Top 18 Open Source and Commercial Stream Analytics Platforms
Stream Analytics helps to develop and deploy solutions to gain real time insights from devices, sensors, and applications by real time stream processing in the cloud. Stream Analytics enables to perform real time analytics for Internet of Things solutions, stream millions of events per second, provide mission critical reliability and performance, also deliver real time dashboards and alerts over data from devices and applications, correlate across multiple streams of data and use SQL based language for development. Stream Analytics customers deploy and monitor streaming jobs.
Applications of stream analytics includes personalized, real-time stock-trading analysis and alerts offered by financial services companies, real-time fraud detection; data and identity protection services, analysis of data generated by sensors and actuators, web clickstream analytics, customer relationship management (CRM) alerts, supply chain alerts, transportation alerts.
Top Open Source and Commercial Stream Analytics Platforms
Open Source : Apache Flink, Spark Streaming, Apache Samza, Apache Storm
Commercial : IBM, Software AG, Azure Stream Analytics, DataTorrent, StreamAnalytix, SQLstream Blaze, SAP Event Stream Processor, Oracle Stream Analytics, TIBCO’s Event Analytics, Striim, Informatica, WSO2 Complex Event Processor, SAS Event Stream Processing, Cisco Connected Streaming Analytics.
Top Open Source Stream Analytics Platforms
Apache Flink is an open source platform for distributed stream and batch data processing. Flink’s core is a streaming dataflow engine that provides data distribution, communication, and fault tolerance for distributed computations over data streams. Flink includes several APIs for creating applications that use the Flink engine: DataStream API for unbounded streams embedded in Java and Scala, DataSet API for static data embedded in Java, Scala, and Python, and Table API with a SQL-like expression language embedded in Java and Scala.
Spark Streaming makes it easy to build scalable fault-tolerant streaming applications. Spark Streaming brings Apache Spark's language-integrated API to stream processing, letting you write streaming jobs the same way you write batch jobs. It supports Java, Scala and Python.
Apache Samza is a distributed stream processing framework. It uses Apache Kafka for messaging, and Apache Hadoop YARN to provide fault tolerance, processor isolation, security, and resource management.
Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing.
Top Stream Analytics Platforms Vendors
IBM Streams is an advanced analytic platform that allows user-developed applications to quickly ingest, analyze and correlate information as it arrives from thousands of data stream sources. The solution can handle very high data throughput rates, up to millions of events or messages per second.
2.Software AG's Apama Streaming Analytics
Software AG's Apama Streaming Analytics is a platform for streaming analytics and intelligent automated action on fast-moving big data. Combining event processing, messaging, in-memory data management and visualization, this platform is a complete solution to turn relentless data streams like those produced by the Internet of Things (IoT) into meaningful real-time metrics.
3.Azure Stream Analytics
Azure Stream Analytics lets you rapidly develop and deploy low-cost solutions to gain real-time insights from devices, sensors, infrastructure, and applications. Use it for Internet of Things (IoT) scenarios, such as real-time remote management and monitoring or gaining insights from devices like mobile phones and connected cars.
DataTorrent’s platform is a robust solution for real-time stream analytics, simplifying the development and running of real-time Big Data applications. DataTorrent’s harnesses open-source Hadoop and commodity hardware for real-time stream processing on a massive scale. The platform is designed with a clear separation between the application logic and runtime operations. This enables developers to focus on their code, and not on the management overhead. With DataTorrent, applications scale automatically and also self-heal – with no state loss, no message loss, and no human intervention or code changes.
StreamAnalytix is a multi-engine, enterprise-grade, Open Source based platform. With support for Apache Storm and Spark Streaming, StreamAnalytix is designed to rapidly build and deploy streaming analytics applications for any industry vertical, any data format, and any use case. Enables to create real-time streaming data analytics applications in minutes with a powerful visual editor , with built-in sources and sinks including HDFS, Amazon S3, RDBMS and EDWs, Kafka, Cassandra and Elasticsearch to easily connect different pipelines together with sub-system integration and extend the built-in sources and sinks with reusable custom operator.
SQLstream Blaze is a stream processing suite for real-time operational intelligence from the integration, analysis and visualization of high volume, high velocity machine data. SQLstream Blaze includes the core stream processor, s-Server, with real-time visualization products for developers and enterprise power users, platform management tools, and a comprehensive suite of agents adaptors for machine data and enterprise integration.
7.SAP Event Stream Processor
SAP Event Stream Processor capture, analyze and act on real-time event streams with the event processing platform . It analyze and act on events as they happen with SAP Event Stream Processor and delivers real-time stream processing and analytics, maximize responsiveness and agility, take full advantage of the Internet of Things (IoT) and develop new applications with embedded CEP functionality.
8.Oracle Stream Analytics
Oracle Stream Analytics platform provides a compelling combination of an easy-to-use visual façade to rapidly create and dynamically change Real Time Streaming Analytics applications, together with a comprehensive run-time platform to manage and execute these solutions.
9.TIBCO’s Event Analytics
TIBCO’s event-driven solutions help you find insights by augmenting your traditional data intelligence processes, and by discovering actions that have the potential to transform your company. TIBCO’s Event Analytics solutions make it easier to interact with machines, collaborate, and act faster than ever before.TIBCO provides a suite of event processing and streaming analytics products that allow you to Gain real-time, actionable operational intelligence, Make better decisions faster while that intelligence is relevant and Take actions to improve operational outcomes.
Striim combines both streaming data integration and streaming operational intelligence in a single platform. Striim enables correlation of streaming information across multiple streams, anomaly detection, and the ability to identify interesting events and patterns while the data is in-motion.
Informatica’s solution is optimized to collect and stream structured, unstructured, and machine data directly into high performance data warehousing appliances, Hadoop, or any analytics platform. It provides access and integrate new data sources, enable streaming data collection over a LAN or WAN, correct, standardize, and de-duplicate data before loading and redirect workloads for optimized performance (e.g., ELT mode).
12.WSO2 Complex Event Processor
WSO2 Complex Event Processor provides real-time analytics to help identify the most meaningful events and patterns from multiple data sources, analyze their impacts, and act on them in real time. It combines into one integrated platform real-time and batch analysis of data with predictive analytics via machine learning to support the multiple demands of Internet of Things solutions, as well as mobile and Web apps.
13.SAS Event Stream Processing
SAS Event Stream Processing, analyze high-velocity big data while it’s in motion, helping you know what requires action, and what can be ignored. Event stream processing from SAS provides in-stream data quality, prebuilt analytic expressions and advanced analytics integration for complex pattern matching.
14.Cisco Connected Streaming Analytics
Cisco Connected Streaming Analytics (CSA) platform delivers insight from high-velocity streams of live data from multiple sources so you can take immediate action. CSA applies to use cases across a wide range of industries and business applications.