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About Warp 10

Warp 10 is a Time Series data management platform from SenX that simplifies managing and processing Time Series data. It includes a Geo Time Series database, a companion analytics engine, and an API for data ingestion so users can efficiently handle large amounts of temporal and spatial data. The platform supports functionalities such as real-time data processing, querying capabilities, and data visualization. Warp 10 is designed to accommodate various industries, allowing users to use their Time Series data without the complexity of managing underlying infrastructure. Key capabilities: Time Series database Analytics engine API for ingestion Real-time processing Data visualization Best for: data analysts and engineers that need to manage and analyze Time Series and spatial data.

Warp 10 Details

Vendor
SenX
Year Launched
2013
Location
SenX HQ 55, Rue Charles Nungesser, Guipavas, Brittany 29490, FR
Deployment
cloud, on premise, windows, linux
Training Options
documentation, videos, live online, in person
Countries Served
All Countries
Languages
English, French
Users
Data Analysts, Data Scientists, Machine Learning Engineers
Industries Served
Healthcare, Education, Finance, Retail, Manufacturing, Telecommunications, Transportation, Energy, Government, Marketing
Tags
time series, big data, IoT, sensor data, warp10, Industry 4.0, opensource, digital transformation, and Data Analytics

Warp 10's In-App Market Place

Does Warp 10 have an in-app market place?

Yes

How many Mini-Apps in the marketplace?

0

Mini Apps

Pricing Options

Free trial
Free version
Request a quote
Promo Offer

Accepted Payment Currencies

USD ($), EUR (€), GBP (£), JPY (¥), CAD ($), AUD ($), CHF (CHF), CNY (¥), INR (₹), RUB (₽)

Pros & Cons

  • Scalability: Supports deployments from edge devices to massive distributed clusters handling petabytes of data.
  • Flexibility in Deployment: Offers Standalone, Standalone+, and Distributed versions to fit various needs and budgets.
  • Open Source: The core platform is free to use, offering transparency and community-driven development.
  • Real-time and Historical Data: Handles both real-time ingestion and efficient long-term storage of historical data.
  • Complexity for Distributed Deployments: The Distributed version, while highly scalable, adds architectural complexity that requires significant expertise to manage.
  • Pricing for Commercial Support/Features: Enterprise-level support and custom services are not publicly priced, requiring direct engagement with the vendor.
  • Niche Focus: Primarily focused on time series data, so it might not be the right fit for general-purpose database needs.

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