Survey of Data Format Tools
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The National Polar-orbiting Operational Environmental Satellite System (NPOESS) will deliver data to four U.S. Government processing centers in Hierarchical Data Format version 5 (HDF5). The National Oceanic and Atmospheric Administration (NOAA) will tailor the NPOESS data by subsetting and translating the HDF5 dataset into data formats such as BUFR, GeoTIFF, NetCDF and SDTS to meet the diverse requirements of its customers. We will present a survey and feature comparison of software tools currently available to evaluate, translate and extract data from datasets in HDF and other standard data formats. Our presentation will also identify opportunities to extend the functionality of existing toolsets to better support the NPOESS HDF5 dataset.
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This document summarizes the fifth annual HDF workshop sponsored by ESDIS and NCSA. It provides an overview of the status of ESDIS, HDF/HDF-EOS, and plans for the future. Over 750 terabytes of Terra and Landsat 7 data have been processed and made available. Some instruments like ASTER and CERES now have validated data while others like MODIS are still being reprocessed. Future plans include installing data pools at DAACs and procuring an EMD contract to support ongoing EOS operations. The community advisory process involves groups like UWGs, DAWG, and SWGD to provide feedback. HDF is a file format for scientific data while HDF-EOS is the
UML Representation of NPOESS Data Products in HDF5
The document discusses the representation of data products from the National Polar-orbiting Operational Environmental Satellite System (NPOESS) in HDF5 format. NPOESS collects global satellite sensor data and creates data products that are packaged into HDF5 files for distribution. The data products contain various types of records, including raw, calibrated, and processed environmental data. Metadata is included in the HDF5 files to describe the data. Granules of data are initially produced and can be aggregated into larger files. Unified Modeling Language (UML) diagrams are used to represent the structure and relationships within the HDF5 files to help users understand and work with the NPOESS data.
HDF Update
An update on HDF, including a status report on the HDF Group, an overview of recent changes to the HDF4 and HDF5 libraries and tools, plans for future releases, HDF Group projects and collaborations, and future plans.
Introduction to HDF5
This tutorial is designed for new HDF5 users. We will cover HDF5 abstractions such as datasets, groups, attributes, and datatypes. Simple C examples will cover the programming model and basic features of the API, and will give new users the knowledge they need to navigate through the rich collection of HDF5 interfaces. Participants will be guided through an interactive demonstration of the fundamentals of HDF5.
This tutorial is for new HDF5 users.
HDF4 Mapping Project Update
The document discusses the HDF4 Mapping Project which aims to ensure long-term access to Earth Observing System (EOS) data stored in HDF4 files. It provides an overview of the project scope, including developing a proof of concept prototype and production quality mapping tools. It also describes verification studies conducted with NASA data centers to identify requirements for verifying correctness of HDF4 file content maps produced by the mapping tools. The project aims to generate content maps for HDF4 files containing valuable EOS data before the HDF4 library and tools are no longer maintained.
ESDIS Status (2002)
The document provides an overview and status update of the Earth Science Data and Information System (ESDIS). ESDIS has successfully supported numerous Earth science satellite missions and currently manages over 2 petabytes of science data. In fiscal year 2002, ESDIS delivered over 16 million data products to more than 1.8 million users. ESDIS is working to enhance its capabilities through initiatives like Data Pools and the EOS ClearingHOuse (ECHO) metadata broker. HDF-EOS 5 development is nearly complete and the workshop will discuss next steps for HDF-EOS tools and community adoption of HDF-EOS 5.
LCI2009-Tutorial
This document provides an overview of a full-day tutorial on HDF5 and netCDF-4. The tutorial will cover the HDF5 and netCDF-4 data models, programming interfaces, and optimization features. It will demonstrate how to use HDF5 for efficient data storage and I/O, including techniques like chunking, compression and parallel I/O. The tutorial will also show how netCDF-4 implements the netCDF data model using the HDF5 file format to provide scalable I/O. The goal is to equip participants with the skills needed to effectively use HDF5 and netCDF-4 on high-performance computing clusters.
LCI2009-Tutorial
This document provides an overview of a full-day tutorial on HDF5 and netCDF-4. The tutorial will cover the HDF5 and netCDF-4 data models, programming interfaces, and optimization features. It will demonstrate how to use HDF5 for efficient data storage and I/O, including techniques like chunking, compression and parallel I/O. It will also show how netCDF-4 implements the netCDF data model using the HDF5 file format to provide scalable I/O. The tutorial is intended to help scientists and developers effectively use and optimize HDF5 and netCDF-4 for data management on high-performance computing systems.
Content Framework for Operational Environmental Remote Sensing Data Sets: NPO...
Content Framework for Operational Environmental Remote Sensing Data Sets: NPO...The HDF-EOS Tools and Information Center
This poster (or slides) describes the main concepts which relate data format (HDF5), metadata, and data organization, as they are being applied to NPOESS. It includes the motivation for selecting HDF5; the motivation and general implementation of FGDC base metadata and remote sensing extensions; and the current attempt to use best experiences with HDF-EOS swath and netCDF Climate & Forecast conventions to guide NPOESS product definition. Profile of NPOESS HDF5 Files
The document provides an overview of National Polar-orbiting Operational Satellite System (NPOESS) HDF5 files. Key points include:
1) NPOESS is a satellite system that collects environmental data related to weather, atmosphere, oceans, land, and near-space. Data products are distributed in HDF5 format.
2) NPOESS HDF5 files contain raw data records, sensor data records, intermediate products, application related products, and environmental data records.
3) Data is organized into granules and aggregations. Granules contain a segment of data and are referenced by aggregations, which group granules over a temporal range.
HDF-EOS Subsetting: HEW and other tools
This document summarizes subsetting tools and methods for Earth science data in HDF-EOS format. It discusses the HDF-EOS Web-based Subsetter (HEW) tool, the HEW Subsetting Engine (HSE) library, other customized subsetting applications for datasets like MODIS, and plans to integrate subsetting into the EOSDIS Core System. The document provides an overview of subsetting goals, steps, currently available tools, and a demonstration of the HEW tool.
HDF-EOS Workshop IV
This document outlines the goals and status updates from Workshop III of the HDF-EOS workshop in September 2000. The goals of Workshop IV were to share progress on tools that support HDF and HDF-EOS, lessons learned from applying it to science products, and gather community feedback. It provides the status of 12 action items from Workshop III, such as the development of a metadata validator tool and recommendations for attributes in HDF-EOS files. It also discusses the transition to HDF-EOS version 3 with HDF5 and a schedule for migration and new applications.
NASA HDF/HDF-EOS Data Access Challenges
This slide summarizes the common data access challenges that data users and producers may experience in NASA HDF/HDF-EOS data.
h5web: a web-based viewer of HDF5 files
This document introduces h5web, a web-based viewer for HDF5 files developed by Loïc Huder at the European Synchrotron Radiation Facility. HDF5 has become the standard format for data acquired at ESRF. H5web allows users to browse HDF5 file hierarchies, inspect metadata and attributes, and display n-dimensional datasets with interactive visualizations. Built with React and available as open-source, h5web demonstrates browsing and visualizing capabilities through a demo. Future work includes incorporating NeXus support and deploying the visualization components in other applications.
HDF OPeNDAP update
The presentation summarized updates to HDF support for OPeNDAP data access. It discussed improvements to HDF4 and HDF5 handlers that enhance performance and support additional Earth science datasets. It demonstrated using OPeNDAP to access HDF files from satellites like TRMM and GPM, as well as a tool for converting HDF files to netCDF format. Challenges discussed included keeping up with evolving data standards and ensuring adequate performance for data aggregation services.
Geoscience Data Analysis and Visualization Tools from NCAR
The document provides an overview of geoscience data analysis and visualization tools from the National Center for Atmospheric Research (NCAR). It describes two interfaces - the NCL scripting language and the Python modules PyNGL and PyNIO - that provide capabilities for data input/output, analysis, and visualization. Key topics covered include an introduction to NCL and PyNGL/PyNIO, the data model and file format support, visualization examples, and the status and distribution of the software.
Using HDF5 Archive Information Package to preserve HDF-EOS2 data
Using HDF5 Archive Information Package to preserve HDF-EOS2 dataThe HDF-EOS Tools and Information Center
This one-year research project, funded by NOAA Climate Program Office (CPO) Scientific Data Stewardship (SDS), provides a solution to migrate data to a single standards-based archive format. Specifically, we investigate on how to store NASA ECS data and metadata into HDF5 Archival Information Packages (AIP). To achieve this, the HDF4 to HDF5 conversion tool has been enhanced so that converted ECS data can be read through the NetCDF4/CDM interface. In addition, metadata tools will be developed that convert ECS collection and granule level metadata to NOAA's collection level and NARA's METS standard. The enhanced HDF4 to HDF5 conversion tool has been released in May 2008 and it includes new functionality as the converted ECS data can be read through the NetCDF4 interface. We have tested 33 typical HDF-EOS2 swath, grid and point products at the National Snow and Ice Data Center (NSIDC). We also demonstrate the initial effort of the work to develop METS compliant metadata from granule metadata held in NASA's Earth Observing System (EOS) Data and Information System (EOSDIS) Core System (ECS). Profile of NPOESS HDF5 Files
The NPOESS program uses Unified Modeling Language (UML) to describe the format of the HDF5 files produced. For each unique type of data product, the HDF5 storage organization and the means to retrieve the data is the same. This provides a consistent data retrieval interface for manual and automated users of the data, without which would require custom development and cumbersome maintenance. The data formats are described using UML to provide a profile of HDF5 files.
Similar to Survey of Data Format Tools (20)
Archive Information Packages for NASA HDF-EOS Data
Archive Information Packages for NASA HDF-EOS Data
UML Representation of NPOESS Data Products in HDF5
UML Representation of NPOESS Data Products in HDF5
Content Framework for Operational Environmental Remote Sensing Data Sets: NPO...
Content Framework for Operational Environmental Remote Sensing Data Sets: NPO...
Geoscience Data Analysis and Visualization Tools from NCAR
Geoscience Data Analysis and Visualization Tools from NCAR
Using HDF5 Archive Information Package to preserve HDF-EOS2 data
Using HDF5 Archive Information Package to preserve HDF-EOS2 data
More from The HDF-EOS Tools and Information Center
Cloud-Optimized HDF5 Files
This document discusses how to optimize HDF5 files for efficient access in cloud object stores. Key optimizations include using large dataset chunk sizes of 1-4 MiB, consolidating internal file metadata, and minimizing variable-length datatypes. The document recommends creating files with paged aggregation and storing file content information in the user block to enable fast discovery of file contents when stored in object stores.
Accessing HDF5 data in the cloud with HSDS
This document provides an overview of HSDS (Highly Scalable Data Service), which is a REST-based service that allows accessing HDF5 data stored in the cloud. It discusses how HSDS maps HDF5 objects like datasets and groups to individual cloud storage objects to optimize performance. The document also describes how HSDS was used to improve access performance for NASA ICESat-2 HDF5 data on AWS S3 by hyper-chunking datasets into larger chunks spanning multiple original HDF5 chunks. Benchmark results showed that accessing the data through HSDS provided over 2x faster performance than other methods like ROS3 or S3FS that directly access the cloud storage.
The State of HDF
This document summarizes the current status and focus of the HDF Group. It discusses that the HDF Group is located in Champaign, IL and is a non-profit organization focused on developing and maintaining HDF software and data formats. It provides an overview of recent HDF5, HDF4 and HDFView releases and notes areas of focus for software quality improvements, increased transparency, strengthening the community, and modernizing HDF products. It invites support and participation in upcoming user group meetings.
Highly Scalable Data Service (HSDS) Performance Features
This document provides an overview of HSDS (HDF Server and Data Service), which allows HDF5 files to be stored and accessed from the cloud. Key points include:
- HSDS maps HDF5 objects like datasets and groups to individual cloud storage objects for scalability and parallelism.
- Features include streaming support, fancy indexing for complex queries, and caching for improved performance.
- HSDS can be deployed on Docker, Kubernetes, or AWS Lambda depending on needs.
- Case studies show HSDS is used by organizations like NREL and NSF to make petabytes of scientific data publicly accessible in the cloud.
Creating Cloud-Optimized HDF5 Files
This document discusses creating cloud-optimized HDF5 files by rearranging internal structures for more efficient data access in cloud object stores. It describes cloud-native and cloud-optimized storage formats, with the latter involving storing the entire HDF5 file as a single object. The benefits of cloud-optimized HDF5 include fast scanning and using the HDF5 library. Key aspects covered include using optimal chunk sizes, compression, and minimizing variable-length datatypes.
HDF5 OPeNDAP Handler Updates, and Performance Discussion
This document discusses updates and performance improvements to the HDF5 OPeNDAP data handler. It provides a history of the handler since 2001 and describes recent updates including supporting DAP4, new data types, and NetCDF data models. A performance study showed that passing compressed HDF5 data through the handler without decompressing/recompressing led to speedups of around 17-30x by leveraging HDF5 direct I/O APIs. This allows outputting HDF5 files as NetCDF files much faster through the handler.
Hyrax: Serving Data from S3
This document provides instructions for using the Hyrax software to serve scientific data files stored on Amazon S3 using the OPeNDAP data access protocol. It describes how to generate ancillary metadata files called DMR++ files using the get_dmrpp tool that provide information about the data file structure and locations. The document explains how to run get_dmrpp inside a Docker container to process data files on S3 and generate customized DMR++ files that the Hyrax server can use to serve the files to clients.
Accessing Cloud Data and Services Using EDL, Pydap, MATLAB
This document provides an overview and examples of accessing cloud data and services using the Earthdata Login (EDL), Pydap, and MATLAB. It discusses some common problems users encounter, such as being unable to access HDF5 data on AWS S3 using MATLAB or read data from OPeNDAP servers using Pydap. Solutions presented include using EDL to get temporary AWS tokens for S3 access in MATLAB and providing code examples on the HDFEOS website to help users access S3 data and OPeNDAP services. The document also notes some limitations, such as tokens being valid for only 1 hour, and workarounds like requesting new tokens or using the MATLAB HDF5 API instead of the netCDF API.
HDF - Current status and Future Directions
The HDF5 Roadmap and New Features document outlines upcoming changes and improvements to the HDF5 library. Key points include:
- HDF5 1.13.x releases will include new features like selection I/O, the Onion VFD for versioned files, improved VFD SWMR for single-writer multiple-reader access, and subfiling for parallel I/O.
- The Virtual Object Layer allows customizing HDF5 object storage and introduces terminal and pass-through connectors.
- The Onion VFD stores versions of HDF5 files in a separate onion file for versioned access.
- VFD SWMR improves on legacy SWMR by implementing single-writer multiple-reader capabilities
HDFEOS.org User Analsys, Updates, and Future
This document discusses user analysis of the HDFEOS.org website and plans for future improvements. It finds that the majority of the site's 100 daily users are "quiet", not posting on forums or other interactive elements. The main user types are locators, who search for examples or data; mergers, who combine or mosaic datasets; and converters, who change file formats. The document outlines recent updates focused on these user types, like adding Python examples for subsetting and calculating latitude and longitude. It proposes future work on artificial intelligence/machine learning uses of HDF files and examples for processing HDF data in the cloud.
HDF - Current status and Future Directions
This document summarizes a presentation about the current status and future directions of the Hierarchical Data Format (HDF) software. It provides updates on recent HDF5 releases, development efforts including new compression methods and ways to access HDF5 data, and outreach resources. It concludes by inviting the audience to share wishes for future HDF development.
H5Coro: The Cloud-Optimized Read-Only Library
The document describes H5Coro, a new C++ library for reading HDF5 files from cloud storage. H5Coro was created to optimize HDF5 reading for cloud environments by minimizing I/O operations through caching and efficient HTTP requests. Performance tests showed H5Coro was 77-132x faster than the previous HDF5 library at reading HDF5 data from Amazon S3 for NASA's SlideRule project. H5Coro supports common HDF5 elements but does not support writing or some complex HDF5 data types and messages to focus on optimized read-only performance for time series data stored sequentially in memory.
MATLAB Modernization on HDF5 1.10
This document summarizes MathWorks' work to modernize MATLAB's support for HDF5. Key points include:
1) MATLAB now supports HDF5 1.10.7 features like single-writer/multiple-reader access and virtual datasets through new and updated low-level functions.
2) Performance benchmarks show some improvements but also regressions compared to the previous HDF5 version, and work continues to optimize code and support future versions.
3) There are compatibility considerations for Linux filter plugins, but interim solutions are provided until MathWorks can ship a single HDF5 version.
HDF for the Cloud - Serverless HDF
HSDS provides HDF as a service through a REST API that can scale across nodes. New releases will enable serverless operation using AWS Lambda or direct client access without a server. This allows HDF data to be accessed remotely without managing servers. HSDS stores each HDF object separately, making it compatible with cloud object storage. Performance on AWS Lambda is slower than a dedicated server but has no management overhead. Direct client access has better performance but limits collaboration between clients.
HDF5 <-> Zarr
HDF5 and Zarr are data formats that can be used to store and access scientific data. This presentation discusses approaches to translating between the two formats. It describes how HDF5 files were translated to the Zarr format by creating a separate Zarr store to hold HDF5 file chunks, and storing chunk location metadata. It also discusses an implementation that translates Zarr data to the HDF5 format by using a special chunking layout and storing chunk information in an HDF5 compound dataset. Limitations of the translations include lack of support for some HDF5 dataset properties in Zarr, and lack of support for some Zarr compression methods in the HDF5 implementation.
HDF for the Cloud - New HDF Server Features
The document discusses HDF for the cloud, including new features of the HDF Server and what's next. Key points:
- HDF Server uses a "sharded schema" that maps HDF5 objects to individual storage objects, allowing parallel access and updates without transferring entire files.
- Implementations include HSDS software that uses the sharded schema with an API and SDKs for different languages like h5pyd for Python.
- New features of HSDS 0.6 include support for POSIX, Azure, AWS Lambda, and role-based access control.
- Future work includes direct access to storage without a server intermediary for some use cases.
Apache Drill and Unidata THREDDS Data Server for NASA HDF-EOS on S3
Apache Drill and Unidata THREDDS Data Server for NASA HDF-EOS on S3The HDF-EOS Tools and Information Center
This document compares different methods for accessing HDF and netCDF files stored on Amazon S3, including Apache Drill, THREDDS Data Server (TDS), and HDF5 Virtual File Driver (VFD). A benchmark test of accessing a 24GB HDF5/netCDF-4 file on S3 from Amazon EC2 found that TDS performed the best, responding within 2 minutes, while Apache Drill failed after 7 minutes. The document concludes that TDS 5.0 is the clear winner based on performance and support for role-based access control and HDF4 files, but the best solution depends on use case and software.STARE-PODS: A Versatile Data Store Leveraging the HDF Virtual Object Layer fo...
STARE-PODS: A Versatile Data Store Leveraging the HDF Virtual Object Layer fo...The HDF-EOS Tools and Information Center
This document discusses STARE-PODS, a proposal to NASA/ACCESS-19 to develop a scalable data store for earth science data using the SpatioTemporal Adaptive Resolution Encoding (STARE) indexing scheme. STARE allows diverse earth science data to be unified and indexed, enabling the data to be partitioned and stored in a Parallel Optimized Data Store (PODS) for efficient analysis. The HDF Virtual Object Layer and Virtual Data Set technologies can then provide interfaces to access the data in STARE-PODS in a familiar way. The goal is for STARE-PODS to organize diverse data for alignment and parallel/distributed storage and processing to enable integrative analysis at scale.HDF5 and Ecosystem: What Is New?
This document provides an overview and update on HDF5 and its ecosystem. Key points include:
- HDF5 1.12.0 was recently released with new features like the Virtual Object Layer and external references.
- The HDF5 library now supports accessing data in the cloud using connectors like S3 VFD and REST VOL without needing to modify applications.
- Projects like HDFql and H5CPP provide additional interfaces for querying and working with HDF5 files from languages like SQL, C++, and Python.
- The HDF5 community is moving development to GitHub and improving documentation resources on the HDF wiki site.
HDF5 Roadmap 2019-2020
This document summarizes new features in HDF5 1.12.0, including support for storing references to objects and attributes across files, new storage backends using a virtual object layer (VOL), and virtual file drivers (VFDs) for Amazon S3 and HDFS. It outlines the HDF5 roadmap for 2019-2022, which includes continued support for HDF5 1.8 and 1.10, and new features in future 1.12.x releases like querying, indexing, and provenance tracking.
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Accessing Cloud Data and Services Using EDL, Pydap, MATLAB
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Apache Drill and Unidata THREDDS Data Server for NASA HDF-EOS on S3
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You can also read: https://www.systoolsgroup.com/updates/office-365-tenant-to-tenant-migration-step-by-step-complete-guide/
Choosing The Best AWS Service For Your Website + API.pptx
Have you ever been confused by the myriad of choices offered by AWS for hosting a website or an API?
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Which one is cheapest? Which one is fastest? Which one will scale to meet our needs?
Join me in this session as we dive into each AWS hosting service to determine which one is best for your scenario and explain why!
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Survey of Data Format Tools
- 1. TITLE Survey of Data Format Tools HDF/HDF-EOS Workshop VIII 28 October 2004 Geof Goodrum National Environmental Satellite, Data and Information Service Office of Systems Development (OSD) 28-Oct-2004 HDF/HDF-EOS Workshop VIII 1
- 2. Background The National Polar-orbiting Operational Environmental Satellite System (NPOESS) will deliver data to four U.S. Government processing centers in Hierarchical Data Format 5 (HDF5). The National Oceanic and Atmospheric Administration (NOAA) will tailor the NPOESS data by translating the HDF5 dataset into data formats such as BUFR, GeoTIFF, netCDF and SDTS to meet the diverse requirements of its customers. 28-Oct-2004 HDF/HDF-EOS Workshop VIII 2
- 3. Purpose Compare available software tools to evaluate, translate and extract data from datasets in HDF and other scientific and geospatial data formats. Identify opportunities to extend the functionality of existing toolsets to better support the NPOESS HDF5 dataset. Provide a roadmap for development of a toolset that NOAA can use to meet the needs of its customers. 28-Oct-2004 HDF/HDF-EOS Workshop VIII 3
- 4. Overview • Software to read, write and modify a specific data format – HDF4; HDF5; HDF-EOS2; HDF-EOS5; CDF; netCDF; GeoTIFF; SDTS; BUFR; GRIB; GRIB2; FITS; JPEG; JPEG2000 • Software to translate one data format into another – FreeForm; GDAL; IDL; Matlab; Ferret • Software that permits applications to access data directly – OPeNDAP 28-Oct-2004 HDF/HDF-EOS Workshop VIII 4
- 5. Summary • IDL, Matlab and GDAL provide a sound foundation for translation toolset development. – IDL and Matlab applications write multiple output formats – Add HDF5 support to GDAL • Consider OPeNDAP if a client is available (or can be developed) for the customer’s application. • Study ongoing. – http://projects.osd.noaa.gov/NDE/publicdocuments.htm 28-Oct-2004 HDF/HDF-EOS Workshop VIII 5
- 6. Summary • IDL, Matlab and GDAL provide a sound foundation for translation toolset development. – IDL and Matlab applications write multiple output formats – Add HDF5 support to GDAL • Consider OPeNDAP if a client is available (or can be developed) for the customer’s application. • Study ongoing. – http://projects.osd.noaa.gov/NDE/publicdocuments.htm 28-Oct-2004 HDF/HDF-EOS Workshop VIII 5