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SQream Technologies

United States
New York
2010
Private
$10-100m
51 - 200
Open website

SQream provides an analytics platform that minimizes Total Time to Insight (TTTI) for time-sensitive data, on-prem and on-the-cloud. Designed for tera-to-peta-scale data, the GPU-powered platform enables enterprises to rapidly ingest and analyze their growing data – providing full-picture visibility for improved customer experience, operational efficiency, and previously unobtainable business insights.

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PubMatic, Cellcom, HP ArcSight, AIS

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[Vendor_Name] is active in the finance and insurance, healthcare and hospitals, pharmaceuticals, retail, and telecommunications industries.
Industries
Finance & Insurance
Healthcare & Hospitals
Pharmaceuticals
Retail
Telecommunications
SQream Technologies’s Technology Stack maps SQream Technologies’s participation in the IoT technology stack.
  • Application Layer
  • Functional Applications
  • Cloud Layer
  • Platform as a Service
    Infrastructure as a Service
  • Edge Layer
  • Automation & Control
    Processors & Edge Intelligence
    Actuators
    Sensors
  • Devices Layer
  • Robots
    Drones
    Wearables
  • Supporting Technologies
  • Analytics & Modeling
    Application Infrastructure & Middleware
    Cybersecurity & Privacy
    Networks & Connectivity
Technological Capability
None
Minor
Moderate
Strong
Number of Podcasts1
EP 156 - How do you manage petaflops of IoT data - Matan Libis, VP of Product, SQream
Monday, Dec 19, 2022

In this episode, we interviewed Matan Libis, VP of Product at SQream. SQream provides an analytics platform that minimizes Total Time to Insight (TTTI) for time-sensitive data, on-prem and on-the-cloud.

In this talk, we discussed the value of modern database architecture for extracting insights from petaflops of data. We also explored the merger of traditional data warehouses with data lakes into lakehouses wherein large volumes of data are queried without duplicating to a warehouse.

Key Questions:

  • What are the unique architectural requirements for managing peta-scale data sets?  
  • How do data management requirement differ for the manufacturing, telco, and banking, industries?
  • What is the difference between a data lake, a warehouse, and a lakehouse?
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