Big data terms you should know

Big data includes specialised terms that are sometimes difficult to understand. And the first step to learn big data is to know the basic definition of these complicated terminology.

We bring you a comprehensive list of big data terminology widely used today. Let us know if you would like to add any big data terminology missing in this list

A

Aggregation – a process of searching, gathering and presenting data.
Algorithms – a mathematical formula that can perform certain analyses on data.
Analytics – the discovery of insights in data.
Anomaly detection – the search for data items in a dataset that do not match a projected pattern or expected behaviour. Anomalies are also called outliers, exceptions, surprises or contaminants and they often provide critical and actionable information.
Anonymization – making data anonymous; removing all data points that could lead to identify a person.
Application – computer software that enables a computer to perform a certain task
Artificial Intelligence – developing intelligence machines and software that are capable of perceiving the environment and take corresponding action when required and even learn from those actions.

B

Behavioural Analytics – analytics that informs about the how, why and what instead of just the who and when. It looks at humanized patterns in the data.
Big Data Scientist – someone who is able to develop the algorithms to make sense out of big data.
Big data start-up – a young company that has developed new big data technology.
Biometrics – the identification of humans by their characteristics.
Brontobytes– approximately 1000 Yottabytes and the size of the digital universe tomorrow. A Brontobyte contains 27 zeros
Business Intelligence – the theories, methodologies and processes to make data understandable.

C

Classification analysis – a systematic process for obtaining important and relevant information about data, also meta data called; data about data.
Cloud computing– a distributed computing system over a network used for storing data off-premises.
Clustering analysis– the process of identifying objects that are similar to each other and cluster them in order to understand the differences as well as the similarities within the data.
Cold data storage– storing old data that is hardly used on low-power servers. Retrieving the data will take longer.
Comparative analysis– it ensures a step-by-step procedure of comparisons and calculations to detect patterns within very large data sets.
Complex structured data– data that are composed of two or more complex, complicated, and interrelated parts that cannot be easily interpreted by structured query languages and tools.
Computer generated data– data generated by computers such as log files.
Concurrency – performing and executing multiple tasks and processes at the same time.
Correlation analysis– the analysis of data to determine a relationship between variables and whether that relationship is negative (- 1.00) or positive (+1.00).
Customer Relationship Management – managing the sales and business processes, big data will affect CRM strategies.

D

Dashboard – a graphical representation of the analyses performed by the algorithms
Data aggregation tools – the process of transforming scattered data from numerous sources into a single new one.
Data analyst – someone analysing, modelling, cleaning or processing data.
Database – a digital collection of data stored via a certain technique.
Database-as-a-Service – a database hosted in the cloud on a pay per use basis, for example Amazon Web Services.
Database Management System– collecting, storing and providing access of data.
Data centre – a physical location that houses the servers for storing data.
Data cleansing– the process of reviewing and revising data in order to delete duplicates, correct errors and provide consistency.
Data custodian– someone who is responsible for the technical environment necessary for data storage.
Data ethical guidelines – guidelines that help organizations being transparent with their data, ensuring simplicity, security and privacy.
Data feed – a stream of data such as a Twitter feed or RSS.
Data marketplace – an online environment to buy and sell data sets.
Data mining – the process of finding certain patterns or information from data sets.
Data modelling – the analysis of data objects using data modelling techniques to create insights from the data.
Data set – a collection of data.
Data virtualization – a data integration process in order to gain more insights. Usually it involves databases, applications, file systems, websites, big data techniques, etc.).
De-identification – same as anonymization; ensuring a person cannot be identified through the data.
Discriminant analysis – cataloguing of the data; distributing data into groups, classes or categories. A statistical analysis used where certain groups or clusters in data are known upfront and that uses that information to derive the classification rule.
Distributed File System – systems that offer simplified, highly available access to storing, analysing and processing data.
Document Store Databases– a document-oriented database that is especially designed to store, manage and retrieve documents, also known as semi structured data.

E

Exploratory analysis– finding patterns within data without standard procedures or methods. It is a means of discovering the data and to find the data sets main characteristics.
Exabytes– approximately 1000 petabytes or 1 billion gigabytes. Today we create one Exabyte of new information globally on a daily basis.
Extract, Transform and Load (ETL) – a process in a database and data warehousing meaning extracting the data from various sources, transforming it to fit operational needs and loading it into the database.

F

Failover – switching automatically to a different server or node should one fail.
Fault-tolerant design – a system designed to continue working even if certain parts fail.

G

Gamification– using game elements in a non game context; very useful to create data therefore coined as the friendly scout of big data.
Graph Databases– they use graph structures (a finite set of ordered pairs or certain entities), with edges, properties and nodes for data storage. It provides index-free adjacency, meaning that every element is directly linked to its neighbour element.
Grid computing– connecting different computer systems from various location, often via the cloud, to reach a common goal.

H

Hadoop – an open-source framework that is built to enable the process and storage of big data across a distributed file system.
HBase – an open source, non-relational, distributed database running in conjunction with Hadoop.
HDFS – Hadoop Distributed File System; a distributed file system designed to run on commodity hardware.
High-Performance-Computing (HPC) – using supercomputers to solve highly complex and advanced computing problems.

I

In-memory – a database management system stores data on the main memory instead of the disk, resulting is very fast processing, storing and loading of the data.
Internet of Things – ordinary devices that are connected to the internet at any time anywhere via sensors.

J

Juridical data compliance – relevant when you use cloud solutions and where the data is stored in a different country or continent. Be aware that data stored in a different country has to oblige to the law in that country.

K

KeyValue Databases– they store data with a primary key, a uniquely identifiable record, which makes easy and fast to look up. The data stored in a KeyValue is normally some kind of primitive of the programming language.

L

Latency – a measure of time delayed in a system.
Legacy system– an old system, technology or computer system that is not supported any more.
Load balancing – distributing workload across multiple computers or servers in order to achieve optimal results and utilization of the system.
Location data – GPS data describing a geographical location.
Log file – a file automatically created by a computer to record events that occur while operational.

M

Machine2Machine data – two or more machines that are communicating with each other.
Machine data – data created by machines via sensors or algorithms.
Machine learning – part of artificial intelligence where machines learn from what they are doing and become better over time.
MapReduce – a software framework for processing vast amounts of data.
Massively Parallel Processing (MPP) – using many different processors (or computers) to perform certain computational tasks at the same time.
Metadata – data about data; gives information about what the data is about.
MongoDB – an open-source NoSQL database.
Multi-Dimensional Databases – a database optimized for data online analytical processing (OLAP) applications and for data warehousing.
Multivalued Databases– they are a type of NoSQL and multidimensional databases that understand three-dimensional data directly. They are primarily giant strings that are perfect for manipulating HTML and XML strings directly.

N

Natural Language Processing– a field of computer science involved with interactions between computers and human languages.
Network analysis– viewing relationships among the nodes in terms of the network or graph theory, meaning analysing connections between nodes in a network and the strength of the ties.
NewSQL– an elegant, well-defined database system that is easier to learn and better than SQL. It is even newer than NoSQL.
NoSQL – sometimes referred to as ‘Not only SQL’ as it is a database that doesn’t adhere to traditional relational database structures. It is more consistent and can achieve higher availability and horizontal scaling.

O

Object Databases – they store data in the form of objects, as used by object-oriented programming. They are different from relational or graph databases and most of them offer a query language that allows object to be found with a declarative programming approach.
Object-based Image Analysis – analysing digital images can be performed with data from individual pixels, whereas object-based image analysis uses data from a selection of related pixels, called objects or image objects.
Operational Databases– they carry out regular operations of an organisation and are generally very important to a business. They generally use online transaction processing that allows them to enter, collect and retrieve specific information about the company.
Optimization analysis- the process of optimization during the design cycle of products done by algorithms. It allows companies to virtually design many different variations of a product and to test that product against pre-set variables.
Ontology– ontology represents knowledge as a set of concepts within a domain and the relationships between those concepts.
Outlier detection – an outlier is an object that deviates significantly from the general average within a dataset or a combination of data. It is numerically distant from the rest of the data and therefore, the outlier indicates that something is going on and generally therefore requires additional analysis.

P

Pattern Recognition – identifying patterns in data via algorithms to make predictions of new data coming from the same source.
Petabytes – approximately 1000 terabytes or 1 million gigabytes. The CERN Large Hydron Collider generates approximately 1 petabyte per second.
Platform-as-a-Service – a services providing all the necessary infrastructure for cloud computing solutions.
Predictive analysis – the most valuable analysis within big data as they help predict what someone is likely to buy, visit, do or how someone will behave in the (near) future. It uses a variety of different data sets such as historical, transactional, social or customer profile data to identify risks and opportunities.
Privacy – to seclude certain data / information about oneself that is deemed personal.
Public data – public information or data sets that were created with public funding.

Q

Quantified Self – a movement to use application to track ones every move during the day in order to gain a better understanding about ones behaviour.
Query – asking for information to answer a certain question.

R

Re-identification – combining several data sets to find a certain person within anonymized data.
Regression analysis – to define the dependency between variables. It assumes a one-way causal effect from one variable to the response of another variable.
RFID– Radio Frequency Identification; a type of sensor using wireless non-contact radio-frequency electromagnetic fields to transfer data.
Real-time data– data that is created, processed, stored, analysed and visualized within milliseconds.
Recommendation engine – an algorithm that suggests certain products based on previous buying behaviour or buying behaviour of others.
Routing analysis– finding the optimized routing using many different variables for a certain means of transport in order to decrease fuel costs and increase efficiency.

S

Semi-structured data – a form a structured data that does not have a formal structure like structured data. It does however have tags or other markers to enforce hierarchy of records.
Sentiment Analysis– using algorithms to find out how people feel about certain topics
Signal analysis– it refers to the analysis of measurement of time varying or spatially varying physical quantities to analyse the performance of a product. Especially used with sensor data.
Similarity searches – finding the closest object to a query in a database, where the data object can be of any type of data.
Simulation analysis – a simulation is the imitation of the operation of a real-world process or system. A simulation analysis helps to ensure optimal product performance taking into account many different variables.
Smart grid– refers to using sensors within an energy grid to monitor what is going on in real-time helping to increase efficiency.
Software-as-a-Service – a software tool that is used of the web via a browser
Spatial analysis– refers to analysing spatial data such geographic data or topological data to identify and understand patterns and regularities within data distributed in geographic space.
SQL – a programming language for retrieving data from a relational database.
Structured data – data that is identifiable as it is organized in structure like rows and columns. The data resides in fixed fields within a record or file or the data is tagged correctly and can be accurately identified.

T

Terabytes – approximately 1000 gigabytes. A terabyte can store up to 300 hours of high-definition video.
Time series analysis – analysing well-defined data obtained through repeated measurements of time. The data has to be well defined and measured at successive points in time spaced at identical time intervals.
Topological Data Analysis– focusing on the shape of complex data and identifying clusters and any statistical significance that is present within that data.
Transactional data– dynamic data that changes over time.
Transparency – consumers want to know what happens with their data and organizations have to be transparent about that.

U

Un-structured data – unstructured data is regarded as data that is in general text heavy, but may also contain dates, numbers and facts.

V

Value– all that available data will create a lot of value for organizations, societies and consumers. Big data means big business and every industry will reap the benefits from big data.
Variability– it means that the meaning of the data can change (rapidly). In (almost) the same tweets for example a word can have a totally different meaning.
Variety – data today comes in many different formats: structured data, semi-structured data, unstructured data and even complex structured data.
Velocity – the speed at which the data is created, stored, analysed and visualized.
Veracity – organizations need to ensure that the data is correct as well as the analyses performed on the data are correct. Veracity refers to the correctness of the data.
Visualization – with the right visualizations, raw data can be put to use. Visualizations of course do not mean ordinary graphs or pie-charts. They mean complex graphs that can include many variables of data while still remaining understandable and readable.
Volume– the amount of data, ranging from megabytes to brontobytes.

W

Weather data – an important open public data source that can provide organisations with a lot of insights if combined with other sources.

X

XML Databases – XML Databases allow data to be stored in XML format. XML databases are often linked to document-oriented databases. The data stored in an XML database can be queried, exported and serialized into any format needed.

Y

Yottabytes– approximately 1000 Zettabytes, or 250 trillion DVD’s. The entire digital universe today is 1 Yottabyte and this will double every 18 months.

Z

Zettabytes – approximately 1000 Exabytes or 1 billion terabytes. Expected is that in 2016 over 1 zettabyte will cross our networks globally on a daily basis.