Businesses are storing more types of data in greater volumes
than ever before. Building on traditional data storage and management
technologies, big data systems have emerged as vital enterprise assets.
Here, we look at key big data building blocks.
Relational Database Management Systems (RDBMS)
An RDBMS is a database management system (DBMS) based on the
relational model. In an RDBMS, data is stored in tables; relationships
among data are also stored in tables. This allows stored data to be
accessed or reassembled in different ways without having the change data
tables. Users access and manipulate data in RDBMS using query languages
such Structured Query Language (SQL). Most popular databases currently
in use are based on the relational database model.
Network Attached Storage (NAS)
Network attached storage systems are networked appliances that
contain one or more hard drives that can be shared with multiple,
heterogeneous computers. Their specialized role within networks is to
store and serve files. NAS disk drives typically support built in data
protection mechanisms including redundant storage containers or
redundant arrays of independent disks (RAID). NAS enables file serving
responsibilities to be separated from other servers on the network and
typically provides faster data access than traditional file servers.
Storage Area Networks
A SAN is a dedicated network that provides access to various
types of storage devices including tape libraries, optical jukeboxes,
and disk arrays. To servers and other devices in the network, a SAN's
storage devices look like locally attached devices. Because it is
specifically designed for storage communications, Fibre Channel is often
used for SAN interconnections. SANs can be centralized or distributed
within enterprise networks depending on required computing requirements.
Data Warehouse (DW)
A DW is a database used for reporting and analysis. The data in a
DW is uploaded from other operational systems. Metadata, data about the
data, is also stored in the DW. Data warehouses can be subdivided into
data marts that store subsets of data from the DW. A data mart is
similar to a partition in a traditional database. DW data is cleaned,
transformed, catalogued and made available for use by managers and other
business professionals for decision support, market research, data
mining, online analytical processing (OLAP), and other forms of business
intelligence (BI).
Business Intelligence (BI)
BI technologies provide current, predictive, and historical
views of business operations. Because BI aims to improve business
decision making, BI systems are often classified as decision support
systems (DSS). Technologies often identified as BI technologies include
benchmarking, business analytics, business performance management, data
mining, event processing, predictive analytics, and text mining.
Conclusion
Big data systems rely on a range of existing data storage and
management technologies that are already well established as part of
enterprise information systems.
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