One of my earlier post described about Hadoop and its ecosystem components. Please refer to it for basic understanding. We will be discussing in this article, the key differences between Apache Sqoop and Apache Flume.
Apache Spoop:
SQL-to-Hadoop is a Hadoop ecosystem component and an ETL tool that offers the capability to extract data from various structured data stores such as relational databases with the help of map-reduce. This command-line interpreter works efficiently to transfer a huge volume of the data to the Hadoop file system (HDFS, HBase, and Hive). Similarly, it exports the data from Hadoop to SQL.
Apache Flume:
Apache Flume is a distributed, reliable, and flexible open-source system for collecting, aggregating, and transferring huge volumes of log data effectively. Its design is simple and flexible, based on streaming data flows. Flume is a tool that allows you to reliably stream log and event data from web servers into Apache Hadoop.
The Apache Flume is not only limited to log data aggregation; data sources may be customized, allowing Flume to carry vast amounts of data such as email messages, social media-generated data, network traffic statistics, and pretty much any other data source.
Differences:
Flume’s main need is that the data be generated in a continuous and streaming manner. When data is stored in relational database systems and needs to be transferred once or several times, Sqoop is used.
Flume is used to collect data from various sources that are generating data for a specific use case and then transferring this large amount of data from distributed resources to a single centralized repository. Sqoop is designed to transfer large amounts of data between Hadoop and Relational Databases.
Sqoop’s data loading is not event-driven, but Apache Flume’s data loading is totally event-driven.
Sqoop is built on a connector-based architecture, which means connectors understand how to connect to various data sources. Flume, on the other hand, is built on an agent-based architecture, which means that the code written in it is referred to as an agent and is in charge of fetching data.
Hope you find this article helpful.
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Big Data and SQL 