Streaming Analytics Platforms - Further Information

Striim offers a unified, enterprise-level combination of continuous data integration, real time CDC, stream processing and streaming analytics.

This paper looks in more detail at the component parts of a good data management solution, and how they can add value to supply chain ISVs and SIs.

An Enterprise Information Architecture can be viewed as the operating system of the Enterprise by means of which all applications are deployed and executed.

In a data-driven world, organisations need to be able to manage the corporate data resource intelligently and flexibly. A data fabric is the key enabler.

IT is critical for financial services companies & often the prime or only source of competitive advantage.

The Diffusion Intelligent Data Platform is a real-time data management platform that has been purpose-built to operate with event data.

This report describes and evaluates the Stream Processing space and the market trends and vendor solutions within it.

Ververica provides commercial support for Apache Flink and extends it via the Ververica Platform.

Confluent develops two enterprise-grade offerings that prominently include, and build upon, Apache Kafka: Confluent Cloud, and Confluent Platform.

Streaming analytics extracts actionable insights from streaming data in real-time. This report discusses the streaming analytics space and its solutions.

Esper is a stream processing/analytics platform that markets itself as “pure-play CEP”, and in fact was one of the first CEP engines introduced to the market.

Gathr is a self-service-oriented data pipeline platform, and more specifically, an all-in-one platform for creating, managing, and deploying data pipelines.

Guavus SQLstream offers efficient and cost-effective streaming analytics across a range of enterprise environments, including the edge and the cloud.

Informatica offers stream processing as part of Intelligent Data Management Cloud (IDMC), a broad, cloud-ready, and well-integrated data management platform.

KX (or, more properly, KX Streaming Analytics) is a highly performant streaming analytics platform layered on top of a time-series database.

Rumi is a software platform that enables enterprises to embed rich, real-time analytical data processing directly into their transactional applications.

SAS ESP is an event stream processing platform that enables streaming analytics by applying SAS’s existing analytics capabilities to data in motion.

Striim offers enterprise-level, continuous, and real-time data integration, CDC, and stream processing, including streaming integration and analytics.

TIBCO Streaming is a streaming analytics product that strives to help you to better connect, understand, and interact with your streaming data.

Rumi is a software platform that enables enterprises to embed rich, real-time analytical data processing directly into their transactional applications.

Kx is built on the kdb+ database, an in-memory columnar database with both streaming and timeseries capabilities.

While this paper focuses on Software AG Apama it is not a review of Apama per se, but rather of Software AG’s approach to IoT Analytics.

This paper discusses and evaluates StreamAnalytix, an analytics platform for both real-time, streaming data and for batch processing.

Kx technology supports the rapid analysis of time-series data and, more generally, the analysis of streaming, in-memory and historical data.

This Market Update considers both stream processing platforms upon which you might build analytics as well as streaming analytics platforms per se.

IBM Streams (previously IBM InfoSphere Streams) is a high performance, low latency platform for analysing and scoring data in real-time. It is a part of the IBM Watson & Cloud Platforms.

Software AG’s Apama was first released in 2001 as a complex event processing platform targeted at supporting algorithmic trading in capital markets. It remains a market leader in that space though IoT now forms a major target market.

SQLstream Blaze, which is the name for the SQLstream suite of products, is a streaming analytics platform that is available for both on-premises and cloud-based deployments. As it’s name suggests it is based on ANSI standard SQL.

EsperTech is the company behind the open source Esper (Java based) and NEsper (.Net based) products, where the former was one of the first CEP engines to be introduced.

SAS has been offering stream processing for a number of years, especially for fraud applications. However, a major driver is now the Internet of Things (IoT) and applications such as predictive maintenance and smart “anything”.

TIBCO StreamBase is the streaming analytics component of TIBCO’s ‘connected intelligence’ product collection, that allows businesses to better connect, understand and interact with their data.

Striim is a streaming integration and analytics platform. Without de-emphasising the latter, it focuses on the former.

Founded in 2018, Streamlio provides an intelligent platform for handling (streaming) data in motion.

Streaming analytics enables businesses to respond appropriately and in real-time to context-aware insights delivered from fast data.

Stream processing is enjoying a resurgence thanks to big data and the emergence of the Internet of Things.

Ancelus is an ACID compliant database with extreme performance characteristics, developed and marketed by Time Compression Strategies Corporation (TCSC).

The fourth issue for big data is integration, where agility will be required (as it is for governance)

TIBCO came to London for their user conference (transFORM2013). This year's theme was all about big data and TIBCO's senior executives outlined their strategy for their platform.

Should it be called CEP? Is CEP only about real-time BI? These were questions we answered 6 years ago. Also, a mention of some Hadoop-based CEP engines.

The C in CEP may stand for "complex" but CEP itself is not complex

There has been some delay in this, the third and final article on untangling events. And just as well. I initially asked the question as to whether the log and event management vendors were more...

The purpose of this series of articles is to identify if all of the different approaches to handling events are part of a single market or whether they should be treated as separate. In the first...

It is now pretty much agreed that in data warehousing environments you need a massively parallel processing (MPP) architecture in order to handle very large data volumes. The important factor is...

Complex event processing, business event processing, security event management, log management, data retention systems, event-driven architecture, event warehousing: are these topics and other...

A couple of months ago I reported on the announcement of IBM InfoSphere Streams as the company's high end (complex) event processing platform. I also described IBM's approach to event processing...

This paper seeks to explain what events are, why they are important to your business, and what the options are for processing and managing these events.

A discussion of some the various ways in which the event processing market is moving forward.

This report describes the technology and what it is good for, including providing a series of use cases. In other words: what event processing is, how it works and where you might use it.

Will the current approach to real-time data warehousing be replaced with a combination of event processing engines and data warehouse appliances?

There is a difference between ESP and CEP and between CEP and business process modelling or rules engines: AptSoft provides a good example.