Skills needed to become a Hadoop Developer

access_time 2019-12-01T12:41:44.091Z face Hadoop

Any Hadoop Developer requires the following primary and essential skills as a beginner –
Storage and Processing are the two fundamental units of Hadoop. The designing of Hadoop was done to manage a massive chunk of Big Data. Until and unless, as a developer, has hands-on experience with storage components that are used in Hadoop, you won’t be able to handle the data. Following are the basic Hadoop Storage units which are needed by a beginner

HDFS Hadoop Distributed File System

Using this file system, you can easily store a large amount of data over various nodes in the cluster of Hadoop.


Hadoop developers run it on top of HDFS (Hadoop Distributed File System). BigTable like capabilities are provided by it to Hadoop. The design of this system is done in such a manner that it offers a fault-tolerant way of collecting an extensive collection of sparse data sets.

Spark SQL

When it comes to Data Querying, this is lightning fast. It associates relational Processing with the functional programming of spark. Support for various data sources is offered by it, thus, making it easy to weave SQL queries that have code transformations.

Data Ingestion Tools

Data can come from any source, and Hadoop is one such framework that can deal with every kind of data. If you know data ingestion tools, it will only be useful at the time when you are managing data from various sources. Following are popular data ingestion tools –

  • Flume: Hadoop Developers use Apache Flume to gather, total, and ship a lot of gushing information, for example, log documents, occasions from different sources like system traffic, online life, email messages, and so forth to HDFS. Flume is exceptionally dependable and disseminated.
  • Sqoop: Apache Sqoop is used by Hadoop Developers to move information between HDFS (Hadoop stockpiling) and social database servers like MySQL, Oracle RDB, SQLite, Teradata, Netezza, Postgres and some more.
  • Kafka: Hadoop Developers use Apache Kafka as an ongoing circulated distribute buy-in informing framework. It was initially created at LinkedIn and later on turned into a piece of the Apache venture. Kafka is quick, spry, versatile, and disseminated by the plan. It has the accompanying segments -
  1. Mapreduce: Amateurs in Hadoop Development, use MapReduce as a programming structure to perform appropriated and parallel handling on vast informational collections in a disseminated situation.
  2. Big Data Training: MapReduce has two sub-partitioned undertakings. A Mapper assignment and Reducer Task. The yield of a Mapper or guide work (key-esteem sets) is a contribution to the Reducer.
  • Apache Spark: Engineers utilize spark cluster computing framework for the Processing in real-time. With a sizeable open-source network, is the dynamic Apache venture. It gives an interface for programming groups with verifiable information parallelism and adaptation to internal failure.

Also Read: Hadoop MapReduce Interview Questions

The Top ETL tools are as follows:

  • Pig: Developers analyze large data sets that represented themselves as data flows. It is created to render an abstraction over MapReduce, thereby, decreasing the complexities of coding/writing a MapReduce program.
  • Hive: Utilized by developers for Data Querying and Data Analytics, Apache Hive is a data warehousing project which is built on top of Hadoop. The Data Infrastructure Team created this on Facebook.
  • GraphX: The API of Apache Spark is GraphX, which is used by developers for the computation of graphs and graph-parallel.
  • Mahout: Mahout for ML is used by Hadoop Developers. It empowers machines to learn without being excessively customized. It produces versatile AI calculations, separates suggestions from informational indexes in an improved manner.
  • Spark ML-Lib: Spark MLlib was created and designed by the Apache foundation and is mainly used to execute machine learning over Apache Spark. It has popular algorithms and utilities.
  • Oozie: Apache Oozie is a scheduler framework to oversee and execute Hadoop employments in a disseminated situation. Designers make the ideal pipeline by joining another sort of assignment. It very well may be your Hive, Pig, Sqoop, or MapReduce task. Utilizing Apache Oozie, you can likewise plan your employments. Inside an arrangement of the undertaking, at least two employments can also be customized to run parallel to one another. It is an adaptable, reliable, and extensible framework.
  • Ambari: Apache Ambari is a project of the Apache programming establishment. Hadoop Developers use Ambari for framework directors to the arrangement, oversee and screen a Hadoop bunch, and to incorporate Hadoop with the predominant.

Also Read: Unboxing Hadoop Developer – Roadmap and reasons for its popularity