Unit 1: Introduction to Big Data

Large and complex data sets that require specialized processing and analysis to extract valuable insights.

The volume of data generated keeps increasing rapidly over time.

The sheer size of big data makes it impossible to process using traditional methods.

It refers to the techniques and tools used to store, process, and analyze big data.

A framework specifically designed to store and analyze large datasets.

A type of database that is not structured like a traditional relational database. It is more flexible and scalable to handle Big Data.

The interconnected network of devices and objects that collect and share data.

A branch of artificial intelligence that focuses on building systems that can learn from data.

The process of analyzing large datasets to identify patterns, trends, insights, and potential business opportunities.

Information collected from various sources like social media, e-commerce websites, and sensor networks.

The ability to use big data analytics to understand customer preferences, behaviors, and needs. This helps businesses tailor their products and services to better meet customer expectations.

The use of big data analytics to create powerful marketing campaigns that reach the right audience with the right message at the right time.

Leveraging big data analytics to drive innovation and product development by identifying emerging trends, customer needs, and market opportunities.

The process of collecting, cleaning, organizing, and preparing big data for analysis.

The challenges associated with managing and analyzing big data, including storage, processing, security, and visualization.

The amount of data generated, which is increasing rapidly.

The speed at which data is generated and processed.

The diverse types of data generated, including structured, semi-structured, and unstructured data.

Refers to the data's structure, including sources, granularity, types, and whether it's static or real-time.

Describes the data's condition, including whether it's ready for analysis or needs cleansing.

Explores the origins and context of the data, including where it was generated, why, and its sensitivity.

A characteristic of Big Data that refers to the constant addition of new and varied data.

A characteristic of Big Data relating to the ability to access and analyze data in real-time to drive immediate decisions.

Unstructured data like images, audio, and video grows at a faster rate than other types of data.

Experts estimate that 80% of the data within an organization is unstructured.

Walmart processes over a million customer transactions every hour.

Facebook stores, accesses, and analyzes over 30 petabytes of user-generated data.

Big Data applications can predict a patient's deteriorating condition in advance by analyzing their health data.

Telecom companies can use Big Data to analyze network data and reduce data packet loss, improving customer experience.

Retail businesses can use Big Data to understand consumer behavior and provide personalized recommendations, like Amazon's suggestions.

Managing traffic flow effectively in cities requires analyzing data from sensors and using it to optimize traffic lights and routes.

Data stored in its original format, without any predefined structure, like video files, text documents, or images.

Data organized in a predefined format with rows and columns, like spreadsheets or databases.

Data gathered from various sources, like social media, databases, and sensor data, often with different formats and quality.

The process of combining data from multiple sources, often facing challenges due to inconsistencies, missing data, and duplicates.

The threats to the confidentiality and integrity of big data, including unauthorized access and data breaches.

The risks associated with big data concerning the privacy and security of personal information.

A system designed for efficient query and analysis of large datasets, often focusing on historical data from various sources.

Data specifically tailored for query and analysis, unlike data for daily operations or transaction processing.

A data system designed to help analyze data from multiple sources. It's focused on understanding past trends and patterns.

Data warehouses are structured around specific business topics or areas, such as sales, marketing, or customer behavior.

Data from different sources is combined into a consistent format, resolving inconsistencies and naming conflicts.

Once data enters the warehouse, it remains unchanged. This allows analysis of historical trends without worry about data modification.

Data warehouses capture data over time to identify patterns and trends. This emphasis on historical data is crucial for business analysis.

Data warehouses are primarily used for reading and retrieving information to support decision-making.

Data warehouses typically store a small number of large tables, which are designed for efficient analysis of large volumes of data.

Data warehouses are designed for a relatively small number of users who engage in lengthy analysis sessions.