offers, or managing omnichannel campaigns, evamX empowers enterprises to boost engagement, increase retention, and drive measurable business results.
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Reviewers say compared to Amazon Kinesis Data Analytics, Evam is:
Better at meeting requirements
Better at support
More expensive
Categories in common with Amazon Kinesis Data Analytics:
Stream Analytics

Tray.ai
By Tray.io
4.5/5
(157)
Product Description
The Tray Platform empowers anyone to do more, faster by harnessing automation with the leading, low-code general automation platform.
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Reviewers say compared to Amazon Kinesis Data Analytics, Tray.ai is:
Better at meeting requirements
Better at support
More expensive
Categories in common with Amazon Kinesis Data Analytics:
Event Stream Processing

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.
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Reviewers say compared to Amazon Kinesis Data Analytics, Apache Kafka is:
Better at meeting requirements
More usable
Better at support
Categories in common with Amazon Kinesis Data Analytics:
Event Stream Processing
Stream Analytics

IBM StreamSets
By IBM
4/5
(116)
Product Description
StreamSets DataOps Platform is an end-to-end data engineering platform to design, deploy, operate and optimize data pipelines to deliver continuous data. StreamSets offers a single pane of glass for batch, streaming, CDC, ETL and ML pipelines with built-in data drift protection for full transparency and control across hybrid, on-premise and multi-cloud environments.
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Reviewers say compared to Amazon Kinesis Data Analytics, IBM StreamSets is:
More expensive
Better at meeting requirements
Better at support
Categories in common with Amazon Kinesis Data Analytics:
Event Stream Processing
Stream Analytics

Confluent
By Confluent
4.4/5
(113)
Product Description
A stream data platform.
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Reviewers say compared to Amazon Kinesis Data Analytics, Confluent is:
More expensive
Better at meeting requirements
Better at support
Categories in common with Amazon Kinesis Data Analytics:
Event Stream Processing
Stream Analytics

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