Big Data Sqoop

Sqoop (SQL-to-Hadoop) is one of the most popular Big Data tools that leverages the competency to haul out data from a non-Hadoop data store by transforming information into a form that can be easily accessed and used by Big Data Hadoop, to then upload it into HDFS. This process is most commonly known as ETL, for Extract, Transform, and Load.

While loading information into Big Data Hadoop is crucial for using MapReduce, it is equally critical to withdraw it from Big Data Hadoop, into an external data source for better usage in other types of applications.

While loading information into Big Data Hadoop is crucial for using MapReduce, it is equally critical to withdraw it from Big Data Hadoop, into an external data source for better usage in other types of applications.

Key features of Big Data Sqoop

• Bulk import: Big Data Sqoop facilitates the import of singular tables and comprehensive databases into HDFS. The information is saved in the native directories and files in the HDFS file system
• Direct input: Big Data Sqoop can also enable import and map SQL (relational) databases into Hive and HBase
• Data interaction: Big Data Sqoop is capable of generating Java classes so that you can interact with the data in the scope of programming
• Data export: Big Data Sqoop can also export information from HDFS into a relational database with the help of a target table definition based on the specifics of the target database

Functionality of Sqoop

Sqoop is one of the best Big Data platforms mostly owing to its superior functionalities. It functions by analyzing the database you want to import and by picking an apt import function required for the source data. After it identifies the input commands, it checks the metadata for the table (or database) and creates a class definition of the concerned requirements of the import.

On the other hand, Sqoop can also be very selective so that it aids you with just the columns you would like to look at before the process of inputting rather than going through the trouble of doing the entire input and then identifying information. This saves time to a great extent. The actual import from the external database to HDFS is performed by a MapReduce job created behind the scenes by Sqoop.

Sqoop is easy enough to be an efficient Big Data tool for amateur programmers too. While it maybe, it is to be kept in mind that it has a high degree of dependence on underlying technologies like HDFS and MapReduce.

Benefits

Ease of Use – Sqoop lets connectors to be configured in one place, which can be managed by the admin role and run by the operator role. This centralized architecture helps in better deployment of Big Data analytics and solutions.

Ease of Extension – The connectors of Sqoop are not restricted to just the JDBC model. It has the competencies to extend and define its own vocabulary without having the need to mention a table name.

Security – The fact that Sqoop operates as server based application that secures access to external systems and does not allow code generation, makes its security to go by.