Big Data Fundamentals

A collection of related data, where each member (datum) has the same set of attributes. Examples include tweets in a file, images in a directory, or rows from a database table saved as a CSV.

The process of examining data to uncover facts, relationships, patterns, insights, or trends. It aims to support informed decision making.

A field of study involving the complete lifecycle of data, including collecting, cleaning, organizing, storing, analyzing, and governing data.

A type of analytics that focuses on understanding the past, answering questions about events that have already happened.

A type of analytics that explores possible future outcomes based on past data and current trends.

A type of analytics that focuses on suggesting actions to improve outcomes based on data insights.

A type of analytics that explores the underlying causes and relationships behind data patterns.

The field dedicated to analyzing, processing and storing massive amounts of information from diverse sources, often needing to be processed quickly and efficiently.

Data that does not follow a predefined structure. It can be text, images, audio, or video.

Data that has some structure but not a rigid, relational structure. It's hierarchical or graph-based. Examples include XML, JSON, and CSV.

A markup language used to represent data in a hierarchical format. It's commonly used for web services and data exchange.

A lightweight format for storing and exchanging data. It's used for web applications and APIs. It's based on key-value pairs.

A plain text format used to store tabular data. It's separated by commas or other delimiters.

Data that describes the characteristics and structure of a dataset. It provides metadata about a data source.

Data that captures relationships between entities. It's well-structured and organized in a relational schema.

The ability to handle different formats and types of data in Big Data solutions. Data variety is crucial for Big Data.

Descriptive Analytics aims to summarize and describe existing data to gain insights.

Diagnostic Analytics delves deeper into data to uncover the "Why" behind events or trends.

Predictive Analytics uses past data to forecast future events or outcomes.

Prescriptive Analytics goes beyond predictions to suggest actions based on the analysis.

The sheer volume of data being generated and stored, often measured in petabytes and beyond.

The speed at which data is generated, processed, and analyzed. This is critical for real-time insights.

The diversity of data types and formats, including structured, unstructured, and semi-structured data.

The quality, accuracy, and reliability of data. Data with high veracity is trustworthy and useful.

The value or usefulness of data for a particular organization or purpose.

Data created or generated by humans, such as emails, social media posts, and documents.

Data generated by machines or sensors, such as website logs, sensor readings, and transaction records.

Data that is organized in a structured format, usually in rows and columns like a spreadsheet.

Data that is not organized in any specific format, such as text documents, images, and videos.

Data that has some structure but not as formal as structured data. Examples include XML and JSON.

Data that describes other data, providing information about its context, format, and other attributes.