Streaming Data Processing 10+ Platforms

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Streaming data processing and analytics is gaining momentum as businesses gear up for real-time processing of streaming data – typically from devices and sensors, but also other sources such as financial data and news.

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. Storm is simple, can be used with any programming language. It integrates with the queueing and database technologies you already use. A Storm topology consumes streams of data and processes those streams in arbitrarily complex ways, repartitioning the streams between each stage of the computation however needed.


Informatica Vibe Data Stream for Machine Data (VDS) helps users manage many small pieces of data as they flow in at high rates and accumulate quickly into large volumes. Vibe Data Stream is purpose-built for efficiently collecting all forms of streaming data and delivering it directly to both real-time and batch processing technologies.


IBM® InfoSphere® Streams is an advanced analytic platform that allows user-developed applications to quickly ingest, analyze and correlate information as it arrives from thousands of real-time sources. The solution can handle very high data throughput rates, up to millions of events or messages per second. It uses an Eclipse-based integrated development environment (IDE).


Microsoft Azure Stream Analytics provides out-of-the-box integration with Event Hubs to ingest millions of events per second. Stream Analytics will process ingested events in real-time, comparing multiple real-time streams or comparing real-time streams together with historical values and models. This enables the detection of anomalies, transformation of incoming data, the ability to trigger an alert when a specific error or condition appears in the stream, and power real-time dashboards. Developers describe their desired transformation with SQL-based syntax and the system will automatically distribute it for scale, performance, and resiliency.


Odysseus is an in-memory data management engine that is designed for the processing of big data. Large volumes of data such as continuously occurring events or sensor data can be processed in real time. The platform provides to a multitude of predefined and reusable processing, with which the data can be filtered, correlated, extended, and transformed so as to generate new complex events and high-order information from simple events. Equipped with efficient resource management and processing concepts, extensible and flexible interfaces and a development environment, Odysseus significantly reduces the cost and minimizes the amount of errors in the development of real-time based applications.
Main memory based processing on the basis of established database technologies.


SAP event stream processing solutions combine insight from fast-moving data with historical data in SAP HANA. Users can process, cleanse, and enrich streaming event data in real time, and Filter out redundant, duplicate, or low-level data for better business intelligence. Alters are also generated when anomalies or other events occur.


SAS Event Stream Processing Engine continuously analyzes streaming data that’s constantly on the move in your organization and in the IoT – in real time. You get instantaneous situational awareness for making on-the-spot, fact-based decisions. A unique pattern-matching facility helps define sequential or temporal events to pinpoint anomalies early. Features such as parsing, filters, joins, field calculations and pattern-matching functions make it easier to create quality data and analyze it in stream. Managed with a RESTful interface, the XML factory server provides one place to validate syntax, control analysis windows, define retention policies, provide native failover and more.


Software AG Apama platform allows businesses to analyze and act on high-volume business operations and customer interactions in real-time. Apama rapidly correlates, aggregates and detects patterns across large volumes of fast-moving data from multiple sources. It is built on an in-memory architecture that enables real-time processing of extremely fast, large data volumes—orders of magnitude larger than traditional database-based IT architectures. The platform captures data from any device with low latency. It then performs analytics on this streaming data, also referencing historic information where necessary, to identify business patterns that have happened or are about to happen.


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.


TIBCO StreamBase® Complex Event Processing (CEP) platform is a high-performance system for rapidly building applications that analyze and act on real-time streaming data. Using StreamBase CEP, users can rapidly build real-time systems and deploy them at a fraction of the cost and risk of many other alternatives.


Vitria Operational Intelligence (OI) empowers businesses to quickly act on insights gleaned from streaming data and information – while it still counts. It is the only unified software platform that combines the ability to analyze streaming Big Data, complex events and processes with the ability to take immediate action on discovered insights through automated processes and guided workflows. Enterprises can continuously monitor business activity across multiple applications to gain real-time actionable insight. Examples of activity patterns that can be quickly uncovered, analyzed, and acted upon – in seconds and minutes – include those related to financial transactions, orders, shipments, packages, vehicles, online customers, passengers, and people of interest.