Glassbeam Studio in IoT Analytics

Glassbeam Studio
Santa Clara, California, US
Below $10 MN
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Glassbeam Studio USP

Glassbeam offers a platform that helps users in building analytics from Log data in a single step. The platform makes use of Semiotic Parsing Language (SPL)which is a single-step solution to transform machine logs of any format and device into a machine data analytics app. The company offers SCALAR platform which is a fully scalable, parallel, asynchronous, machine data analytics platform that transforms any log format into actionable information. The platform adjusts itself to optimize the system while ensuring efficient resource utilisation. The company has developed Rules Engine which is a highly scalable event processor, which enables users to develop complex rules on their incoming streams or batched data. Request Glassbeam Studio Pricing to get more information.


Glassbeam Big Data Platform offers complex machine data modeling, complex event processing, security & compliance, and scalable & multi-tenancy solutions. Glassbeam's analytics platform is mainly applicable across four major areas: Explorer - It offers a powerful ‘search and explore’ capability for the user's machine log data. It has been developed on top of the company's semiotic parsing language. It also enables users to perform full text search and parametric search on hundreds of attributes parsed from the log. Log Vault - It is a centralized cloud repository for all log files, providing users with a single point of access and management for all their machine data. All the machine logs received by the platform are stored in a scalable repository in its original format. Rules and Alerts - The company's Rules Engine delivers a wide range of rules evaluation features. It allows users to perform evaluation of rules can on every incoming row of data or rules that can be triggered over a time window. Workbench - It exposes numerous metrics extracted from the user's machine log through a simple drag and drop analysis interface. It then automatically converts these metrics to measures and dimensions for detailed analysis.

360 Quadrants

Strengths and Weaknesses
  • +10 Batch Data Processing
  • +12 Directly
  • +11 Real-time Streaming Data Processing (Data-in-Motion)
  • +6 Semi-Structured Data
  • +5 Structured Data
  • +7 Unstructured Data
  • +8 Hosted / On-Cloud
  • +7 On-Premise
  • +10 Descriptive Analytics
  • +11 Diagnostics Analytics
  • +12 Predictive Analytics
  • +13 Prescriptive Analytics
  • +9 Databases
  • +8 Microsoft Corporation Office (including Spreadsheet)
  • +7 Programming Language (such as R, Python, Java)
  • +12 Remote Support
  • +11 Customer Service
  • +8 Operations/Production
  • +11 Per User Basis
  • +9 Product Development
  • -13 Through Partners / Third-Party Vendors
  • -11 On-Site Support
  • -10 Servers (such as Hadoop, Apache)
  • -11 Any other Integration type
  • -6 Finance
  • -9 Full Time Equivalent
  • -7 Human Resources (HR)
  • -5 Marketing
  • -10 Supply Chain
  • -9 Data Profiling
  • -11 Data Virtualization
  • -13 Image
  • -5 Speech/Audio
  • -14 Video
  • -13 Any Other Data Preparation Features
  • -9 Any Other Data Presentation Features
  • -7 Other Data formats
  • -7 Building and Home Automation
  • -10 Connected Logistics
  • -14 Security and Emergencies
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