BIG DATA AND HADOOP STORE AND PROCESSING MASSIVE DATASETS

Big Data is a term that describes a large volume of data storage, both structured and unstructured data. Large companies usually use big data to analyze strategies to make the right decisions. Besides that, big data can also help companies collect real-time data from products, resources, and customers to optimize customer satisfaction or efficient use of resources.Big data was created by a scientist from England in 1989. However, the term big data only appeared in 2005; at that time Doug Laney, an expert in industrial analysis, used data on a large scale. Big data and Hadoop were created in the same year.Hadoop is software that can connect many computers so that they are connected to each other and efficiently work together to store and manage data—Hadoop stores and processes data using MapReduce programming. MapReduce is a programming model that can process large amounts of data in a distributed and parallel manner in a cluster of thousands of computers. The Hadoop ecosystem includes many tools and applications that can help collect, store, analyze, and process big data.

The creation of Hadoop was inspired by the publication of the Google File System (GFS) paper in October 2003. The contents of the paper describe Big Data, which is used to store Google’s enormous amounts of data. Hadoop was created by Doug Cutting and Mike Cafarella in 2005. Over time, Hadoop is constantly updated to match technological developments. The first version was released in April 2006. The last version released by Hadoop was version 2.8, which already has various features. Hadoop can also be a solution for dealing with big data, which has significant challenges: volume, velocity, and variety.

Here are some of the software used by Hadoop:

1. Hadoop Cores

The Hadoop core comprises the Hadoop Distributed File System (HDFS) and MapReduce. Apart from that, Core Hadoop can also be downloaded on the Apache Hadoop website. HDFS supports storing and managing large data so that it is smaller to be processed in parallel. Meanwhile, MapReduce functions to convert the HDFS process into a tuple, and it will be returned to HDFS.

2. Data Mining

Data Mining is used to perform large and unstructured data analysis. Examples of data mining that can only be used with Hadoop include Apache Pig and Apache Hive.

3. NoSQL Database (Not Only SQL)

NoSQL databases are used to manage large and unstructured data. In addition, its function is needed so that data access can be done more quickly.

Hadoop and Big data are two different things. Following are the differences between Hadoop and Big data:

1. Hadoop is used to manage and analyze large amounts of data, while Big data is the data to be analyzed

2. Hadoop only uses technology such as the Hadoop Distributed File System (HDFS) to manage data, while Big Data does not use any particular technology.

Processing massive data sets requires technologies and tools to handle large amounts of data. In addition, using technology will also make it easier to analyze data to be more effective and efficient. Big data processing and analysis may require mass parallel software that runs on tens, hundreds, or thousands of servers. Processing large-scale data sets requires large storage, usually, the storage scale used to store data in terabytes or petabytes.

Here are some things to keep in mind when processing large data sets:

1. Big data will require a special NoSQL database to easily store data on certain models, so data storage provides flexibility in analyzing the sources of information obtained.

2. Of the various kinds of big data diversity, you need a system that can process various structured and unstructured data.

The things above must always be considered when processing extensive data. If not, it can lead to leaked data and will threaten the privacy of employees or customers. In addition, if the data quality is good, it will produce accurate and reliable insights.