The 10 Best Streaming Analytics Tools and Real-Time Platforms for 2022

The Best Streaming Analytics Tools

Solutions Review’s listing of the best streaming analytics and real-time platforms (commercial) is an annual mashup of products that best represent current market conditions, according to the crowd. Our editors selected the best streaming analytics tools and real-time platforms based on each solution’s Authority Score; a meta-analysis of real user sentiment through the web’s most trusted business software review sites and our own proprietary five-point inclusion criteria.

The editors at Solutions Review have developed this resource to assist buyers in search of the best streaming analytics tools (real-time platforms) to fit the needs of their organization. Choosing the right vendor and solution can be a complicated process — one that requires in-depth research and often comes down to more than just the solution and its technical capabilities. To make your search a little easier, we’ve profiled the best streaming analytics tools (real-time) providers all in one place. We’ve also included platform and product line names and introductory software tutorials straight from the source so you can see each solution in action.

Note: The best streaming analytics tools (real-time) are listed in alphabetical order.

The Best Streaming Analytics Tools and Real-Time Platforms (Commercial)

Altair

Platform: Altair Panopticon

Related products: Altair Knowledge Studio, Altair Knowledge Studio for Apache Spark, Altair Knowledge Hub, Altair Knowledge Works Altair Monarch

Description: Altair (formerly Datawatch) offers a suite of solutions through its Knowledge Works portfolio and is headlined by an advanced data mining and predictive analytics workbench called Knowledge Studio. The product features patented Decision Trees, Strategy Trees, and a workflow and wizard-driven graphical user interface. It also includes capabilities for data preparation tasks, visual data profiling, advanced predictive modeling, and in-database analytics. Users can import and export using common languages like R and Python, as well as data types like SAS, RDBMS, CSV, Excel, and SPSS. 

Learn more and compare products with the Solutions Review Buyer’s Guide for Data Science and Machine Learning Platforms.

Amazon Web Services

AWS 150

Platform: Amazon Kinesis

Related products: Amazon QuickSight

Description: Amazon Kinesis Kinesis enables customers to collect, process, and analyze real-time, streaming data. Its most prominent features ensure process streaming data at any scale, along with the flexibility to choose tools to best suit your specific situation. Kinesis lets you ingest real-time data like video, audio, application logs, website clickstreams, and IoT telemetry data for machine learning, analytics, and other applications. The platform is made up of Kinesis Video Streams, Kinesis Data Streams, Kinesis Firehose, and Kinesis Data Analysis.

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Google

Platform: Google Cloud Dataflow

Related products: Google Cloud AI Platform, Google Cloud Data Fusion, Google Cloud AutoML, Google BigQuery ML, Google AI Platform Notebooks, Google TensorFlow

Description: Google Cloud Dataflow is a unified stream and batch processing tool offering a serverless architecture. It is available as a fully-managed service and touts automated provisioning and management of processing resources. Dataflow features horizontal autoscaling of worker resources to maximize resource utilization and OSS community-driven innovation with Apache Beam SDK. Additional key features include flexible scheduling and pricing, ready-to-use AI patterns, and dynamic work rebalancing.

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IBM

Platform: IBM Streams

Related products: IBM Watson Analytics, IBM Watson Studio, Cognos Analytics

Description: IBM Stream evaluates a broad range of streaming data (unstructured text, video, audio, geospatial, and sensor). Customers can combine IBM Streams with other IBM Cloud Pak for Data capabilities to help enable their data scientists to collaboratively build models to apply to stream flows. Users can also analyze massive amounts of data in real-time. Key features include development support, rich data connections, and analysis and visualization.

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Microsoft (Azure)

Platform: Azure Stream Analytics

Related products: Power BI, Power BI Desktop, Power BI Report Server

Description: Microsoft Azure Stream Analytics is an easy-to-use, real-time analytics service that was built end-to-end and features a serverless streaming pipeline. The product is extensible and touts custom code and built-in machine learning for more advanced scenarios. Azure Stream Analytics is an end-to-end pipeline and can be production-ready quickly via familiar SQL syntax. Enterprise-grade reliability through built-in recovery and machine learning is present as well.

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Oracle

Platform: Oracle Stream Analytics

Related products: Oracle Analytics Cloud, Oracle Data Visualization Desktop

Description: Oracle Stream Analytics lets users process and analyze large-scale real-time information by using sophisticated correlation patterns, enrichment, and machine learning. It also includes real-time actionable business insight on streaming data and automates action. Oracle Stream Analytics enables users to identify events of interest by executing queries against event streams in real time. It allows for the creation of custom operational dashboards that provide real-time monitoring, transform streaming data, or raise alerts based on stream analysis.

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RapidMiner

Platform: RapidMiner Studio

Related products: RapidMiner AI Hub, RapidMiner Go, RapidMiner Notebooks, RapidMiner AI Cloud

Description: RapidMiner offers a data science platform that enables people of all skill levels across the enterprise to build and operate AI solutions. The product covers the full lifecycle of the AI production process, from data exploration and data preparation to model building, model deployment, and model operations. RapidMiner provides the depth that data scientists needbut simplifies AI for everyone else via a visual user interface that streamlines the process of building and understanding complex models.

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SAP

Platform: SAP HANA Streaming Analytics

Related products: SAP Analytics Cloud, SAP BusinessObjects BI, SAP Crystal Solutions

Description: SAP offers a broad range of BI and analytics tools in both enterprise and business-user driven editions. The company’s flagship BI portfolio is delivered via on-prem (BusinessObjects Enterprise), and cloud (BusinessObjects Cloud) deployments atop the SAP HANA Cloud. SAP also offers a suite of traditional BI capabilities for dashboards and reporting. The vendor’s data discovery tools are housed in the BusinessObjects solution, while additional functionality, including self-service visualization, are available through the SAP Lumira tool set.

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SAS

Platform: SAS Event Stream Processing

Related products: SAS Visual Data Mining and Machine Learning, SAS Viya, SAS Visual Machine Learning, SAS Visual Data Science, SAS Data Science Programming, SAS Visual Data Decisioning

Description: SAS offers a suite of advanced analytics and data science products which is headlined by SASVisual Data Mining and Machine Learning. The product provides access to data in any format and from any source, as well as automated data preparation and data lineage and model management. SAS Visual Data Mining and Machine Learning automatically generates insights for common variables across models. It also features natural language generation for creating project summaries. The companion SAS Model Manager enables users to register SAS and open-source models within projects or as standalone models.

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TIBCO

Platform: TIBCO Streaming

Related products: TIBCO Spotfire, TIBCO Jaspersoft, TIBCO Data Science

Description: TIBCO Streaming applied learning algorithms to live streaming data in real-time, whether they’re embedded in applications to automate decisions. The product is designed for business intelligence professionals and developers via no-code visual development tools. It’s also enterprise-ready with high-performance connectors, advanced analytics, development tools, extensibility, usability, and high availability. Key features include Dynamic Learning Operators, Anywhere Connection, Operationalized Data Science, and Accelerators.

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Timothy King
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