Search results
Results from the WOW.Com Content Network
In May 2011, the list of supported file systems bundled with Apache Hadoop were: HDFS: Hadoop's own rack-aware file system. [47] This is designed to scale to tens of petabytes of storage and runs on top of the file systems of the underlying operating systems.
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.
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Gluster, Lustre and old (1.6.x) version of MooseFS, although this document is from 2013 and a lot of information are outdated (e.g. MooseFS had no HA for Metadata Server at that time).
Hadoop Distributed File System is a distributed file system that handles large data sets running on commodity hardware (Ishengoma, 2013). It is used to scale a single Apache Hadoop cluster to hundreds (and even thousands) of nodes. HDFS is one of the major components of Apache Hadoop, the others being MapReduce and YARN.
Shared-disk file systems (also called shared-storage file systems, SAN file system, Clustered file system or even cluster file systems) are primarily used in a storage area network where all nodes directly access the block storage where the file system is located. This makes it possible for nodes to fail without affecting access to the file ...
Apache Hive supports the analysis of large datasets stored in Hadoop's HDFS and compatible file systems such as Amazon S3 filesystem and Alluxio.It provides a SQL-like query language called HiveQL [9] with schema on read and transparently converts queries to MapReduce, Apache Tez [10] and Spark jobs.
The MapR File System (MapR FS) is a clustered file system that supports both very large-scale and high-performance uses. [1] MapR FS supports a variety of interfaces including conventional read/write file access via NFS and a FUSE interface, as well as via the HDFS interface used by many systems such as Apache Hadoop and Apache Spark.
Drill is primarily focused on non-relational datastores, including Apache Hadoop text files, NoSQL, and cloud storage. A notable feature also includes in situ querying of local JSON and Apache Parquet files. Some additional datastores that it supports include: All Hadoop distributions (HDFS API 2.3+), including Apache Hadoop, MapR, CDH and ...