BrightFonts provides large-scale data processing and storage solutions in an economical way for various sectors. We provide technical support during the setup and management of private and public clouds based on open-source distributed frameworks including Hadoop and Hive.

BrightFonts Hadoop Offering

  • Analytics Roadmap: Get clarity on your analytic options with an Analytics Roadmap.
  • Data Architecture Planning: Plan for modern and enduring data architecture.
  • ETL & Database Engineering: We'll help you transform your data into an essential asset.
  • Hadoop Engineering: Engineered big data solutions for analysis.
  • Business Intelligence Development: We'll help you build an analytics capability with maximum benefit, and minimal risk.
  • Data Science Statistical Services: We've got the techniques and tools in place to show you how to get it done.
  • Data Science & BI Integration: Put your findings in play with our data science integration knowledge.
  • Data Science Exploration Services: Sometimes you need to do a little exploration to see where the opportunities are.
  • Embedded Analytics Development: Need analytics in your app, but don't want to build it from scratch? We can help.



Reach our Global Offices


Corporate Office:

BrightFonts, Inc
545 East John Carpenter Freeway, Suite 300
Las Colinas, Texas 75062

Write to us:


Contact us on:

Phone: (469) 463-6560
Fax: 1-469-718-0465



Get In Touch

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Latest News

  • 9th Jan 2015

    Latest News

    BrightFonts participated in an international symposium on Big Data and Predictive Computational Modeling

    Current computational models are capable of generating petabyte-sized databases providing insight into processes taking place at extremely fine spatio-temporal scales. Apart from the obvious difficulties in visualizing, storing and processing such data, the fundamental challenge lies in extracting knowledge about the salient features and understanding of the driving mechanisms.

    This is more pronounced in the context of multiscale simulations which can only be enabled by the ability to identify accurate but computationally-tractable reduced descriptions even in cases where physical insight is unavailable. Learning from data and identifying structural characteristics is a typical task in several computational statistics and machine learning applications.

    Read More



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