Difference Between flume and sqoop

Both Flume and Sqoop are meant for data movement.

Sqoop and Flume both are meant to fulfill data ingestion needs but they serve different purposes. Apache Flume works well for streaming data sources that are generated continuously in Hadoop environment such as log files from multiple servers whereas whereas Apache Sqoop works well with any RDBMS has JDBC connectivity.

Sqoop is actually meant for bulk data transfers between Hadoop and any other structured data stores. Flume collects log data from many sources, aggregating it, and writing it to HDFS.

Flume:

Flume is a framework for populating Hadoop with data. Agents are populated throughout ones IT infrastructure – inside web servers, application servers and mobile devices, for example – to collect data and integrate it into Hadoop.

Flume helps to collect data from a variety of sources, like logs, jms, Directory etc. Multiple flume agents can be configured to collect high volume of data. It scales horizontally.

Flume is a better choice when moving bulk streaming data from various sources like JMS or Spooling directory whereas Sqoop is an ideal fit if the data is sitting in databases like Teradata, Oracle, MySQL Server, Postgres or any other JDBC compatible database then it is best to use Apache Sqoop.

Sqoop:

Sqoop is a connectivity tool for moving data from non-Hadoop data stores – such as relational databases and data warehouses – into Hadoop. It allows users to specify the target location inside of Hadoop and instruct Sqoop to move data from Oracle,Teradata or other relational databases to the target.

Sqoop helps to move data between Hadoop and other databases and it can transfer data in parallel for performance.

Apache Sqoop provides direct input i.e. it can map relational databases and import directly into HBase and Hive.

Sqoop helps in mitigating the excessive loads to external systems.