What Is Hadoop Big Data: Big data is a term used to describe the large amounts of data that are now available, that can be collected, stored, and processed. This data comes from a variety of sources, including social media, smartphone applications, and smart devices like Amazon Echo or Nest.
As our world becomes increasingly connected through the internet and an ever-expanding array of smart devices, the volume, and complexity of data are increasing at an incredible rate.
The terms “big data” and “data analytics” are often used interchangeably. However, they refer to two different concepts: Data analytics refers to the process of extracting useful information from raw data; big data refers to the vast quantities of raw data that are analyzed.
Both are integral in modern business operations and are being harnessed by all kinds of organizations for a variety of purposes.
What is Hadoop?
Apache Hadoop is an open-source software framework used for distributed storage and processing of large amounts of data. It was created by Apache Software Foundation in 2008, and it is widely used today.
What Is Hadoop Big Data: Hadoop consists of two core components: the HDFS (HDFS) and the YARN (Yet Another Resource Negotiator). The HDFS is a distributed file system that stores data, while the YARN manages computational resources such as machines and applications.
Thanks to its open-source nature, Hadoop can be integrated with other tools to provide a complete solution for data analysis and processing. Many companies offer proprietary solutions that include Hadoop as one of their components.
There are many reasons why Big Data professionals seek to master Hadoop. Knowing how to install, configure, and use this software can put you at an advantage in this field.
How are they related?
Big data and Hadoop are related concepts that refer to the era we are living in now. The rise of powerful machines, like smartphones and supercomputers, is making it easier to process large amounts of data.
As more data is produced every day, the need to manage it effectively is only going to increase. Companies need a way to leverage all their data in order to gain a marketing advantage or improved the bottom line.
Big data is more than just large volumes of raw data, however. It also refers to different types of data, how it’s used, and how much context is associated with it.
In general, there are two primary types of big data: structured and unstructured. Structured data takes the form of lists, tables, graphs, and other formal representations of information. Unstructured data takes on a more informal tone, such as audio recordings or documents with little formal structure.
Examples of Hadoop use
One of the most common uses of Hadoop is data mining. Data mining is when you use software to sift through large amounts of data to find patterns and correlations.
Many companies hire teams of data miners to work full-time to find these correlations and patterns, but you can create your own with the right software.
You can even do it for free with open-source software like Hadoop. Plus, you can use Hadoop on a cloud service like Amazon Web Services (AWS) so that you do not have to purchase your own hardware to do this.
What Is Hadoop Big Data: There are many other applications for Hadoop outside of just data mining, though. You could use it for logistics planning, financial analysis, or any other field that requires heavy data processing.
Get your data into a Hadoop environment
Once you’ve decided to use Hadoop, the next step is getting your data into the system.
There are a few ways to do this. The easiest is to export your data into a file system. You can then import this data using an ETL (extract, transform, load) tool like Apache Oozie.
If your data is in a relational database, you can use SQL queries to pull the data you want and put it into Hadoop’s distributed file system. You can also pull data from other sources like external APIs or internal systems using these methods.
Once the data is in Hadoop, it is sorted and stored so that it can be accessed later when you want to do an analysis. The sorting helps make analysis faster since all of the information is organized by specific attributes.
Run analytics directly on the data set
One of the biggest advantages of using Hadoop is that you can run analytics directly on the data set itself. This means you do not have to transfer the data to a separate platform or server to process or analyze it.
This is a very useful feature as it allows you to process and analyze data at scale. Since all of the components of the system are distributed, this also means that there is no single point of failure.
By running processing and analyzing on the data sets themselves, this also reduces the amount of time it takes to complete tasks. This is particularly useful for situations where there is a need for real-time analytics.
Another advantage of this is that it reduces the need for sophisticated databases or servers. With traditional databases, there is a need for stronger hardware to support them. With Hadoop, less sophisticated servers can be used, lowering costs.
Use a prepackaged solution
The next step is to decide if you should use a pre-packaged solution. Pre-packaged solutions are already built and tested solutions that others have used and vouched for.
There are many open-source solutions for Hadoop and Big Data, so this is a good way to start. You can also take it one step further and use commercial software like Oracle’s Sun Cloud Suite or HP’s Vertica. These have already been integrated with Hadoop, so you do not need to spend too much time trying to integrate them.
Some advantages of using pre-packaged software include less time spent integrating components, better-tested components, and support from the company that made the software. Disadvantages include not being able to customize the software as much as you would like.
Know the limitations of Hadoop
Hadoop is not a solution in itself. It is a framework that allows you to build solutions. You will need to invest time and resources into learning how to use it.
Furthermore, even if you learn how to use it, you will need to find people who can implement your solutions. As there are very few people with the required expertise, this can be a hard task.
Hadoop is free software, so there is no cost associated with using it. However, investing in people and resources to make the most of it is not free. You will have to invest in educating people on how to use it and working with it, or you will not see any benefits.
Finally, since it is open-source software, there is the potential for corruption. Security issues may arise due to users misusing the software. This issue can be addressed through education and awareness.
Understand the hardware behind Hadoop
Hadoop is a framework for parallel processing. This means it can break down a very large task into smaller sub-tasks that can be processed simultaneously.
Parallel processing is made possible by the hardware it runs on. Specifically, the hardware that handles storage, communication, and computation.
The way Hadoop uses hardware is specifically related to storage, communication, and computation. Let’s take a closer look at these three areas as they relate to Hadoop.
When it comes to storage, Hadoop uses the distributed filesystem GridFS. This allows data to be split into small chunks, also known as granularity. These granular chunks of data are then distributed across multiple machines in the cluster for storage. When it comes time to retrieve the data, all of the machines must retrieve their portion of the data in order to put the whole picture together.