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.
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).
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.
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.
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.
The Tray Platform empowers anyone to do more, faster by harnessing automation with the leading, low-code general automation platform.
Cloudera Enterprise Core provides a single Hadoop storage and management platform that natively combines storage, processing and exploration for the enterprise.
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.
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.
The best alternatives to Amazon Kinesis Data Analytics include SAS Viya (4.3/5 stars, 800 reviews), HubSpot Data Hub (4.5/5 stars, 576 reviews), Tealium Customer Data Hub (4.3/5 stars, 475 reviews), and Spotfire Analytics (4.2/5 stars, 362 reviews). These platforms offer stronger support, better usability, and more comprehensive feature sets, including AI integration, real-time data activation, and advanced visualization.
Amazon Kinesis Data Analytics lacks advanced AI and machine learning integration, comprehensive real-time customer data unification, and extensive out-of-the-box data visualization and predictive analytics capabilities found in alternatives.
Reviewers recommend SAS Viya for its powerful AI, analytics, and cloud-native scalability with 800 reviews praising its ease of use and integration. HubSpot Data Hub is favored for its real-time, bidirectional data sync and user-friendly interface, supported by 576 reviews highlighting its automation and data quality features. Tealium Customer Data Hub is recommended for its robust real-time customer data unification, extensive integrations, and audience segmentation capabilities, with 475 reviews emphasizing its flexibility and activation power. Spotfire Analytics is noted for its advanced data visualization, predictive analytics, and integration with R and Python, supported by 362 reviews praising its interactive dashboards and ease of use.
According to G2 data, Amazon Kinesis Data Analytics has an average rating of 4.1/5 from 14 reviews, while SAS Viya holds a higher average rating of 4.3/5 based on 800 reviews. SAS Viya leads in meeting requirements with a score of 8.5 versus 7.4 for Amazon Kinesis Data Analytics, a 1.1-point advantage. In usability, Amazon Kinesis Data Analytics scores slightly higher at 8.3 compared to SAS Viya's 8.2. SAS Viya also outperforms in support (8.4 vs. 7.6) and ease of setup (8.0 vs. no data for Amazon Kinesis). Users praise Amazon Kinesis Data Analytics for its easy integrations, cost efficiency, real-time analytics, cloud services, and security features, but note challenges with complexity, setup, learning curve, and limited customization. In contrast, SAS Viya is highly regarded for ease of use (234 mentions), extensive features (175 mentions), advanced analytics, data visualization, performance efficiency, flexibility, scalability, and strong integration with open-source tools like Python and R. However, SAS Viya users report a steep learning curve (105 mentions), complexity (103 mentions), high cost (78 mentions), and setup difficulties (44 mentions). Overall, SAS Viya is favored for its comprehensive analytics capabilities, scalability, and enterprise-grade features, while Amazon Kinesis Data Analytics is noted for simpler integration and cost-effectiveness but with less feature depth and customization.
Users choose SAS Viya over Amazon Kinesis Data Analytics primarily for its comprehensive analytics capabilities and enterprise readiness. SAS Viya offers a unified, cloud-native platform that integrates data preparation, advanced analytics, AI, and visualization, supporting multiple programming languages including SAS, Python, and R. It excels in handling large datasets efficiently with in-memory processing and provides strong governance, scalability, and performance efficiency, which are critical for regulated and large-scale environments. With 234 mentions of ease of use and 175 mentions of rich features, users appreciate its intuitive interface, drag-and-drop modeling, and seamless integration with open-source tools. SAS Viya’s advanced machine learning and AI capabilities, along with robust support (8.4 vs. 7.6), further differentiate it. Despite a steep learning curve and higher cost cited by 78 users, organizations prioritize SAS Viya’s ability to accelerate analytics lifecycles, improve decision-making, and support complex workflows. Its cloud-native architecture and scalability make it suitable for enterprises seeking a powerful, all-in-one analytics solution that outperforms Amazon Kinesis Data Analytics in meeting requirements by 1.1 points and in support by 0.8 points according to G2 data.