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Hierarchical Data Format (HDF) is a set of file formats (HDF4, HDF5) designed to store and organize large amounts of data.Originally developed at the U.S. National Center for Supercomputing Applications, it is supported by The HDF Group, a non-profit corporation whose mission is to ensure continued development of HDF5 technologies and the continued accessibility of data stored in HDF.
The Parallel-NetCDF package can read/write only classic and 64-bit offset formats. Parallel-NetCDF cannot read or write the HDF5-based format available with netCDF-4.0. The Parallel-NetCDF package uses different, but similar APIs in Fortran and C. Parallel I/O in the Unidata netCDF library has been supported since release 4.0, for HDF5 data files.
Some researchers have made a functional and experimental analysis of several distributed file systems including HDFS, Ceph, Gluster, Lustre and old (1.6.x) version of MooseFS, although this document is from 2013 and a lot of information are outdated (e.g. MooseFS had no HA for Metadata Server at that time).
Database design is the organization of data according to a database model. The designer determines what data must be stored and how the data elements interrelate. With this information, they can begin to fit the data to the database model. [1] A database management system manages the data accordingly.
Each system has at least some features of an object–relational database; they vary widely in their completeness and the approaches taken. The following tables compare general and technical information; please see the individual products' articles for further information.
State-based CRDTs (also called convergent replicated data types, or CvRDTs) are defined by two types, a type for local states and a type for actions on the state, together with three functions: A function to produce an initial state, a merge function of states, and a function to apply an action to update a state.
For example, the characterization of an architecture as "database-centric" may mean any combination of the following: using a standard, general-purpose relational database management system, as opposed to customized in-memory or file-based data structures and access methods.
Apache Parquet is a free and open-source column-oriented data storage format in the Apache Hadoop ecosystem. It is similar to RCFile and ORC, the other columnar-storage file formats in Hadoop, and is compatible with most of the data processing frameworks around Hadoop.