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There are several open source projects that provide similar data version control capabilities to DVC, [52] such as: Git LFS, Dolt, Nessie, and lakeFS. These projects vary in their fit to the different needs of data engineers and data scientists such as: scalability, supported file formats, support in tabular data and unstructured data, volume ...
List of GitHub repositories of the project: Red Hat Government This data is not pre-processed List of GitHub repositories of the project: Red Hat Consulting This data is not pre-processed List of GitHub repositories of the project: Red Hat Communities of Practice This data is not pre-processed List of GitHub repositories of the project
Liang Wang held a tutorial at the CUFP 2017 to demonstrate data science in OCaml. [7] In 2018, Prof. Richard Mortier gave a talk about Owl in the Alan Turing Institute . [ 8 ] To further promote OCaml and functional programming in data science, Owl provides abundant learning materials in the form of a details manual.
KNIME (/ n aɪ m / ⓘ), the Konstanz Information Miner, [2] is a free and open-source data analytics, reporting and integration platform.KNIME integrates various components for machine learning and data mining through its modular data pipelining "Building Blocks of Analytics" concept.
The set of images in the Fashion MNIST database was created in 2017 to pose a more challenging classification task than the simple MNIST digits data, which saw performance reaching upwards of 99.7%. [1] The GitHub repository has collected over 4000 stars and is referred to more than 400 repositories, 1000 commits and 7000 code snippets. [5]
There is also an active R community around the tidyverse. For example, there is the TidyTuesday social data project organised by the Data Science Learning Community (DSLC), [16] where varied real-world datasets are released each week for the community to participate, share, practice, and make learning to work with data easier. [17]
Since October 2024, Voreen is developed in an open repository on GitHub. Although it is intended and mostly used for medical applications, [2] any other kind of volume data can be handled, e.g., microscopy, flow data or other simulations. [3] [4]
Fluentd was positioned for "big data," semi- or un-structured data sets.It analyzes event logs, application logs, and clickstreams. [3] According to Suonsyrjä and Mikkonen, the "core idea of Fluentd is to be the unifying layer between different types of log inputs and outputs.", [4] Fluentd is available on Linux, macOS, and Windows.