Are you confused by terms like Big Data, Apache or Hadoop and their functions and definitions? Fear not, we are going to discuss all this and much more in detail below.
What is Big Data?
Businesses collect large volumes of data on a daily basis. This data can be structured or unstructured. Structured data refers to the organized form of data, like spreadsheets or similar forms and unstructured data refers to data in the form of text files or multimedia files. The data can be collected from any source and all this data put together is known as Big Data. It is used by many industries like energy, telecom, retail, manufacturing, banking, insurance, public, media, healthcare etc.
Importance of Big Data
Big Data helps businesses analyze their daily activities which can lead to better strategic actions and decisions to boost the business. It can also help them optimize the launch of a new product and reduce the production time for some products. An added advantage is the root cause of some problems and defects related to a particular product can be determined if Big Data is coupled with a high-powered analytics tool such as Apache Hadoop.
What is Apache Hadoop?
Apache Hadoop is an open-source software framework for distributed storage and distributed processing of large volumes of data usually written in Java. It can provide quick and steady analysis of large volumes of data, both structured and unstructured.How does Apache Hadoop work?
Apache Hadoop comprises of the following modules
- Hadoop MapReduce: A programming model for extensive data processing.
- Hadoop Distributed File System (HDFS): As the name suggests, it is a distributed file system that stores data on main machines which provides high overall bandwidth across the clusters of machines.
- Hadoop Common: It is the collection of libraries and utilities required by other Hadoop modules.
- Hadoop YARN: It is a redesigned resource manager which separates functionalities of resource management and job scheduling into different daemons.
Apache Pig, Apache Hive, and Apache HBase are a few projects, apart from the aforementioned ones, which are included in the Apache Hadoop platform. All the modules in Hadoop are designed with the fundamental aspect in mind that hardware problems can occur with any system in the cluster. To combat this, if any system in cluster faces a hardware issue, the tasks assigned to that system are immediately transferred to other systems in the cluster so that the analysis is done within the stipulated time.
Activities performed by Apache Hadoop on Big Data
- Storage: It is necessary to store large volumes of Big Data in order to analyze it.
- Process: The large volumes of data are processed by enriching, cleansing, transforming, calculating and running algorithms on it.
- Access: The data which is analyzed is segregated and stored in such a manner that it is easy to retrieve it and also search for it.
Examples of How a Module Works
- Hadoop MapReduce: It consists of JobTracker and TaskTracker, once the client program is copied on each node.
- JobTracker figures out the number of splits from the input data and identifies a few TaskTrackers based on their proximity to data sources.
- JobTracker sends task requests to the selected TaskTrackers.
- Each TaskTracker starts the map phase processing by extracting the input data from the splits.
- TaskTracker notifies the JobTracker once the map task is complete.
- The TaskTracker is notified when the task is completed.
- When all the TaskTrackers are done, the JobTracker informs the appointed TaskTrackers for reduced phase.
- Each TaskTracker reads the region files and conducts the reduce function, which in turn collects the key value into the output file.
- After both phases are completed, the JobTracker provides access to the client program.