Hadoop Simplified

A simple explanation of how Hadoop works


Today we live in the age of Big data, where data volumes have outgrown the storage & processing capabilities of a single machine, and the different types of data formats required to be analyzed has increased tremendously. 

This brings 2 fundamental challenges:

  • How to store and work with huge volumes & variety of data
  • How to analyze these vast data points & use it for competitive advantage.

Hadoop fills this gap by overcoming both challenges. Hadoop is based on research papers from Google & it was created by Doug Cutting, who named the framework after his son’s yellow stuffed toy elephant.

So What is Hadoop? It is a framework made up of:

  • HDFS – Hadoop distributed file system
  • Distributed computation tier using programming of MapReduce
  • Sits on the low cost commodity servers connected together called Cluster
  • Consists of a Master Node or NameNode to control the processing
  • Data Nodes to store & process the data
  • JobTracker & TaskTracker to manage & monitor the jobs

Let us see why Hadoop has become so popular today.

  • Over last decade all the data computations were done by increasing the computing power of single machine by increasing the number of processors & increasing the RAM, but they had physical limitations.
  • As the data started growing beyond these capabilities, an alternative was required to handle the the storage requirements of organizations like eBay (10 PB), Facebook (30 PB), Yahoo (170 PB), JPMC (150 PB)
  • With typical 75 MB/Sec disk data transfer rate it was impossible to process such huge data sets
  • Scalability was limited by physical size & no or limited fault tolerance
  • Additionally various formats of data are being added to the organizations for analysis which is not possible with traditional databases

How Hadoop addresses these challenges:

  • Data is split into small blocks of 64 or 128MB and stored onto a minimum of 3 machines at a time to ensure data availability & reliability
  • Many machines are connected in a cluster work in parallel for faster crunching of data
  • If any one machine fails, the work is assigned to another automatically
  • MapReduce breaks complex tasks into smaller chunks to be executed in parallel

Benefits of using Hadoop as a Big data platform are:

  • Cheap storage – commodity servers to decrease the cost per terabyte
  • Virtually unlimited scalability – new nodes can be added without any changes to existing data providing the ability to process any amount of data with no archiving necessary
  • Speed of processing – tremendous parallel processing to reduce processing time
  • Flexibility – schema less, can store any data format – structured & unstructured (audio, video, texts, csv, pdf, images, logs, clickstream data, social media)
  • Fault tolerant – any node failure is covered by another node automatically

Later multiple products & components are added to Hadoop so it is now called an eco-system, such as:

  • Hive – SQL like interface
  • Pig – data management language, like commercial tools AbInitio, Informatica,
  • Hbase – column oriented database on top of HDFS
  • Flume – real time data streaming such as credit card transaction, videos
  • Sqoop – SQL interface to RDBMS and HDFS
  • Zookeeper – a DBA management for Hadoop

And several such products are getting added all the time from various companies like Cloudera, Hortonworks, Yahoo etc.

How some of the world leaders are using Hadoop:

  • Chevron collects large amounts of seismic data to find where they can get more resources
  • JPMC uses it for storing more than 150 PB of data, over 3.5 Billion user log-ins for Fraud detection
  • eBay using it for real time analysis and search of 9 PB data with 97 million active buyers, over 200 million items on sale
  • Nokia uses it store data from phone service logs to analyze how people interact with apps and usage patterns
  • Walmart uses it to analyze customer behavior of over 200 million customer visits in a week
  • UC Irvine Health hospitals are storing 9 million patients records over 22 years to build patients surveillance algorithms

Hadoop may not replace the existing data warehouses, but it is becoming the number 1 choice for Big data platforms with a strong price/performance ratio.


Read next:

Working At The Boundaries Of Aesthetics And Inference