DOE has continued to partner with The HDF Group, supporting development of HDF5 through two generations of computing; sponsoring this development has benefited the entire HDF5 user community. Today, DOE supports current HDF5 R&D to ensure that the data challenges of third generation exascale computing ...
HDFql offers a language similar to SQL for HDF5. By providing a simpler/cleaner interface, HDFql aims to ease scientific computing and big data management....
Pearah joins The HDF Group as new Chief Executive Officer
Champaign, IL — The HDF Group today announced that its Board of Directors has appointed David Pearah as its new Chief Executive Officer. The HDF Group is a software company dedicated to creating high performance computing technology to address many of today’s Big Data challenges.
Pearah replaces Mike Folk upon his retirement after ten years as company President and Board Chair. Folk will remain a member of the Board of Directors, and Pearah will become the company’s Chairman of the Board of Directors.
Pearah said, “I am honored to have been selected as The HDF Group’s next CEO. It is a privilege to be part of an organization with a nearly 30-year history of delivering innovative technology to meet the Big Data demands of commercial industry, scientific research and governmental clients.”
Industry leaders in fields from aerospace and biomedicine to finance join the company’s client list. In addition, government entities such as the Department of Energy and NASA, numerous research facilities, and scientists in disciplines from climate study to astrophysics depend on HDF technologies.
Pearah continued, “We are an organization led by a mission to make a positive impact on everyone we engage, whether they are individuals using our open-source software, or organizations who rely on our talented team of scientists and engineers as trusted partners. I will do my best to serve the HDF community by enabling our team to fulfill their passion to make a difference. We’ve just delivered a major release of HDF5 with many additional powerful features, and we’re very excited about several innovative new products that we’ll soon be making available to our user community.”
“Dave is clearly the leader for HDF’s future, and
Organizations in both the public and private sectors use HDF to meet long term, mission-critical data management needs. For example, NASA’s Earth Observing System, the primary data repository for understanding global climate change, uses HDF. Over the lifetime of the project, which began in 1999, NASA has stored 15 petabytes of satellite data in HDF which will be accessible by NASA data centers and NASA HDF end users for many years to come.
In a previous blog, we discussed the concept of using the Hyrax OPeNDAP web server to serve NASA HDF4 and HDF5 products. Each year, The HDF Group has enhanced the HDF4 and HDF5 handlers that work within the Hyrax OPeNDAP framework to support all sorts of NASA HDF data products, making them interoperable with popular Earth Science tools such as NASA’s Panoply and UCAR’s IDV. The Hyrax HDF4 and HDF5 handlers make data products display properly using popular visualization tools.
We are excited and pleased to announce HDF5-1.10.0, the most powerful version of our flagship software ever.> This major new release of HDF5 is more powerful than ever before and packed with new capabilities that address important data challenges faced by our user community. HDF5 1.10.0 contains many important new features and changes, including those listed below. The features marked with * use new extensions to the HDF5 file format. The Single-Writer / Multiple-Reader or SWMR feature enables users to read data while concurrently writing it. * The virtual dataset (VDS) feature enables users to access data in a collection of HDF5 files as a single HDF5 dataset and to use the HDF5 APIs to work with that dataset. * (NOTE:...
The HDF Group has a long history of collaboration with Francesc Alted, creator of PyTables. Francesc was one of the first HDF5 application developers who successfully employed external compressions in an HDF5 application (PyTables). The first two compression methods that were registered with The HDF Group were LZO and BZIP2 implemented in PyTables; when Blosc was added to PyTables, it became a winner.
While HDF5 and PyTables address data organization and I/O needs for many applications, solutions like the Blosc meta-compressor presented in this blog, are simpler, achieve great I/O performance, and are alternative solutions to HDF5 in cases when portability and data organization are not critical, but compression is still desired. Enjoy the read!
Compression is a hot topic in data handling. The largest database players have recently (or not-so-recently) implemented support for different kinds of compression libraries. Why is that? It’s all about efficiency: modern CPUs are so fast in comparison with storage write speeds that compression not only offers the opportunity to store more with less space, but to improve storage bandwidth also:
The HDF5 library is an excellent example of a data container that supported out-of-the-box compression in the very first release of HDF5 in November 1998. Their innovation was to introduce support for compression of chunked datasets in a way that permitted the developer to apply compression to each of the chunks individually, resulting in reasonably fast and transparent compression using different codecs. HDF5 also introduced pluggable compression filters that allowed external developers to implement support for different codecs for HDF5. Then with release 1.8.11, they added the ability to discover, load and register filters at run time. More recently, in release 1.8.15 (and fully documented in 1.8.16), HDF5 has a new Plugin Interface that provides a complete programmatic control of dynamically loaded plugins. HDF5’s filter features now offer much-desired flexibility, giving users the freedom to choose the codec that best suits their needs.
In the last decade the trend has been to implement faster codecs at the expense of reduced compression ratios. The idea is to reduce compression/decompression time overhead
The HDF Group is collaborating with the University of California, Santa Barbara and Data Observation Network for Earth (DataONE), to help scientific research communities enhance the consistency and quality of their metadata, to foster discovery, access and understanding of data resources. As part of this collaboration, on February 9, 2016, The HDF Group’s Ted Habermann, Director of Earth Science, and Lindsay Powers, Deputy Director of Earth Science will co-lead a webinar “Sharing Data Through Guided Metadata Improvement” along with Matthew Jones, Director of Informatics Research at the National Center for Ecological Analysis and Synthesis.
Metaphorically speaking, this blog post is about a frog trying to climb out of a well, a damp and unsightly corner of the HDF5 ecosystem called HDF5.NET. People who know more about its genesis tell us that it was never intended as what it became to be perceived as, an “aspirational” .NET interface for HDF5 that would one day be complete and fully supported. Be that as it may, it’s important to ask, “What can we do today to better serve the needs of the .NET community?” We believe, as the title suggests, we need to take a step back to move forward.