HDF5

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HDF5 (Virtual) Users Group Meeting – October 13-16, 2020

The HDF® Group and the Lawrence Berkeley National Lab (LBNL) hosted a virtual HDF5® User Group Meeting on October 13th to October 16th, 2020. We are working on the recordings and they will be posted soon.

HSDS release v0.6 is now available!

New features include support for running on Microsoft Azure and support for POSIX-based storage.  A complete list of new features can be found on github.

Learn more about HSDS

If you would like to talk through your use case for HSDS, please feel free to reach out to our team or post on the forum.

What is HDF5®?

Heterogeneous Data

HDF® supports n-dimensional datasets and each element in the dataset may itself be a complex object.

Easy Sharing

HDF® is portable, with no vendor lock-in, and is a self-describing file format, meaning everything all data and metadata can be passed along in one file.

Cross Platform

HDF® is a software library that runs on a range of computational platforms, from laptops to massively parallel systems, and implements a high-level API with C, C++, Fortran 90, and Java interfaces. HDF has a large ecosystem with 700+ Github projects.

Fast I/O

HDF® is high-performance I/O with a rich set of integrated performance features that allow for access time and storage space optimizations.

Big Data

There is no limit on the number or size of data objects in the collection, giving great flexibility for big data.

Keep Metadata with Data

HDF5® allows you to keep the metadata with the data, streamlining data lifecycles and pipelines.

Our Advantage
Learn about how HDF can meet your big data needs.