Flume and Sqoop: Loading large amounts of data into Hadoop

Flume and Sqoop: Loading large amounts of data into Hadoop

When it comes to analyzing Big Data, Apache Hadoop is the single most-preferred solution. Right from its scalability attributes to cost-effectiveness; hadoop is much more flexible as compared to other platforms. However, loading large amounts of data into hadoop from various sources comes with its own share of difficulties.

The traditional techniques of using a variety of scripts for loading data in hadoop is not very feasible as the amount of data is usually in PB (petabytes) and EB (exabytes). So, for the purpose of loading such huge amounts of data in hadoop, many different types of tools have been created. Flume and Sqoop are two such tools.

What is Flume?

Apache Flume is developed for loading large amounts of streaming data into the HDFS environment. For instance, collecting log from the web servers and combining it for the purpose of data analysis can be done with the help of Flume. Apart from its simple architecture, it also has a variety of failover and recovery mechanisms. Moreover, you can also fine-tune its reliability mechanism.

How does Apache Flume work?

Flume has a pretty straightforward architecture based on 3 important events-

  1. Source- This is where the data comes from, like a file or message queue
  2. Sink- This is the destination of all the data that is collected from various sources.
  3. Channel- These are the pipes that function as a platform between the origin (source) and destination (sink).

In Apache Flume, the master functions as the sole configuration authority which is used by all the nodes for the purpose of retrieving configuration. A node can be defined as an event in Flume, which reads from the source location and writes the same on the destination. The role of this node depends on the characteristics of the source and destination locations.

Features

  • Flume is a highly flexible data feeding tool as it can be used in an environment with five to thousands of machines.
  • It offers low latency and very high throughput.
  • It is stream oriented, fault tolerant and easily scalable.

What is Sqoop?

Apache Sqoop (‘SQ’L-Had’oop’) is a perfect tool for people who want to move large amounts of data into Hadoop for analyzing it. T is be used to transfer data from Oracle, MySQL, etc. into HDFS or Hive. Moreover, it can also be used to transfer data from HDFS to RDBMS. It s based on ‘Command Line Interpreter’ technique in which, the commands are executed one-by-one through the interpreter.

How does Apache Sqoop work?

Sqoop is highly recommended for non-programmers and works by looking at the source and database as well as automatically chooses the best import function. Once sqoop recognizes the input, it reads the metadata for the same and creates a class definition required by the input.

You can also force it function as per your requirements by simply adjusting the columns that you need. In such a scenario, sqoop will not import the entire input and then search for the data in it. It uses a MapReduce function that runs in the background and imports the database.

Features-

  • Sqoop allows you to import both, individual tables as well as the entire database. The files will go into the HDFS file system and data will land in the inbuilt directories.
  • It parallelizes the entire data transfer operation to utilize the system at its best and improve its speed.
  • With the help of sqoop, excess data can be mitigated to peripheral systems.
  • With the help of Java classes, it provides you with programmatic data interaction.

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Author

Prabhat Jain spends most of his time doing research to analyze unstructured data with Hadoop framework. He also focuses on the emerging tools and technologies that deals with big data. He like coding & blogging, also loves to eat junk food and listen EDM songs.

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