Hadoop Database and the Components Related to It
A database management system or a DBMS allows in the processing of data in a refined manner. Different databases work in different environment that are used for facilitating varied processes. While some allow in the simplified transfer of data, others work well in processing them in clustered systems. This article will throw light on the Hadoop database that is most recommended while running applications in clustered systems and its components.
What is Hadoop?
Formally known as apache hadoop, it is an open source distributed processing framework. It allows in the management of data processed as well as allows storing of applications utilizing big file data, running them in clustered systems. Apache develop this technology as a part of their open source project from where it derives its name.
The process and working of Hadoop
As mentioned, this technology runs on clusters found in community servers, thereby making it possible for storing any amount of big application data in the system. The database aids in storing huge files through the process of scaling up itself with a view of supporting thousands of hardware nodes. This thereby assists in storing a massive amount of information or data. Two of the reasons why it is the most preferred open source database for managing cluster systems are as follows:
Limitation of Hadoop and ways to overcome
Though the particular database has earned popularity amongst developers and programmers across the globe. It helps in storing big data and it has great ability to manage and process in a clustered system. It does have few limitations, which are as follows:
These drawbacks are limited with the help of HBase and hive data warehouses that are built over the database for offering data query and analysis.
HBase architecture and its advantages
An HBase helps in overcoming the drawbacks of HDFS. The hbase architecture is a NoSQL interface that allows in random writing and reading of the system fastidiously. A column-oriented data warehouse is built over the top of the database process thus limiting the drawbacks. Some of the advantages of HBase are as follows:
Handling of varied databases
This type of architecture is used in the handling a myriad of data. Year after years there is an increase in the growth of data. This hinders the relational databases in handling a variety of databases. An HBase offers scalability as well as partitioning thereby allowing efficient storage and revival of data.
Access to random data
HBase is an important component of the open-source database system that leverages with the HDFS’s fault tolerance feature. The data model aids in providing random access to any volume of data, whether structured or unstructured. Furthermore, it offers real-time read and write access to the data.
Hadoop Hive and its advantages
Similar to HBase, hadoop hive is a data warehouse that is run on SQL interface. The primary function of this data warehouse is offering data query and analysis that is stored in different databases and file systems for integrating with the main database. The major components of the hive are as follows:
The HBase or hive data warehouse processes helps the HDFS in processing data in clusters without hassle. It allows in processing data faster and efficiently than in conventional architecture, which relies on computation and data distribution through high-speed networking.
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