As data continues to increase at an evermore rapid pace, organizations struggle to deal with this data torrent, let alone use it to analyze and capture value. The ways used to understand this big data also is increasingly rapidly, which introduces myriad terms used to define these methods.
The follow is an attempt to provide natural explanations to some of the significant terms and technologies you will come across when you’re getting into big data.
Algorithms: Mathematical and analytical formulas that also include statistical processes used to analyze data. Algorithms are implemented in software to analyze, process the input data and produce output or results.
Analytics: The course of depicting conclusions based on the raw data. With the help of analysis, otherwise-meaningless numbers and data can be converted into something useful. The emphasis here is on interpretation and not on big software systems. That may be why data analysts are very experienced in the art of storytelling.
Biometrics: Using analytics and technology to identify people by one or many of physical characteristics, such as fingerprint recognition, face recognition or iris recognition.
Cassandra: A very well-known open-source database management system managed by the Apache Software Foundation, which has been constructed to handle high volumes of data throughout distributed servers.
Cloud: A term used to describe data or software running on remote servers rather than locally. Data stored in the cloud is usually reachable over the internet, wherever the owner of that data in the world might be.
Database: A systematized collection of data, such as schemas, charts or tables. A database management system (DBMS) is software that helps in data analysis and exploration.
Data Mining: This term can mean different things for different context. To the layman, it means the automatic examining of large databases. To an analyst, it refers to the pool of statistical and machine learning methods used in the databases.
Dark Data: The information collected and managed by a business that is never put to use, yet sits waiting to be studied. Most companies don’t realize they have a lot of this kind of data lying around.
Data Scientist: A skilled expert in extracting value and insights from data. A data scientist typically is someone with skills in computer science, analytics, mathematics, creativity, statistics, communication and data visualization, as well as strategy and business.
Gamification: The process of creating a game-like environment in areas that typically would not have games, such as websites, to attract users and increase engagement. In the terms of big data, a gamification is a powerful tool for incentivizing the data collection.
Hadoop: An open-source software structure that works mainly by processing and storing files and data. Hadoop is known for its big processing power, which makes it easy to run a host of tasks alongside. It helps companies access, save and analyze enormous amounts of data.
IoT: An acronym that stands for internet of things. Principally, it defines an ecosystem of things, from diapers to self-driving cars, that can communicate with each other via the internet. Their sensors generate a large amount of data that can be analyzed.
Machine Learning: A highly casual way performing data analysis. Machine learning mechanizes logical model building and trusts on the ability of the device to adapt. With the use of algorithms, models dynamically learn and improve themselves every time they process any new data. Machine learning is not new; however, it is receiving massive attraction as a modern tool for data analysis. It allows devices to grow and acclimatize without demanding numerous hours of extra work by the scientists.
MapReduce: A model for programming, generating and processing massive data sets. It does two different things: the Map, which includes rotating one dataset to the other, more valuable and fragmented dataset made of bits known as tuples; and the Reduce, which takes all of these fragmented tuples and breaks them even further. It results in a useful breakdown of information.
NoSQL: Database management systems that do not use relational tables used in most old-style database systems. The data retrieval and storage system is designed for managing massive volumes of data without tabular categorization.
SaaS: An acronym for software as a service, a method of application delivery in which vendors host applications and make them available through the internet. SaaS providers deliver their services via the cloud.
Spark: An open-source computing structure developed at the University of California, Berkeley, and donated to the Apache Foundation. It is used for interactive analytics and machine learning.
