DEFINITION: Sqoop is a tool that efficiently transfers bulk data between the relational database to Hadoop and Hadoop to relational databases using a parallel and a distributed tool called mapper.
Sqoop – “SQL to Hadoop and Hadoop to SQL”
It is an ecosystem of Hadoop.
What is ecosystem?
Hadoop ecosystem is a framework that solves big data problems. It comprises many services (ingesting, storing, analyzing and maintaining). The following are the Hadoop components:
- HDFS -> Hadoop Distributed File System
- YARN -> Yet Another Resource Negotiator
- Map Reduce -> Data processing using programming
- Spark -> In-memory Data Processing
- PIG, HIVE-> Data Processing Services using Query (SQL-like)
- HBase -> NoSQL Database
- Mahout, Spark MLlib -> Machine Learning
- Apache Drill -> SQL on Hadoop
- Zookeeper -> Managing Cluster
- Oozie -> Job Scheduling
- Flume, Sqoop -> Data Ingesting Services
- Solr & Lucene -> Searching & Indexing
- Ambari -> Provision, Monitor and Maintain cluster
Let’s discuss them later. Now just have in mind that the above are the Hadoop ecosystem components and SQOOP is one among them.
Big Data development starts when the data is available in their platforms like hdfs, hive or hbase. In the initial stages, all the data were stored in relational database servers. Before Sqoop, developers used to write code to transfer data between rdbms and hadoop. Sqoop filled the gap to achieve this transfer easily.
In Sqoop, developers just need to mention the source, destination and the rest of the work will be done by the Sqoop tool.
Why data transfer?
Data warehouse consolidation: In an organization n no of databases and warehouses are used. So, all the databases are diverged since the data gets bigger daily. The organization wants to maintain a single enterprise database but that’s not possible since the size is larger and a costly operation too. Sqoop transfers the data from the traditional system to Hadoop platform in an efficient manner on daily/scheduled/on demand basis. So, from different data sources we can bring the data and place it in a single platform and do the manipulations.
Data warehouse migration: The cost involved in maintaining or fetching the data from these warehouses is higher when compared with the big data platform. E.g.: If it involves 40$ in maintaining Teradata, it costs only 1$ in big data platform.
Backup and Availability: The data in the data warehouse platform might not be stored for a long period of time because it is costly, and it degrades the performance too. So, we should backup the data regularly either in tape or in big data. But the availability in big data is high than the tape drive.