Hadoop is an ecosystem of open source components with Java-based programming framework that supports the processing and storage of extremely large data sets in a distributed computing environment. Hadoop can fundamentally change the way enterprises store, process, and analyze data. Unlike traditional storage file systems, Hadoop enables multiple types of analytic workloads to run on the same data, at the same time, at massive scale on industry-standard components.
- RESTful API
- Intuitive GUI and CLI
- High Availability
- Hitless Upgrade
- Standard Enterprise Administrative Tools & Security
- Diagnostic Support
- Call Home Support
- Cloud-Based Services
What is the Hadoop File System
Hadoop was designed for the impending avalanche of big data. Information is generated exponentially on a daily basis, and is very difficult to access and manage. Hadoop has moved beyond its beginnings in web indexing as a simple write-once storage infrastructure.
Hadoop is used in industries globally for a variety storage at scale levels for both structured and unstructured data that include vast volume, variety, and velocity of data. Hadoop is now widely used across industries, including finance, media and entertainment, government, healthcare, information services, retail, and other industries with big data requirements but the limitations of the original storage infrastructure remain.
Hadoop succeeds with its’ ability to store, manage and analyze vast amounts of structured and unstructured data quickly, reliably, flexibly and at low-cost.
- Scalability and Performance – distributed processing of data local to each node in a cluster enables Hadoop to store, manage, process and analyze data at petabyte scale.
- Reliability – large computing clusters are prone to failure of individual nodes in the cluster. Hadoop is fundamentally resilient – when a node fails processing is re-directed to the remaining nodes in the cluster and data is automatically re-replicated in preparation for future node failures.
- Flexibility – unlike traditional relational database management systems, you don’t have to created structured schemas before storing data. You can store data in any format, including semi-structured or unstructured formats, and then parse and apply schema to the data when read.
- Low Cost – unlike proprietary software, Hadoop is open source and uses commodity components
Hadoop services provide for data storage, data processing, data access, data governance, security, and operations.
|The Hadoop Distributed File System (HDFS) provides scalable, fault-tolerant, cost-efficient storage for your big data. It was designed to span large clusters of commodity servers scaling up to hundreds of petabytes and thousands of servers.||HDFS can take advantage of the locality of data, processing it near the place it is stored on each node in the cluster in order to reduce the distance over which it must be transmitted.||Applications interact with the data in Hadoop using batch or interactive SQL or low-latency access with NoSQL, thus allowing business users and data analysts to use their preferred business analytics, reporting and visualization tools with Hadoop.||The Hadoop ecosystem extends data access and processing with powerful tools for data governance and integration including centralized security administration and data classification tagging, which combined enable dynamic data access policies that proactively prevent data access violations from occurring.|