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What Is Hadoop?

Hadoop is a collection of open-source software from Apache that allows a network of computers to solve problems that require massive amounts of data and computational power. The framework’s massive storage capabilities allow it to store data across commodity hardware as needed and can handle a nearly limitless number of concurrent tasks efficiently. 

Is Hadoop a Database?

Hadoop is not a database. Hadoop is an open-source framework designed to handle large volumes of data.

 

Hadoop vs. Databases: What’s the Difference?

Hadoop is designed for big data analysis and can manage concurrent tasks that involve large amounts of structured and semi-structured data. Most structured data, however, can be entered, stored, queried and analyzed in a straightforward manner, even when working with massive amounts of it, meaning that traditional databases are often an organization’s best choice for these needs.

Hadoop’s real power comes in helping add structure to unstructured data for analysis. Data that comes from complex sources, such as email, texts, videos, photos, audio files, documents and social media sources are particularly valuable. Unstructured data makes up a vast majority of the world’s data and contains many valuable insights, but traditional relational databases are unable to analyze it. Through the use of massive computational power, Hadoop can efficiently join, aggregate and analyze massive amounts of unstructured data for further analysis.

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Is Coding Required for Hadoop?

Hadoop is a Java-encoded, open-source framework, but working with it requires very little knowledge of programming languages.

Hadoop’s software is built through the use of Java encoding. The framework requires very little knowledge of Java in order to operate it, however. Instead, Hadoop uses two tools for data management, Pig and Hive, in conjunction with SQL

Hive was created by Meta and allows users to read, write and manage petabytes worth of data by using SQL rather than writing MapReduce jobs in Java. Pig is a big data platform for analyzing these large data sets through parallel execution in order to complete interrelated data transformation tasks that are encoded as easily usable data flow sequences. Pig tasks are completed through the use of Hadoop’s proprietary textual language known as Pig Latin.

An area where programming knowledge is useful is in creating MapReduce jobs. MapReduce is a common software framework used often used by hardware engineers in Hadoop that streamlines the creation of applications that process vast amounts of data in parallel on large clusters of hardware in a fault-tolerant manner. A MapReduce job will typically split an input data set into independent chunks to be processed by map tasks in an independent manner. The framework then sorts the outputs of these maps to be input into reduce tasks.

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Is Hadoop Hard to Learn?

Hadoop may be difficult for those new to data analysis, but the learning curve is greatly reduced for those with knowledge of SQL.

Hadoop knowledge is amongst the most in-demand skill sets for data professionals, which makes it a natural starting point for those interested in launching a career working with big data. Hadoop previously required users to be able to write MapReduce jobs in Java, but Meta soon created Hive to allow those with knowledge of SQL to query data in Hadoop. Pig, and its textual language Pig Latin, is also similar to SQL and allows users to analyze data within the framework. 

Due to the inherent similarities, Pig and Hive are not difficult to learn for those with some background in SQL. This makes the Hadoop framework easy to get started with, and the platform provides plenty of room for users to become familiar with its capabilities over time while still completing tasks. A worthwhile approach for many hoping to increase their Hadoop capabilities is to take a specialized course dedicated to teaching specific Pig, Hive and SQL skills.

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