Glossary of Terms related to Big Data-Alphabet P to Z

Alphabet – P

Parse:

Parse or parsing is the process of breaking a data block like a string into smaller parts by following a set of rules, so that it can be more easily analysed, managed, or processed by a computer.

Pattern Recognition:

Pattern recognition is viewed as synonymous with data mining and machine learning which focuses on the recognition of patterns and regularities within large data sets. Pattern recognition is the process in which we classify the input data into objects or classes based on certain important features. Persistent storage: Persistent Storage is any data storage device where the data remains saved even after the power to that device is switched off. It is also called non-volatile storage. It is used for any data that is required to be used again, either by many instances or after the end of a specific instance.

Petabytes (PB):

A Petabyte is approximately equal to 1,000 Terabytes. In other words a Petabyte is the number of Bytes that is equal to 1 followed by 15 zeroes. 1 Petabyte could hold approximately 20 million 4-door filing cabinets full of text.

According to the IBM Dictionary of computing, the hard disk space, or data storage space in terms of bytes in decimal notation are described as1 Bit = Binary Digit; 8 Bits = 1 Byte; 1000 Bytes = 1 Kilobyte; 1000 Kilobytes = 1 Megabyte;
1000 Megabytes = 1 Gigabyte; 1000 Gigabytes = 1 Terabyte;
1000 Terabytes = 1 Petabyte and 1000 Petabytes = 1 Exabyte & so on.

Predictive Analytics:

Predictive analytics is the branch of the advanced analytics which is used to make predictions about unknown future events. Predictive analytics in the context of Big-Data uses many advanced techniques from data mining, advanced statistical processes, machine learning, and artificial intelligence for analyzing the data for making forecasts with probabilities of what might happen in future.

Prescriptive Analytics:

Prescriptive analytics is the area of business analytics (BA) wherein managers make data-driven decisions to find out the best course of action under a particular situation. Prescriptive analytics builds on predictive as well as descriptive analytics by carrying out the analysis of outcomes for prescribing the best action plans.

Python:

Python is a high-level programming language used for general purpose programming. It is easy to use by all programmers as concept can be expressed through fewer lines of code.

Alphabet – R

R:

R is an open-source programming language for statistical computing and graphics. It is used primarily for data visualization and predictive analytics.

Radio Frequency Identification (RFID):

Radio Frequency Identification uses wireless non-contact radio-frequency electromagnetic fields to automatically identify and track tags attached to objects. The tags contain electronically-stored information. RFID tags can be embedded into every possible ‘thing’ for generating data for analysis.

Real-Time Stream Processing:

Stream processing is a model for analyzing sequences of real-time streaming data on many machines in parallel, using continuous queries.

Relational Database Management System (RDBMS):

Relational database management system is database management system based upon the relational model. It handles, captures, and analyzes data which is grouped based on shared attributes called relations. Here the data is presented to the user as relations presented in tabular form with tables having set of rows and columns.

Resilient Distributed Dataset (RDD):

Resilient Distributed Datasets (RDD) is a fundamental data structure of Apache Spark. It is a fault-tolerant way of storing the data across multiple machines operating in parallel.

Alphabet – S

SaaS (Software-as-a-Service):

Software-as-a-Service is an online software licensing and delivery model. Here the software is licensed on a subscription basis and is centrally hosted over the cloud.

Semi-Structured Data:

Semi-structured data is a form of data which is not formatted in conventional ways. It is not totally raw or unstructured data yet it can have some data tables, tags or other markers thereby differentiating it from semantic elements and can maintain hierarchies of fields and records in the data.

Sentiment Analysis:

Sentiment analysis refers to the process of computationally gathering feelings, emotions and opinions expressed by the consumers in the form of text documents, social media, calls to customer care executives, surveys etc. The process of sentiment analysis generally involves text analytics and natural language processing to assess as to whether the consumer’s attitude towards a particular product or service is positive, negative, or neutral.

Shard:

Shard in context of databases refers to a horizontal partition of data in a database or a search engine. Every individual partition is called shard. Every shard is stored on a separate database server instance, to distribute the load.

Smart Data:

Smart Data refers to useful digital information which is formatted and is actionable for filtration by algorithms and further consolidation and analysis.

Spatial Analysis:

Spatial analysis refers to the process of analysing spatial data like topological or geographic data for identifying regularities within the vast spectrum of data distributed over geographic space.

Stream Processing:

Stream processing is real-time processing of streaming data record-by-record through continuous queries. Its objective is to continuously calculate mathematical or statistical analytics on the fly within high volume of data in real time.

Structured Data:

Structured data refers to the information having high degree of organization and the one which is stored in a particular field like in a relational database or spreadsheet. Objective of structured data is to facilitate seamless inclusion in the relational database and simplification of searching operations.

Alphabet – T

Taxonomy:

Taxonomy is the science of data classification according to a predefined system, for building a catalog used to provide a conceptual framework for easy access and retrieval of data.

Telemetry:

Telemetry is an automated communications process by which measurements and other data are accessed at remote points and transmitted further across receiving equipment for actions.

Terabyte (TB):

Terabyte is a fairly popular term after Gigabyte in the world digital storage these days. Yet I am describing it here to explain its relation with its next higher counterpart (Petabyte). Terabyte is approximately equal to 1,000 Gigabytes. In other words a Terabyte is the number of Bytes that is equal to 1 followed by 12 zeroes. According to the IBM Dictionary of computing, the hard disk space, or data storage space in terms of bytes in decimal notation are described as
1 Bit = Binary Digit; 8 Bits = 1 Byte; 1000 Bytes = 1 Kilobyte; 1000 Kilobytes = 1 Megabyte; 1000 Megabytes = 1 Gigabyte; 1000 Gigabytes = 1 Terabyte and; 1000 Terabytes = 1 Petabyte; & so on.A single Terabyte can hold 3.6 million 300 Kilobyte images or about 300 hours of good quality video files, or 1,000 copies of the Encyclopedia Brittanica.

Transformation:

Transformation in context of data is the process of converting information from one format to another. Generally the objective of transformation remains migration of data from one system to the other

Alphabet – U

Unstructured Data:

Unstructured data is the one that neither having a pre-defined data model nor organized according to some pre-defined manner.

Alphabet – V

Visualization:

Visualization in context of data refers to the process of carrying out analysis of data and projecting it in a understandable and readable formats like graphical format like charts or complex graphs etc.

Alphabet – Y

Yottabytes (YB):

A Yottabyte is approximately equal to 1,000 Zettabytes. In other words a Yottabyte is the number of Bytes that is equal to 1 followed by 24 zeroes. A single Yottabyte file may take about 11 trillion years to download from the Internet with the help of a high-power broadband. It can be said that the entire digital World Wide Web or Internet may be about one Yottabyte and it may double every 18 months.

According to the IBM Dictionary of computing, the hard disk space, or data storage space in terms of bytes in decimal notation are described as1 Bit = Binary Digit; 8 Bits = 1 Byte; 1000 Bytes = 1 Kilobyte; 1000 Kilobytes = 1 Megabyte;
1000 Megabytes = 1 Gigabyte; 1000 Gigabytes = 1 Terabyte; 1000 Terabytes = 1 Petabyte;
1000 Petabytes = 1 Exabyte; 1000 Exabytes = 1 Zettabyte;
1000 Zettabytes = 1 Yottabyte; and 1000 Yottabytes = 1 Brontobyte & so on

Alphabet – Z

Zones:

Zones are particular areas which perform a well-defined and specific function within a Data Lake. A data lake having proper zones are helpful in providing quality-checked data to the users.

Zettabytes (ZB):

A Zettabyte is approximately equal to 1,000 Exabytes or 1 billion terabytes or 250 Billion DVDs. In other words a Zettabyte is the number of Bytes that is equal to 1 followed by 21 zeroes.

According to the IBM Dictionary of computing, the hard disk space, or data storage space in terms of bytes in decimal notation are described as
1 Bit = Binary Digit; 8 Bits = 1 Byte; 1000 Bytes = 1 Kilobyte; 1000 Kilobytes = 1 Megabyte; 1000 Megabytes = 1 Gigabyte; 1000 Gigabytes = 1 Terabyte; 1000 Terabytes = 1 Petabyte; 1000 Petabytes = 1 Exabyte; 1000 Exabytes = 1 Zettabyte and 1000 Zettabytes = 1 Yottabyte; & so on

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