You must have heard the same old story; Big Data could be a game-changer for your company as it offers a unique opportunity to make faster managerial decisions, deliver personalized customer experience and harness tons of structured and unstructured data. But you are wondering, what is Hadoop? Well, today we will focus on it and discuss several things you need to know about Hadoop before enrolling to big data Hadoop training.
So, what is Hadoop?
This is a question you don't even want to ask your tech guy as you are sure it will portray you as a confused newbie in the room. So, to define Hadoop in simple non-geeky terms, we can say that it’s a solution to common database problems such as:
- Too much data that exceed your server capacity
- Slow execution of SQL statement when your table is growing, and many other problems that keep popping up every day.
Hadoop is an open-source framework designed to address the three main challenges of Big Data: Volume, Velocity, and Variety - the three Vs. Hadoop starts making sense when conventional relational databases tussle to scale.
Before we narrow down to basics of Hadoop, let's see how it works:
Hadoop works on a simple principle of grid computing, which means dividing process execution and data storage on multiple clusters or nodes of servers. Take, for instance: you have a file that's larger than your server capacity; this means you cannot store it. But with Hadoop, you can store files bigger than your single server capacity by splitting data into chunks that are then distributed on multiple clusters or nodes. It accomplishes this through Hadoop Distributed File System.
With that in mind, here are five things you need to know about Hadoop before you get started:
This stands for Hadoop Distributed File System. It is optimized to store large volumes of data across a computing node. Users just load files into the HDFS, and it figures out where and how to distribute data. Almost any interaction with Hadoop involves HDFS either directly or indirectly.
Often mistaken for Hadoop itself, MapReduce is the Hadoop programming model for analytics on data in HDFS. To grasp the concept of MapReduce, assume two relational database tables – one for account transactions and the second for a bank account. To compute the average transaction amount for each of the accounts, you'd map the two original tables to form a single dataset through a join. Then, all transactions with similar account numbers would be aggregated to a single amount. This is the same concept applied by MapReduce when distributing large data set across a node.
This allows the user to plan a tabular structure on the available data so that they can steer clear of the MapReduce API for SQL-like abstraction known as HiveQL.
This is a centralized service that coordinates activities among the machines in the nodes.
An analytic abstraction that’s similar to Hive but has a query syntax known as Pig Latin, which favors a scripting pipeline approach over SQL-like HiveQL.
There are many things you need to learn about Hadoop. Hadoop is always associated with big data. As IOT devices expand and data collected grows, the demand for Hadoop’s processing capabilities will grow as well. So you need to take advantage of big data. You can enroll in an online Hadoop training to learn more.