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SigLens icon

SigLens

SigLens is a cutting-edge, purpose-built solution designed to efficiently manage massive volumes of data ranging from terabytes to petabytes, all while optimizing resource utilization. Functioning as a column-oriented database, SigLens incorporates dynamic columnar compressions...

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Cost / License

  • Free
  • Open Source

Application type

Platforms

  • Self-Hosted
  • Docker
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Properties

  1.  Lightweight

Features

  1.  Website Monitoring
  2.  Logging

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SigLens information

  • Developed by

    SigLens
  • Licensing

    Open Source (AGPL-3.0) and Free product.
  • Written in

  • Alternatives

    31 alternatives listed
  • Supported Languages

    • English

AlternativeTo Category

Network & Admin

GitHub repository

  •  1,656 Stars
  •  269 Forks
  •  100 Open Issues
  •   Updated  
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SigLens was added to AlternativeTo by Navya Srivastav on and this page was last updated .
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What is SigLens?

SigLens is a cutting-edge, purpose-built solution designed to efficiently manage massive volumes of data ranging from terabytes to petabytes, all while optimizing resource utilization. Functioning as a column-oriented database, SigLens incorporates dynamic columnar compressions to achieve an impressive 90% compression across diverse data types, spanning from low to high cardinality datasets.

One of SigLens' groundbreaking features is its micro indexing technology, a revolutionary advancement that significantly reduces the index size to just 1/100th of conventional indexes while maintaining equivalent functionality. These microindices exhibit dynamic adaptability, seamlessly adjusting to the evolving structure of the data.

Introducing another innovative element, SigLens introduces AgileAggsTrees, a unique approach designed to accelerate aggregation queries with unparalleled speed. By combining these state-of-the-art technologies, SigLens promises an unparalleled data management experience.

We cordially invite you to experience the power and efficiency of SigLens firsthand. Try out SigLens today and unlock a new realm of possibilities for handling and analyzing extensive datasets.

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