Sqoop vs Flume

Differences Between Sqoop and Flume

Sqoop is a  product from Apache software. Sqoop extracts useful information from Hadoop and then passes to the outside data stores. With the help of Sqoop, we can import data from an RDBMS or mainframe into HDFS. Flume is also from Apache software. It collects and moves the recursive data that are generated. The  Apache Flume is not only restricted to log data aggregation but data sources are customizable and thus Flume can be used to transport massive quantities of data. The best way of collecting, aggregating, and moving large amounts of data between the Hadoop Distributed File System and RDBMS is via using tools such as Sqoop or Flume.

Let’s discuss these two commonly used tools for the above-mentioned purpose.

What is Sqoop

To use Sqoop, a user has to specify the tool user want to use and the arguments that control the particular tool. You can also then export the data back into an RDBMS using Sqoop. The export functionality of Sqoop is used to extract useful information from Hadoop and export them to the outside structured data stores. It works with different databases like Teradata, MySQL, Oracle, HSQLDB.

• Sqoop Architecture: –

Architecture of Sqoop

The connector in a Sqoop is a plugin for a particular Database source, so it is fundamental that it is a piece of Sqoop establishment. Despite the fact that drivers are database-specific pieces and distributed by various database vendors, Sqoop itself comes bundled with different types of connectors utilized for prevalent database and information warehousing system. Thus Sqoop ships with a mixed variety of connectors out of the box as well. Sqoop gives a pluggable component for an ideal network and external system. The Sqoop API gives a helpful structure for assembling new connectors and therefore any database connectors can be dropped into Sqoop installation to give connectivity to different data systems.

What is Flume

The  Apache Flume is not only restricted to log data aggregation but data sources are customizable and thus Flume can be used to transport massive quantities of data including but not limited to email messages, social-media-generated data, network traffic data and pretty much any data source possible.

Flume architecture: –Flume architecture is based on many-core concepts:

1. Flume Event- it is represented as the unit of data flowing, which has a byte payload and set of strings with optional string headers. Flume considers an event just a generic blob of bytes.
2. Flume Agent- It is a JVM process that hosts the components such as channels, sink, and sources. It has the potential to receive, store and forward the events from an external source to the next level.
3. Flume Flow- it is the point of time the event is being generated.
4. Flume Client- it refers to the interface where the client operates at the origin point of the event and delivers it to the Flume agent.
5. Source- A source is one that consumes events having a specific format and delivers it via a specific mechanism.
6. Channel- It is a passive store where events are held until the sink removes it for further transport.
7. Sink – It removes the event from a channel and put it on an external repository like HDFS. It currently supports creating text and sequence files and supports compression in both file types.

                                                                                 Architecture of Flume

Head to Head Comparison between Sqoop and Flume (Infographics)

Below is the top 7 comparison between Sqoop and Flume:

Key Differences between Sqoop and Flume

We now know that there are many differences between Sqoop and Flume, here are the most important differences between them given below –

1. Sqoop is designed to exchange mass information between Hadoop and Relational Database.
Whereas, Flume is used to collect data from different sources which are generating data regarding a particular use case and then transferring this large amount of data from distributed resources to a single centralized repository.

2. Sqoop also includes a set of commands which allows you to inspect the database you are working with. Thus we can consider Sqoop as a collection of related tools.
While collecting the date Flume scales the data horizontally and multiple Flume agents can be put in action to collect the date and aggregate them. Thereafter data logs are moved to a centralized data store i.e. Hadoop Distributed File System (HDFS).

3. The key factor for using Flume is that the data must be generated in a continuous and streaming fashion. Similarly, Sqoop is the best suited in situations when your data lives in database systems such as MySQL, Oracle, Teradata, PostgreSQL

Sqoop and Flume Comparison Table

Below is the comparison table between Sqoop and Flume.

Conclusion

As you learned above Sqoop vs Flume, are primarily two Data Ingestion tools used is the Big Data world.  If you need to ingest textual log data into Hadoop/HDFS then Flume is the right choice for doing that. If your data is not regularly generated then Flume will still work but it will be an overkill for that situation. Similarly, Sqoop is not the best fit for event-driven data handling.

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