The HDF-EOS Aura Data Guidelines - "What's New"
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The document discusses the Aura Guidelines, a common data format developed and adopted by four instruments on the Aura satellite. The Guidelines were developed using HDF5/HDF-EOS5 to store each instrument's data in a single format. New features include guidelines for zonal mean and gridded products, following similar naming conventions and metadata definitions as level 2 swath products. The Aura Guidelines have been rewritten and submitted as a technical note to NASA's Standards Process Group.
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HDF-EOS is a standard developed by NASA for storing Earth science data in the Hierarchical Data Format (HDF). It defines structures for gridded data, swaths, points, and other arrays. The HDF-EOS APIs provide functions for reading, writing, and manipulating these data structures and attributes in both HDF4 and HDF5 formats. Tools like HDFView and the HDF-EOS to GeoTIFF Converter (HEG) allow users to browse, subset, and convert HDF-EOS data to other formats.
HDF-EOS 2/5 to netCDF Converter
The tool takes HDF-EOS 5 data as input, and generates COARDS-compatible output - if the input file has enough metadata to be COARDS-compliant, the output file will be COARDS-compliant. The tool is written in portable C, and ought to run on any platform where the HDF-EOS and netCDF libraries are available.
This year, we have made two major enhancements to the converter:
It now automatically detects whether its input is HDF-EOS2 or HDF-EOS5 format, and handles either one. The previous tool worked with HDF-EOS5 only.
Its netCDF output attempts to conform to the new CF conventions (a superset of the COARDS conventions). This is primarily an improvement in its translation of Swath datasets, which CF handles much better than COARDS.
HDF-EOS Aura File Format Guidelines
This document presents guidelines for formatting HDF-EOS files for NASA's Aura satellite mission, which is scheduled to launch in June 2003. It summarizes the Aura instruments, discusses changes from previous guidelines, and outlines the standardized structure for HDF-EOS files, including dimensions, geolocation fields, data fields, and attributes. The guidelines are intended to promote cross-platform use and understanding of Aura data files.
HDF-EOS Aura File Format Guidelines
The document presents guidelines for standardizing the HDF-EOS file format for the Aura satellite mission. It proposes organizing data fields and attributes in a consistent way, using standard dimension and field names, and reporting data on a common pressure grid. This will make files easier to understand, cross-platform compatible, and simplify software development. The guidelines cover topics such as data ordering, units, and attribute names. Standardizing these aspects aims to reduce mismatches between instruments. [/SUMMARY]
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.
Archive Information Packages for NASA HDF-EOS Data
This presentation discusses the development of Archival Information Packages (AIPs) for NASA HDF-EOS data. It outlines the components of an AIP according to the OAIS reference model. It then describes efforts to implement AIPs using standards like METS, PREMIS, ISO 19115 and HDF-5 to package HDF data files with associated metadata. The goals were to prototype AIPs at the data set and granule level and test the usability of digital library standards for geospatial data. Next steps involve further work applying these standards to create preservation-ready packages of NASA's HDF data holdings.
HDF Update
Mike Folk from the National Center for Supercomputing Applications gave an update on HDF software in 2003. HDF is supported by several government agencies for applications in earth science, simulations, and data-intensive computing. Version 4.2 Release 1 was planned for October 2003 with bug fixes and new features. HDF5 1.6.0 was released in July 2003 with new filters, properties, and performance improvements. Work was also being done on high-level APIs, parallel HDF5, tools, and collaborations with other projects.
HDF Update
The document provides an update on the HDF software projects. It discusses recent releases of HDF4, HDF5, and HDF Java products. It highlights new features, platforms supported, and organizations contributing to development. Upcoming work includes improvements to parallel I/O, data indexing and viewing tools, and harmonization with netCDF and OPeNDAP formats.
HDF-EOS Development: Current Status and Tools
This document provides a summary of the current status and schedule of HDF-EOS development. It describes what HDF-EOS is, its data structures like Grid, Swath, Point, and Zonal Average, and applications that can be used to access and process HDF-EOS data. HDF-EOS 5, based on HDF5, is now used by EOS Aura producers and users, while HDF-EOS 2 will continue to be supported.
HDF Update
This document summarizes Mike Folk's presentation at the Science Data Processing Workshop from February 26-28, 2002. The presentation provided updates on HDF4 and HDF5, including recent releases and future plans. HDF4 and HDF5 are open source data formats and software libraries for scientific data that support efficient storage of arrays, images, and tables. The presentation outlined ongoing work to improve performance, add new features, and facilitate the transition from HDF4 to HDF5.
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.
An IDL-Based Validation Toolkit: Extensions to use the HDF-EOS Swath Format
An IDL-Based Validation Toolkit: Extensions to use the HDF-EOS Swath FormatThe HDF-EOS Tools and Information Center
This document discusses extensions made to an IDL-based validation toolkit to allow it to work with HDF-EOS swath format files. The extensions addressed problems with the toolkit's previous native format by making it faster and easier to work with satellite data files. While the HDF-EOS format presented some challenges, it provides a standard way of storing atmospheric profile data. Lessons learned include developing generic swath reading tools and working to easily select variables from swaths. Future work may include expanding the data mapping capabilities and developing interfaces for other HDF formats.SEEDS Standards Process
The SEEDS (Strategic Evolution of ESE Data Systems) process is a strategy for maximizing the utility of Earth science data within NASA. It involves formulating best practices for data systems through a study of past and present systems. The SEEDS process engages data providers and users to incorporate lessons learned. It focuses on adopting common standards to increase flexibility rather than developing new systems. The document outlines the status of the SEEDS formulation studies, including the standards process study which recommends adopting the IETF process for approving and developing interoperability standards. It notes that standards working groups will be established to evaluate existing standards like HDF for adoption.
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.
Introduction to HDF5
This tutorial is designed for new HDF5 users. We will go over a brief history of HDF and HDF5 software, and will cover basic HDF5 Data Model objects and their properties; we will give an overview of the HDF5 Libraries and APIs, and discuss the HDF5 programming model. Simple C and Fortran examples, and Java tool HDFView will be used to illustrate HDF5 concepts.
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.
Transitioning from HDF4 to HDF5
The document discusses the transition from HDF4 to HDF5. There are three main challenges: 1) supporting both HDF4 and HDF5, 2) enabling interoperability between HDF4 and HDF5 files and libraries, and 3) converting data from HDF4 to HDF5. NCSA has developed tools like h4toh5 and libh4toh5 to help with the conversion process. Experiments show that default conversion of HDF4 files to HDF5 is feasible and conversion of HDF-EOS files can be achieved through custom tools built with libh4toh5.
Welcome to HDF Workshop V
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
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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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The best part is you can achieve this without building a custom workflow! Say goodbye to the hassle of using separate automations to call APIs. By seamlessly integrating within App Studio, you can now easily streamline your workflow, while gaining direct access to our Connector Catalog of popular applications.
We’ll discuss and demo the benefits of UiPath Apps and connectors including:
Creating a compelling user experience for any software, without the limitations of APIs.
Accelerating the app creation process, saving time and effort
Enjoying high-performance CRUD (create, read, update, delete) operations, for
seamless data management.
Speakers:
Russell Alfeche, Technology Leader, RPA at qBotic and UiPath MVP
Charlie Greenberg, host
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Crafting Excellence: A Comprehensive Guide to iOS Mobile App Development Serv...
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Discover top-tier mobile app development services, offering innovative solutions for iOS and Android. Enhance your business with custom, user-friendly mobile applications.Taking AI to the Next Level in Manufacturing.pdf
Read Taking AI to the Next Level in Manufacturing to gain insights on AI adoption in the manufacturing industry, such as:
1. How quickly AI is being implemented in manufacturing.
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Monitoring and Managing Anomaly Detection on OpenShift
Overview
Dive into the world of anomaly detection on edge devices with our comprehensive hands-on tutorial. This SlideShare presentation will guide you through the entire process, from data collection and model training to edge deployment and real-time monitoring. Perfect for those looking to implement robust anomaly detection systems on resource-constrained IoT/edge devices.
Key Topics Covered
1. Introduction to Anomaly Detection
- Understand the fundamentals of anomaly detection and its importance in identifying unusual behavior or failures in systems.
2. Understanding Edge (IoT)
- Learn about edge computing and IoT, and how they enable real-time data processing and decision-making at the source.
3. What is ArgoCD?
- Discover ArgoCD, a declarative, GitOps continuous delivery tool for Kubernetes, and its role in deploying applications on edge devices.
4. Deployment Using ArgoCD for Edge Devices
- Step-by-step guide on deploying anomaly detection models on edge devices using ArgoCD.
5. Introduction to Apache Kafka and S3
- Explore Apache Kafka for real-time data streaming and Amazon S3 for scalable storage solutions.
6. Viewing Kafka Messages in the Data Lake
- Learn how to view and analyze Kafka messages stored in a data lake for better insights.
7. What is Prometheus?
- Get to know Prometheus, an open-source monitoring and alerting toolkit, and its application in monitoring edge devices.
8. Monitoring Application Metrics with Prometheus
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9. What is Camel K?
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10. Configuring Camel K Integrations for Data Pipelines
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11. What is a Jupyter Notebook?
- Overview of Jupyter Notebooks, an open-source web application for creating and sharing documents with live code, equations, visualizations, and narrative text.
12. Jupyter Notebooks with Code Examples
- Hands-on examples and code snippets in Jupyter Notebooks to help you implement and test anomaly detection models.
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