IBM Streams Reviews & Ratings

IBM Streams

4.0 out of 5 stars

Ratings and Reviews 1 Rating

Total 4 out of 5 stars
ease 4 out of 5 stars
features 4 out of 5 stars
design 5 out of 5 stars
support 4 out of 5 stars

Ratings and Reviews 0 Ratings

Total 0 out of 5 stars
ease 0 out of 5 stars
features 0 out of 5 stars
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What is IBM Streams?

IBM Streams processes a wide range of streaming information, encompassing unstructured text, video, audio, geospatial data, and sensor inputs, which allows organizations to discover opportunities and reduce risks while making prompt decisions. Utilizing IBM® Streams, users can convert swiftly evolving data into valuable insights. This platform assesses different types of streaming data, equipping organizations to detect trends and threats as they emerge. When combined with the other features of IBM Cloud Pak® for Data, which is built on a versatile and open framework, it boosts collaboration among data scientists in crafting models suitable for stream flows. Additionally, it enables the real-time evaluation of extensive datasets, making it easier than ever to extract actionable value from your data. These capabilities empower organizations to fully leverage their data streams, leading to enhanced outcomes and strategic advantages in their operations. As a result, organizations can optimize their decision-making processes and drive innovation across various sectors.

What is Hitachi Streaming Data Platform?

The Hitachi Streaming Data Platform (SDP) is specifically designed for the real-time processing of large volumes of time-series data as it is generated. By leveraging in-memory and incremental computation methods, SDP facilitates swift analyses that avoid the usual lags associated with traditional data processing approaches. Users can define summary analysis scenarios using Continuous Query Language (CQL), which has a syntax similar to SQL, thereby allowing flexible and programmable data analysis without the need for custom-built applications. The platform's architecture comprises various elements such as development servers, data-transfer servers, data-analysis servers, and dashboard servers, which collectively form a scalable and efficient ecosystem for data processing. Moreover, SDP’s modular architecture supports numerous data input and output formats, including text files and HTTP packets, and integrates effortlessly with visualization tools like RTView for real-time performance tracking. This thoughtful design guarantees that users can proficiently manage and analyze their data streams as they happen, ultimately empowering them with actionable insights. As a result, organizations can respond more quickly to changing data conditions, significantly enhancing their operational agility.

Media

Media

Integrations Supported

Integrations Supported

Virtana Platform

API Availability

API Availability

Has API

Pricing Information

Pricing not provided
Free Trial Offered?

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

Not specified

Customer Service / Support

Web-Based Support

Customer Service / Support

Standard Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

IBM

Date Founded

1911

Company Location

United States

Company Website

www.ibm.com/cloud/streaming-analytics

Company Facts

Organization Name

Hitachi

Company Location

Japan

Company Website

www.hitachi.com

Categories and Features

Data Science

Not specified

Streaming Analytics

Data Enrichment
Data Wrangling / Data Prep
Multiple Data Source Support
Process Automation
Real-time Analysis / Reporting
Visualization Dashboards

Categories and Features

Streaming Analytics

Data Enrichment
Data Wrangling / Data Prep
Multiple Data Source Support
Process Automation
Real-time Analysis / Reporting
Visualization Dashboards

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