Evidence tier Source Confirmed · 10 captures on record
What the publisher says
As described on Microsoft Marketplace.
Vaex is an open-source Python library designed for high-performance data processing, visualization, and analysis of large tabular datasets. It uses lazy evaluation, memory mapping, and out-of-core computation to efficiently process billions of rows without loading entire datasets into memory, making it ideal for big data analytics, data science, and machine learning workflows.
Key Features of Vaex:
Show the rest of the publisher’s description (16 more lines)
- High-performance processing of large tabular datasets with minimal memory usage.
- Lazy evaluation for fast and efficient data transformations.
- Out-of-core computation to handle datasets larger than available system memory.
- Fast filtering, grouping, aggregation, and statistical analysis.
- Memory-mapped file support for efficient access to large datasets.
- Supports popular file formats including HDF5, CSV, Apache Arrow, and Parquet.
- Seamless integration with NumPy, Pandas, Apache Arrow, and Jupyter Notebook.
- Built-in visualization tools for interactive exploration of large datasets.
- Machine learning utilities and scalable data preprocessing capabilities.
- Optimized for data analytics, scientific computing, and big data applications.
# Display the installed Vaex package version
$ docker run --rm vaex:latest python -c "import vaex; print(vaex.__version__['vaex'])"
# Display all installed Vaex component versions
$ docker run --rm vaex:latest python -c "import vaex; print(vaex.__version__)"
Disclaimer:
Vaex is an open-source Python library for efficient processing and analysis of large datasets, developed and maintained by the Vaex open-source community and its contributors. This content is provided for informational purposes only. We are not affiliated with, endorsed by, or sponsored by the Vaex project. All trademarks, product names, and logos are the property of their respective owners.
Agent build and provenance
See the full provenance
The layer-by-layer build, the evidence behind each claim, the risk basis and the cross-marketplace links are open to any account. Some rows are disclosed, some the source leaves Unknown; a free account shows you which.
Compliance
- FedRAMPConfirmedNot listed90%, registry-checkedNo FedRAMP Marketplace entry matched this vendor's domain, checked 2026-08-27registry recordas observed 2026-08-27
Confirmed means matched to a public authoritative registry. Claimed means the vendor or its listing states it, not yet cross-checked. A framework not shown was not found in any source we hold, which is not evidence against it. Not listed means a scoped registry check found no match for this vendor's domain: a No is a scoped registry check, not a compliance judgment. Confidence bands: 95% domain-verified, 90% registry-checked, 80% self-attested, 70% weak signal. Self-attested items marked “vendor's site” are gathered from the vendor's own website and are not verified by us.
Vendor
External enrichment · as of 2026-08-29
Sources
Publisher resources
1 linkLinked repositories
Unknown means this listing does not publish a repository. It is not a statement that the code is closed, and a linked repository is not a claim that the publisher wrote it: the registry computes that relationship privately and does not publish it.
Evidence risk is the share of the build you cannot see before you deploy, not a security rating. Sign in to see the layer-by-layer basis for this band.