Big Data Trends and Measurements

Large datasets with volume, velocity, or variety too significant for traditional databases to manage and process.

The sheer size of big data, often from various sources, making storage and processing complex.

The speed at which the data is generated, especially in high-volume sources. Critical for applications that need quick analysis.

Different forms of data involved. From structured data (databases) to unstructured data (text, images).

Databases designed for structured data, not optimized for large, high-speed datasets typical in big data.

The process of analyzing large datasets using specialized tools and techniques.

The various places where big data comes from, including business transactions, social media, and sensors.

The core characteristics of big data as volume, velocity, and variety.

Big data comes in different formats: structured, unstructured, and semi-structured data.

Data organized in rows and columns, like spreadsheets.

Data not organized in a predefined format, like images or social media posts.

Accuracy of Big Data; Ensuring data is correct to avoid errors

Removing incorrect or faulty data to improve analysis accuracy

Usefulness of data for a specific purpose; is data useful?

Analyzing data immediately as it's generated

Various sources contributing to big data, including mobile phone activity, such as messages, photos, and social media.

A large volume of data that is difficult to process using traditional data processing tools.

The increasing amount and variety of data being generated worldwide.

Big data can include various formats, such as structured, semi-structured, and unstructured data.

The process of collecting, storing, and analyzing big data to drive insights.

Network of physical objects embedded with sensors, software, and other technologies.

Online search using the meaning of words.

The amount of data being generated globally.

Different types of data being generated (structured, semi-structured, and unstructured).

A unit of digital information equal to 10 to the power of 21 bytes, or 1 quintillion bytes.

The rate at which the amount of data is increasing globally.

Predicted to reach approximately 175 zettabytes of data in 2025.

The percentage of people globally who have access to the Internet.

The increasing scale of data that started with bytes, progressing through kilobytes, megabytes, gigabytes, terabytes, petabytes, exabytes, zettabytes, and yottabytes.

Data generated from customer actions like online or in-store purchases.

Data collected from multiple outside sources, like a credit report.

Data from experiments, often combining created and transactional data, like using a focus group.

Data gathered from sources like Google searches or GPS.

Data created by users, like social media posts and videos.

Fixes problems like bad formatting, missing values, or corrupted records, in data.

Transforming raw data into consistent formats, addressing format differences across sources.

Removing duplicate data records to prevent errors and inefficiencies.

Data measured in Celsius and Fahrenheit temperature units.

Selecting data based on criteria or eliminating incorrect data.

Methods for analyzing data, including statistics, regression, and more.

Analyzing data over longer time intervals, like daily or monthly.

Analyzing data as it's generated, often within seconds.

Data analysis allowing user input and interactive query processing.

Techniques used to process data for analysis, such as MapReduce and Stream Processing.

Representations of analysis results; static displays data stored, dynamic updates data regularly, and Interactive lets users interact and explore.