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Trending October 2023 # Learn The Different Tools Of Hadoop With Their Features # Suggested November 2023 # Top 10 Popular

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Introduction to Hadoop Tools

Hadoop, Data Science, Statistics & others

Features of Hadoop Tools







Now we will see the features with a brief explanation.

1. Hive

The Apache Hive was founded by Facebook and later donated to Apache Foundation, which is a data warehouse infrastructure, it facilitates writing SQL like Query called HQL or HiveQL. These queries are internally converted to Map Reduce jobs and processing is done utilizing Hadoop’s distributed computing. It can process the data which resides in HDFS, S3 and all the storage compatible with Hadoop. We can leverage the facilities provided by Map Reduce whenever we find something difficult to implement in Hive by implementing in User Defined Functions. It enables the user to register UDF’s and use it in the jobs.

Features of Hive

Hive can process many types of file formats such as Sequence File, ORC File, TextFile, etc.

Partitioning, Bucketing, and Indexing are available for faster execution.

Compressed Data can also be loaded into a hive table.

Managed or Internal tables and external tables are the prominent features of Hive.

2. Pig

Yahoo developed the Apache Pig to have an additional tool to strengthen Hadoop by having an ad-hoc way of implementing Map Reduce. Pig is having an engine called Pig Engine which converts scripts to Map Reduce. Pig is a scripting language, the scripts written for Pig are in PigLatin, just like Hive here also we can have UDF’s to enhance the functionality. Tasks in Pig are optimized automatically so programmers need not worry about it. Pig Handles both structured as well as unstructured data.

Features of Pig

Users can have their own functions to do a particular type of data processing.

It is easy to write codes in Pig comparatively also the length of the code is less.

The system can automatically optimize execution.

3. Sqoop

Features of Sqoop

Sqoop can import all tables at once into HDFS.

We can embed SQL queries as well as conditions on the import of data.

We can import data to hive if a table is present from HDFS.

The number of mappers can be controlled, i.e. parallel execution can be controlled by specifying the number of mappers.

4. HBase

The database management system on top of HDFS is called HBase. HBase is a NoSQL database, that is developed on top of HDFS. HBase is not a relational database; it does not support structured query languages. HBase utilizes distributed processing of HDFS. It can have large tables with millions and millions of records.

Features of HBase

HBase provides scalability in both linear as well as modular.

API’s in JAVA can be used for client access.

HBase provides a shell for executing queries.

5. Zookeeper

Features of Zookeeper

Performance can be increased by distributing the tasks which are achieved by adding more machines.

It hides the complexity of the distribution and portrays itself as a single machine.

Failure of a few systems does not impact the entire system, but the drawback is it may lead to partial data loss.

It provides Atomicity, i.e. transaction is either successful or failed but not in an imperfect state.

6. Flume

Apache Flume is a tool that provides data ingestion, which can collect, aggregate and transport a huge amount of data from different sources to an HDFS, HBase, etc. Flume is very reliable and can be configured. It was designed to ingest streaming data from the webserver or event data to HDFS, e.g. it can ingest twitter data to HDFS. Flume can store data to any of the centralized data stores such as HBase/HDFS. If there is a situation where the data produce is at a higher rate compared to that of the speed of the data can be written then flume acts as a mediator and ensures data flows steadily.

Features of Flume

It can ingest web servers data along with the event data such as data from social media.

Flume transactions are channel-based, i.e. two messages are maintained; one is for sending, and one is for receiving.

Horizontal scaling is possible in a flume.

It is highly faulted tolerant as contextual routing is present in a flume.


Here in this article, we have learned about a few of the Hadoop tools and how they are useful in the world of data. We have seen Hive and Pig, which is used to query and analyze data, snoop to move data and flume to ingest streaming data to HDFS.

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