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Tags: Data pipelines and messaging
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1–24 of 25 · Most stars

GoDS (Go Data Structures) - Sets, Lists, Stacks, Maps, Trees, Queues, and much more

  • go
  • golang
  • data-structure
  • map

Cleanlab's open-source library is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

🔩 Like builtins, but boltons. 250+ constructs, recipes, and snippets which extend (and rely on nothing but) the Python standard library. Nothing like Michael Bolton.

The AsyncAPI specification allows you to create machine-readable definitions of your asynchronous APIs.

Deequ is a library built on top of Apache Spark for defining "unit tests for data", which measure data quality in large datasets.

Meltano: the declarative code-first data integration engine that powers your wildest data and ML-powered product ideas. Say goodbye to writing, maintaining, and scaling your own API integrations.

  • dataops
  • dataops-platform
  • elt
  • open-source

:surfer: Traceable, extendable and minimalist **event bus** implementation for Elixir with built-in **event store** and **event watcher** based on ETS.

Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.

Download, unpack from a ZIP/TAR/GZ/BZ2 archive, parse, correct, convert units and import Google Spreadsheets, XLS, ODS, XML, CSV, HTML, etc. into your ActiveRecord models. Uses RemoteTable gem internally.

Redis is an in-memory database that persists on disk. The data model is key-value, but many different kind of values are supported: Strings, Lists, Sets, Sorted Sets, Hashes, Streams, HyperLogLogs, Bitmaps.

Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.

hypermatch is a high-performance ⚡ Go library designed for rapid matching of a large number of rules to events. It processes thousands of events per second 🚀 against extensive rule sets in-memory with minimal latency ⏱️.

  • go
  • golang
  • matching
  • matching-engine