Data Science and Hadoop: A Practical Guide

To tackle the difficulties that arise when processing significant amounts of data across distributed computing environments, the open-source Apache Hadoop framework was developed. This software system is remarkably adaptable and can be scaled from a single server to a network of up to ten thousand computers. Hadoop presents a budget-friendly and effective solution for organisations seeking to effectively analyse, process, and store vast datasets.

Although Hadoop is primarily written in Java, it is also compatible with other programming languages that data scientists frequently use, such as Python, C++, Ruby, Perl, and others.

The Origins of Hadoop

Originally developed as an open-source initiative by Yahoo in 2008, Hadoop has since grown into a thriving community of professional software developers and contributors. Today, it is managed by the Apache Software Foundation (ASF), a non-profit organisation that safeguards the platform’s open-source status, ensuring it remains accessible to all who wish to use it.

Doug Cutting and Mike Cafarella originally created Nutch, an open-source search engine, as a means of utilising distributed data processing and multitasking to accelerate online search results. Their objective was to leverage the power of multiple computers to enable multitasking and thereby enhance search capabilities. Hadoop is based on their work developing Nutch.

How can Hadoop be utilised in the field of data science?

Scalability for Big Data

With the amount and variety of data requiring secure storage, maintenance, analysis, and interpretation growing constantly, database technology is becoming more prevalent in online services and traditional industries, including finance, banking, and transportation. Data science is the field dedicated to the investigation of this data in search of insightful conclusions.

Compared to other solutions, Hadoop’s ecosystem is widely recognised for its reliability and scalability, making it an attractive option for businesses managing vast amounts of data. As the volume of data being stored grows exponentially, conventional database management systems are struggling to keep up with the immense quantity of information being processed.

Hadoop provides an exceedingly scalable and fault-tolerant solution enabling the storage of immense quantities of data while maintaining high standards of data quality. Moreover, it was developed with the goal of allowing for effortless vertical or horizontal expansion, providing users with exceptional flexibility.

Vertical Expansion Capabilities

To enhance the performance and lifespan of a Hadoop system, it may be necessary to scale up by adding more resources, such as CPUs, to a single node. This vertical scaling method allows the system to take advantage of the added hardware capabilities, such as enhanced processing power and expanded memory, to enhance overall system performance.

Comprehensive Scalability

When a distributed software system boosts its capacity by adding more nodes or systems, this process is referred to as horizontal scaling. Unlike vertical scaling, which can cause system overload, horizontal scaling guarantees optimal system performance as no processing time is wasted waiting for idle processing. Moreover, horizontal scaling enables the linking of multiple computers to run in parallel, resulting in a more efficient method of increasing capacity.

Compute Capabilities

Thanks to its distributed compute capabilities, Hadoop is a distributed computing system capable of filtering and processing vast amounts of data. Its processing efficiency is significantly increased when multiple nodes are utilised, making it possible to analyse a larger volume of data in less time.

Fault Tolerance

Hadoop, by default, generates several replicas of each database, ensuring that if a node fails, the task can be distributed to other network nodes. Additionally, distributed computing profits from this protection.


Hadoop can receive data without any user pre-processing necessities. It is capable of sorting different types of information, including unstructured data like text, images and videos, and deciding on the next appropriate steps with the data.

Reduced Expenses

Data is saved on the cloud, a network of servers and storage devices that is cost-effective and accessible to everyone. One of the benefits of cloud computing is that it is constructed on open source technology, which implies that it is free to use and access.

Hadoop Use Cases for Businesses

  • eBay

    utilises Hadoop to extract value from data and enhance search result indexing.
  • Facebook

    employs Hadoop to backup internal logs and dimension data sources for use in analytics, reporting, and machine learning.
  • LinkedIn’s

    “People You May Know” feature is supported by Hadoop as the backend.
  • Opower

    uses Hadoop to support customers with discounts, services recommendations, and energy consumption reduction.
  • Orbitz

    analyses guests’ browsing behaviours using Hadoop to boost a hotel’s position in search outcomes.
  • Hadoop has the capability to gather, analyse, and report data, similar to the way Spotify uses it to play music.
  • Twitter’s

    Hadoop is employed to store and interpret data such as tweets and log files.

Data scientists consider Hadoop a vital tool.

Storing massive data, which can be a mix of structured and unstructured information, is the primary role of Hadoop.

Consolidating and analysing large data sets can be easier for data scientists with the aid of Hadoop. Hadoop is an effective software that enables scientists to manage immense data with ease. Furthermore, being adept in Hadoop may provide data scientists with an advantage in their career progression.

Here are some additional purposes:

Efficient data exploration

Research indicates that data preparation takes up around 80% of a data scientist’s time and attention. Utilising Hadoop’s effective data exploration abilities can help data scientists detect nuances in the data that may have gone unnoticed without such assistance.

In addition to data storage, Hadoop is proficient in managing immense data sets with minimal analysis.

Elimination of Redundant Data

Data scientists usually do not use entire data sets when creating machine learning models or classifiers, except in rare cases. Instead, they often need to filter the data to meet the needs of marketing and research.

To recognise and examine significant patterns, data scientists will often need to scrutinise entire data records. This method of data filtering can also help locate any inconsistencies or gaps in the data. By having excellent expertise in Hadoop, data scientists can streamline the data filtering process and deal with domain-specific issues more quickly.

Participation in a Data Set

As same-type records are usually stored together and follow a predetermined structure, constructing a model with only the first thousand records in a database is not feasible. Data scientists must attain a profound knowledge of the data attributes. Sampling can make this process significantly more effective, and the user-friendly Hadoop interface enhances the speed of the sampling process.

Data analysts can determine the most effective methods for data analysis and modelling by using data sampling. Employing Hadoop Pig’s ‘Sample’ function is a great way to reduce the amount of data that needs to be collected.

The Hadoop architecture relies heavily on the Hadoop Distributed File System (HDFS) to store data in Hadoop clusters and achieve high-speed data access. Composed of NameNode and DataNode, HDFS allows big data analytics programs to be securely run on massive data sets. Additionally, HDFS provides a safe, secure data storage environment.

Overview of Hadoop’s Building Blocks

As unstructured data continues to proliferate, it is estimated that between eighty to ninety percent of data is now in this form. Hadoop is instrumental in guiding big data tasks to appropriate systems and reorganising data management. Its systematic design enables businesses to efficiently handle and analyse vast amounts of data, while being both adaptable and affordable.

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