Best Streaming Analytics Platforms in the USA

Cumulocity offers an enterprise-grade AIoT platform that connects & manages assets efficiently, transforms raw device data into AI-ready data, and orchestrates innovation from cloud to edge, combined with a team of experts and a large ecosystem of device, technology, and implementation partners to achieve lasting customer success. Cumulocity is the preferred choice for industrial equipment makers to develop high-value digital services and is trusted by leading companies worldwide as their partner, knowing they have access to the software and services needed to power their smart connected products in manufacturing, fleet management, consumer electronics, and more.

IBM® StreamSets enables users to create and manage smart streaming data pipelines through an intuitive graphical interface, facilitating seamless data integration across hybrid and multicloud environments. This is why leading global companies rely on IBM StreamSets to support millions of data pipelines for modern analytics, intelligent applications and hybrid integration. Decrease data staleness and enable real-time data at scale—handling millions of records of data, across thousands of pipelines within seconds. Insulate data pipelines from change and unexpected shifts with drag-and-drop, prebuilt processors designed to automatically identify and adapt to data drift. Create streaming pipelines to ingest structured, semistructured or unstructured data and deliver it to a wide range of destinations.

SQLstream ranks #1 for IoT stream processing & analytics (ABI Research). Used by Verizon, Walmart, Cisco, & Amazon, our technology powers applications across data centers, the cloud, & the edge. Thanks to sub-ms latency, SQLstream enables live dashboards, time-critical alerts, & real-time action. Smart cities can optimize traffic light timing or reroute ambulances & fire trucks. Security systems can shut down hackers & fraudsters right away. AI / ML models, trained by streaming sensor data, can predict equipment failures. With lightning performance, up to 13M rows / sec / CPU core, companies have drastically reduced their footprint & cost. Our efficient, in-memory processing permits operations at the edge that are otherwise impossible. Acquire, prepare, analyze, & act on data in any format from any source. Create pipelines in minutes not months with StreamLab, our interactive, low-code GUI dev environment. Export SQL scripts & deploy with the flexibility of Kubernetes.

eRAG (enterprise RAG) combines the power of real-time operational data with GPT’s fantastic user experience: Chat spontaneously and get immediate answers grounded in a unique understanding of your operational data. With its sophisticated semantic reasoning capabilities, eRAG ensures you get accurate, consistent answers. It answers complex, cross-system questions instantly, supports decisions with suggestions, challenges, and next steps. eRAG connects your business data with external events, so that you can weigh the effect of new tax legislation or weather disruptions on your operations. eRAG combines all your operational data sources so you can get a full, unified picture of your business, offering measurable revenue and efficiency outcomes. Through a self-serve UI, IT teams can connect SQL-based databases like Oracle, PostgreSQL, SAP and other systems in just a few clicks. And you can get up and running in 2–3 weeks - no data prep needed.

A scalable cloud database for real-time analysis on large and streaming datasets. Kinetica is designed to harness modern vectorized processors to be orders of magnitude faster and more efficient for real-time spatial and temporal workloads. Track and gain intelligence from billions of moving objects in real-time. Vectorization unlocks new levels of performance for analytics on spatial and time series data at scale. Ingest and query at the same time to act on real-time events. Kinetica's lockless architecture and distributed ingestion ensures data is available to query as soon as it lands. Vectorized processing enables you to do more with less. More power allows for simpler data structures, which lead to lower storage costs, more flexibility and less time engineering your data. Vectorized processing opens the door to amazingly fast analytics and detailed visualization of moving objects at scale.

Esper Enterprise Edition is a distributable platform for linear and elastic horizontal scalability and fault-tolerant event processing. EPL editor and debugger; Hot deployment; Detailed metric and memory use reporting with break-down and summary per EPL. Data Push for multi-tier CEP-to-Browser delivery; Management of Logical and Physical Subscribers and Subscriptions. Web-based user interface for managing all aspects of multiple distributed engine instances with JavaScript and HTML 5. Composable, configurable and interactive displays of distributed event streams or series; Charts, Gauges, Timelines, Grids. JDBC-compliant client and server endpoints for interoperability. Esper Enterprise Edition is a closed-source commercial product by EsperTech. The source code is made available to support customers only. Esper Enterprise Edition is a distributable platform for linear and elastic horizontal scalability and fault-tolerant event processing.

Evam's Continuous Intelligence Platform combines multiple products for processing and visualizing real-time data. It runs real-time machine learning models on streaming data, while enriching the real-time data with a smart in-memory caching mechanism. EVAM empowers telecommunications, financial services, retail, transportation and travel companies to maximize their business value. Through continuous intelligence platform with machine learning capabilities. EVAM processes real-time data and designs and orchestrates customer journeys visually with advanced analytical models, machine learning, and artificial intelligence algorithms. EVAM enables enterprises to engage their customers using their data across all channels, including legacy ones, in real-time. Collect billions of events and process them in real-time. Understand each customer's needs and attract, engage, and retain them more effectively.

Oracle Stream Analytics allows users to process and analyze large scale real-time information by using sophisticated correlation patterns, enrichment, and machine learning. It offers real-time actionable business insight on streaming data and automates action to drive today’s agile businesses. Visual GEOProcessing with GEOFence relationship spatial analytics. New Expressive Patterns Library, including Spatial, Statistical, General industry and Anomaly detection, streaming machine learning. Abstracted visual façade to interrogate live real time streaming data and perform intuitive in-memory real time business analytics.

​The Hitachi Streaming Data Platform (SDP) is a real-time data processing system designed to analyze large volumes of time-sequenced data as it is generated. By leveraging in-memory and incremental computational processing, SDP enables swift analysis without the delays associated with traditional stored data processing. Users can define summary analysis scenarios using Continuous Query Language (CQL), similar to SQL, allowing for flexible and programmable data analysis without the need for custom applications. The platform's architecture comprises components such as development servers, data-transfer servers, data-analysis servers, and dashboard servers, facilitating scalable and efficient data processing workflows. SDP's modular design supports various data input and output formats, including text files and HTTP packets, and integrates with visualization tools like RTView for real-time monitoring.

Cloudera DataFlow for the Public Cloud (CDF-PC) is a cloud-native universal data distribution service powered by Apache NiFi ​​that lets developers connect to any data source anywhere with any structure, process it, and deliver to any destination. CDF-PC offers a flow-based low-code development paradigm that aligns best with how developers design, develop, and test data distribution pipelines. With over 400+ connectors and processors across the ecosystem of hybrid cloud services—including data lakes, lakehouses, cloud warehouses, and on-premises sources—CDF-PC provides indiscriminate data distribution. These data distribution flows can then be version-controlled into a catalog where operators can self-serve deployments to different runtimes.