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IBM Streams

IBM Streams

Overview

What is IBM Streams?

A real-time analytics solution that turns fast-moving volumes and varieties into insights. Streams evaluates a broad range of streaming data — unstructured text, video, audio, geospatial and sensor — helping organizations spot opportunities and risks as they happen.Its Eclipse-based, visual…

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Recent Reviews

Streaming Live analysis

9 out of 10
February 14, 2019
Incentivized
IBM Streaming Analytics is being used to analyze real time data. This is limited to the IT Department in analyses of logs and problem …
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Awards

Products that are considered exceptional by their customers based on a variety of criteria win TrustRadius awards. Learn more about the types of TrustRadius awards to make the best purchase decision. More about TrustRadius Awards

Popular Features

View all 9 features
  • Visualization Dashboards (5)
    10.0
    100%
  • Data Ingestion from Multiple Data Sources (5)
    9.0
    90%
  • Machine Learning Automation (5)
    9.0
    90%
  • Real-Time Data Analysis (5)
    8.0
    80%
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Pricing

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IBM Streams

IBM Streams

N/A
Unavailable

What is IBM Streams?

A real-time analytics solution that turns fast-moving volumes and varieties into insights. Streams evaluates a broad range of streaming data — unstructured text, video, audio, geospatial and sensor — helping organizations spot opportunities and risks as they happen. Its Eclipse-based, visual…

Entry-level set up fee?

  • Setup fee optional
For the latest information on pricing, visithttps://console.bluemix.net/catalog/ser…

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services

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Alternatives Pricing

Striim

Striim

$4,400
per month per 100 million Striim events
What is Striim?

Striim is an enterprise-grade platform that offers continuous real-time data ingestion, high-speed in-flight stream processing, and sub-second delivery of data to cloud and on-premises endpoints.

Elecard StreamEye Studio

Elecard StreamEye Studio

Free
What is Elecard StreamEye Studio?

Elecard StreamEye - a software tool for professionals in the video compression field. It enables in-depth bitstream analysis to macroblock level, codec parameters inspection. MPEG-1, MPEG-2, AVC/H.264, HEVC/H.265, AV1, VP9, VVC (preview version). Finding an issue in the elementary stream that may…

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Product Demos

Acquire, Analyze and Act in Real Time with IBM Streams

YouTube
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Features

Streaming Analytics

Streaming Analytics is performing analytic computations on streaming data. Data streams can come from devices, sensors, websites, social media, applications, infrastructure systems, and more.

8.3
Avg 8.1
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Product Details

What is IBM Streams?

A real-time analytics solution that turns fast-moving volumes and varieties into insights. Streams evaluates a broad range of streaming data — unstructured text, video, audio, geospatial and sensor — helping organizations spot opportunities and risks as they happen.

Its Eclipse-based, visual IDE lets solution architects visually build applications or use familiar programming languages like Java™, Scala or Python. Data engineers can connect with virtually any data source — whether structured, unstructured or streaming — and integrate with Hadoop, Spark and other data infrastructures.

Built-in domain analytics — like machine learning, natural language, spatial-temporal, text, and acoustics — create adaptive stream applications.

IBM Streams Integrations

IBM Streams Competitors

IBM Streams Technical Details

Operating SystemsUnspecified
Mobile ApplicationNo
Supported CountriesUnited States, United Kingdom, Australia, Germany
Supported LanguagesEnglish, French, German, Italian, Japanese, Korean, Portugese/Brazil, Spanish, Chinese simplified, Chinese traditional

Frequently Asked Questions

Azure Stream Analytics and Amazon Kinesis are common alternatives for IBM Streams.

Reviewers rate Visualization Dashboards highest, with a score of 10.

The most common users of IBM Streams are from Small Businesses (1-50 employees).
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Comparisons

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Reviews and Ratings

(62)

Reviews

(1-2 of 2)
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April 05, 2018

IBM Streams in the classroom

Verified User
Engineer in Research & Development
Higher Education Company, 1001-5000 employees
Score 8 out of 10
Vetted Review
Verified User
Incentivized
Use Cases and Deployment Scope
I used IBM Streams to design a lab at a university used for analyzing streaming data from IoT devices. The students had to implement signal processing algorithms using the IBM Streams API.
Pros and Cons
  • Easy to integrate with other IBM cloud services.
  • Fairly simple API with essential functions, not overly complicated.
  • The system seems reliable and has high performance.
  • Python API available.
  • Documentation could be more extensive, with more examples, although overall this is not too bad compared to some of the alternative solutions.
  • Seems expensive to use in production.
Likelihood to Recommend
Well suited to process data streams, especially if collected via other IBM services such as IBM IoT.
Return on Investment
  • N/A
Alternatives Considered
I have considered Apache Spark Streaming and Apache Flink. Spark Streaming is still changing too often for my taste and does not seem as easy to connect to IoT data especially for students having limited experience with cloud computing. Interesting signal processing functions require stateful computations, which I find is not so well documented in Spark Streaming. Flink does not have a Python API, which would be useful for an introduction to a course.
March 19, 2018

Stream Processing - Better Knowledge Faster

JS
Jim Sharpe
President
Sharpe Engineering Inc (Computer Software, 1-10 employees)
Score 8 out of 10
Vetted Review
Verified User
Incentivized
Use Cases and Deployment Scope
IBM Streams allows me to solve problems for my clients that would be difficult, impossible, or too expensive to do with other technologies. Most of the applications areas for which I have been applying it have been real-time in nature with a requirement for low end-to-end latency. Lately, a common use case has been to use Streams to ingest and transform incoming events into a form more suitable for storing for subsequent long-term analysis such as model training. For example, ingesting complex nested JSON documents and transforming and enriching that data into a flattened columnar format to be persisted in Spark, Event Store, Parquet files, etc. With this pattern, Streams is one element in an overall data processing pipeline where multiple technologies are optimally employed to do what they do best. Some of the features provided by the IBM Streams platform are particularly well suited for implementing dynamic microservices than can quickly be developed and deployed to provide valuable agility for evolving problem spaces.
Pros and Cons
  • IBM Streams is well suited for providing wire-speed real-time end-to-end processing with sub-millisecond latency.
  • Streams is amazingly computationally efficient. In other words, you can typically do much more processing with a given amount of hardware than other technologies. In a recent linear-road benchmark Streams based application was able to provide greater capability than the Hadoop-based implementation using 10x less hardware. So even when latency isn't critical, using Streams might still make sense for reducing operational cost.
  • Streams comes out of the box with a large and comprehensive set of tested and optimized toolkits. Leveraging these toolkits not only reduces the development time and cost but also helps reduce project risk by eliminating the need for custom code which likely has not seen as much time in test or production.
  • In addition to the out of the box toolkits, there is an active developer community contributing additional specialized packages.
  • Although there is support for developing Streams application in Python and Java as well as a visual programming interface. In order to get the absolute most out of the platform IMO it's still best to develop applications using proprietary SPL (Stream Programming Language). Although SPL is a very effective language for stream processing it does present a barrier to entry that will be avoided with the updated visual development tools which being worked on.
Likelihood to Recommend
Streams is a good fit for situations requiring low end-to-end latency, have complex real-time analytical processing needs on large fast data, or where the reduction of operational costs is important. However, it is very much a data-in-motion technology and not well suited for situations such as some forms of machine learning where the entire historical data set needs to be operated on. Note that it's fairly common to use Streams to perform online scoring using models that were trained offline using other technologies.
Return on Investment
  • Historically Streams has allowed me to solve problems for clients that simply could not have been addressed using any other means. So the business benefit was actually being able to provide a solution to very challenging requirements. However, the relatively recent proliferation of stream processing platforms means there are now more options available that might meet the desired requirements.
  • IBM Streams was a critical component in a data science processing pipeline allowing us to identify a potential biomarker in EEG recordings indicating whether traumatic brain injury patients are at risk for developing post-traumatic epilepsy. This is important for identifying which patients should or should not be included in drug studies.
  • Another successful project employed Streams as part of a pipeline for detecting and classifying sources in underwater acoustic data. Compared to other methods the approach had very high simultaneous levels of both sensitivity and discrimination. It also had the significant benefit of being able to detect signals which had not previously been observed.
Alternatives Considered
The selection of a stream processing platform depends heavily on the details of the requirements. There is no one right answer for all situations. However, IBM Streams typically has the advantage when sub-millisecond latency is important, complex analytics need to be performed on large volumes of data-in-motion, or where minimizing operational costs are important (i.e. doing more work on less hardware).
Other Software Used
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