Big Data

What is big data?

Big data is an umbrella term used to describe extremely large data sets that are difficult to process and analyze in a reasonable amount of time using traditional methods. 


Big data consists of structured, unstructured, and semi-structured data. It is formally characterized by its five Vs: volume, velocity, variety, veracity, and value.

  • Volume describes the massive scale and size of data sets that contain terabytes, petabytes, or exabytes of data.
  • Velocity describes the high speed at which massive amounts of new data are being generated.
  • Variety describes the broad assortment of data types and formats that are being generated.
  • Veracity describes the quality and integrity of the data in an extremely large data set.
  • Value describes the data’s ability to be turned into actionable insights.


Big data comes from a wide variety of sources across different industries and domains. Below are some examples of sources for large data sets and the types of data they include.

Big Data Source Description
Customer Data Data collected through CRM systems, including customer profiles, sales records, and customer interactions.
E-commerce Transactions Data generated from online retail platforms, including customer orders, product details, payment information, and customer reviews.
Financial Transactions Data obtained from banking systems, credit card transactions, stock markets, and other financial platforms.
Government and Public Data Data provided by government agencies, census data, public transportation data and weather data.
Health and Medical Records Data from electronic health records (EHRs), medical imaging, wearable health devices, clinical trials, and patient monitoring systems.
Internet of Things (IoT) Devices Data collected from various IoT devices such as intelligent sensors, smart appliances, wearable devices, and connected vehicles.
Research and Scientific Data Data from research experiments, academic studies, scientific observations, digital twin simulations, and genomic sequencing.
Sensor Networks Data gathered from environmental sensors, industrial machinery, traffic monitoring systems, and other wireless sensor networks.
Social Media Platforms Data generated from social media platforms like Facebook, Twitter, Instagram, and LinkedIn, including posts, comments, likes, shares, and user profiles.
Web and Mobile Applications Data produced by users while interacting with websites, mobile apps, and online services, including clicks, page views, and user behavior.


Big data is important because of its potential to reveal patterns, trends, and other insights that can be used to make data-driven decisions

From a business perspective, bighelps organizations improve operational efficiency and optimize resources. For example, by aggregating large data sets and using them to analyze customer behavior and market trends, an e-commerce business can make decisions that will lead to increased customer satisfaction, loyalty – and, ultimately, revenue.

Advancements in open-source tools that can store and process large data sets have significantly improved big data analytics. Apache’s active communities, for instance, have often been credited with making it easier for newcomers to use big data to solve real-world problems.

Types of Big Data

Big data can be categorized into three main types: structured, unstructured, and semi-structured data.

  • Structured big data: It is highly organized and follows a pre-defined schema or format. It is typically stored in spreadsheets or relational databases. Each data element has a specific data type and is associated with predefined fields and tables. Structured data is characterized by its consistency and uniformity, which makes it easier to query, analyze and process using traditional database management systems.
  • Unstructured big data: It does not have a predefined structure and may or may not establish clear relationships between different data entities. Identifying patterns, sentiments, relationships, and relevant information within unstructured data typically requires advanced AI tools such as natural language processing (NLP), natural language understanding (NLU), and computer vision.
  • Semi-structured big data: contains elements of both structured and unstructured data. It possesses a partial organizational structure, such as XML or JSON files, and may include log files, sensor data with timestamps, and metadata.

In most cases, an organization’s data is a mixture of all three data types. For example, a large data set for an e-commerce vendor might include structured data from customer demographics and transaction records, unstructured data from customer feedback on social media, and semi-structured data from internal email communication.


The evolution of big data since the beginning of the century has been a roller coaster ride of challenges followed by solutions. 

At first, one of the biggest problems with the vast amounts of data that were being generated on the internet was that traditional database management systems were not designed to store the sheer volume of data produced by businesses as they went digital.

Around the same time, data variety became a considerable challenge. In addition to traditional structured data, social media and the IoT introduced semi-structured and unstructured data into the mix. As a result, companies had to find ways to efficiently process and analyze these varied data types, another task for which traditional tools were ill-suited.

As the volume of data grew, so did the amount of incorrect, inconsistent, or incomplete information, and data management became a significant hurdle.

It wasn’t long before the new uses for extremely large data sets raised a number of new questions about data privacy and information security. Organizations needed to be more transparent about what data they collected, how they protected it, and how they used it. 

Disparate data types typically need to be combined into a single, consistent format for data analysis. The variety of data types and formats in large semi-structured data sets still poses challenges for data integration, analysis, and interpretation.

For example, a company might need to blend data from a traditional relational database (structured data) with data scraped from social media posts (unstructured data). The process of transforming these two data types into a unified format that can be used for analysis can be time-consuming and technically difficult.

Advancements in machine learning and artificial intelligence (AI) helped address many of these challenges, but they are not without their own set of difficulties. 

Big Data Tools

Dealing with large data sets that contain a mixture of data types requires specialized tools and techniques tailored for handling and processing diverse data formats and distributed data structures. Popular tools include:

Azure Data Lake: A Microsoft cloud service known for simplifying the complexities of ingesting and storing massive amounts of data.

Beam: An open-source unified programming model and set of APIs for batch and stream processing across different big data frameworks.

Cassandra: An open-source, highly scalable, distributed NoSQL database designed for handling massive amounts of data across multiple commodity servers.

Databricks: A unified analytics platform that combines data engineering and data science capabilities for processing and analyzing massive data sets.

Elasticsearch: A search and analytics engine that enables fast and scalable searching, indexing, and analysis for extremely large data sets.

Google Cloud: A collection of big data tools and services offered by Google Cloud, such as Google BigQuery and Google Cloud Dataflow.

Hadoop: A widely used open-source framework for processing and storing extremely large datasets in a distributed environment.

Hive: An open-source data warehousing and SQL-like querying tool that runs on top of Hadoop to facilitate querying and analyzing large data sets.

Kafka: An open-source distributed streaming platform that allows for real-time data processing and messaging.

KNIME Big Data Extensions: Integrates the power of Apache Hadoop and Apache Spark with KNIME Analytics Platform and KNIME Server.

MongoDB: A document-oriented NoSQL database that provides high performance and scalability for big data applications.

Pig: An open-source high-level data flow scripting language and execution framework for processing and analyzing large datasets.

Redshift: Amazon’s fully-managed, petabyte-scale data warehouse service.

Spark: An open-source data processing engine that provides fast and flexible analytics and data processing capabilities for extremely large data sets.

Splunk: A platform for searching, analyzing, and visualizing machine-generated data, such as logs and events.

Tableau: A powerful data visualization tool that helps users explore and present insights from large data sets.

Talend: An open-source data integration and ETL (Extract, Transform, Load) tool that facilitates the integration and processing of extremely large data sets.

Big Data and AI 

Big data has been closely linked with advancements in artificial intelligence like generative AI because, until recently, AI models needed to be fed vast amounts of training data so they could learn how to detect patterns and make accurate predictions.

In the past, the axiom “Big data is for machines. Small data is for people.” was often used to describe the difference between big data and small data, but that analogy no longer holds true. As AI and ML technologies continue to evolve, the need for big data to train some types of AI and ML models is diminishing, especially in situations when aggregating and managing big data sets is time-consuming and expensive. 

In many real-world scenarios, it is not feasible to collect large amounts of data for every possible class or concept that a model may encounter. Consequently, there has been a trend towards using big data foundation models for pre-training and small data sets to fine-tune them.

The shift away from big data towards using small data to train AI and ML models is driven by several technological advancements, including transfer learning and the development of zero-shot, one-shot, and few-shot learning models. 


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Margaret Rouse

Margaret Rouse is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical, business audience. Over the past twenty years her explanations have appeared on TechTarget websites and she's been cited as an authority in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine and Discovery Magazine.Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages. If you have a suggestion for a new definition or how to improve a technical explanation, please email Margaret or contact her…