"Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software."
Big Data refers to large, complex datasets that cannot be effectively managed, processed, and analyzed using traditional approaches.
Data Structures and Algorithms: Understanding how data is stored and retrieved, and how to create efficient algorithms to process large amounts of data.
Data Warehousing: Designing and implementing data storage systems to enable efficient querying and data retrieval.
Data Mining: Analyzing and extracting meaningful insights from large data sets.
Machine Learning: Creating algorithms that can learn and make predictions based on data patterns.
Data Visualization: Using graphical representations of data to convey insights and patterns to stakeholders.
Statistical Analysis: Understanding statistical methods and techniques for analyzing and interpreting data.
Big Data Technologies: Familiarizing oneself with the different technologies used to store, process, and analyze large amounts of data, such as Hadoop, Spark, and NoSQL databases.
Data Governance: Ensuring that data is used ethically and responsibly, and is compliant with legal and regulatory requirements.
Data Security: Implementing measures to protect data from theft, loss, or corruption.
Data Acquisition: Understanding how to gather data from a variety of sources, including social media, sensors, and other IoT devices.
Cloud Computing: Understanding how to deploy and manage data science applications on cloud platforms such as AWS and Azure.
Data Cleaning and Preprocessing: Preparing data for analysis, including removing duplicates, handling missing values, and transforming data into a suitable format.
Natural Language Processing: Analyzing and processing text data, including sentiment analysis and language translation.
Deep Learning: Creating neural networks that can learn and make predictions on complex data sets, such as images and speech.
Distributed Computing: Understanding how to process large data sets across multiple machines or nodes.
Structured Data: This type of data is organized and stored in predefined formats, such as tables, columns, and rows. Structured data is usually found in databases and is easy to analyze using traditional data analysis tools.
Unstructured Data: This is data that has no predefined structure or organized format. Unstructured data can include text, images, videos, and audio files, among others. It is complex to analyze as it requires advanced tools to extract information from the data.
Semi-Structured Data: This type of data is a combination of structured and unstructured data. It has some organization, but some parts of it may not fit into a defined format. Examples of semi-structured data include XML and JSON documents.
Time Series Data: This type of data is collected over time and is usually used to analyze trends, patterns, and cyclical events. Time series data can include stock prices, weather data, and website traffic analytics.
Geospatial Data: This type of data includes information that is geographically referenced, such as coordinates, addresses, and zip codes. Geospatial data is used in fields such as urban planning, environmental analysis, and public safety.
Social Media Data: This data comes from social media platforms, such as Twitter, Facebook, and Instagram, and includes text, images, and videos. Social media data is used to analyze social behavior, brand reputation, and customer engagement.
Machine Data: This type of data is generated by machines and devices, such as sensors, computers, and servers. Machine data is used to monitor and optimize operational efficiency, diagnose and fix technical issues, and track the performance of hardware and software.
Biometric Data: This data includes physiological and behavioral characteristics, such as fingerprints, facial recognition, and voiceprints. Biometric data is used for identification and authentication purposes in security systems and access control.
Cloud Data: Cloud data refers to data that is stored in cloud-based platforms and applications. Cloud data can include emails, documents, images, and videos, among other file types.
Multi-Structured Data: This type of data is a combination of two or more types of data (structured, semi-structured, unstructured). Multi-structured data is used in fields such as healthcare, finance, and e-commerce.
"Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data source."
"Big data was originally associated with three key concepts: volume, variety, and velocity."
"Thus a fourth concept, veracity, refers to the quality or insightfulness of the data."
"Areas including Internet searches, fintech, healthcare analytics, geographic information systems, urban informatics, and business informatics."
"The size and number of available data sets have grown rapidly as data is collected by devices such as mobile devices, cheap and numerous information-sensing Internet of things devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks."
"Every day 2.5 exabytes (2.5×260 bytes) of data are generated."
"By 2025, IDC predicts there will be 163 zettabytes of data."
"According to IDC, global spending on big data and business analytics (BDA) solutions is estimated to reach $215.7 billion in 2021."
"While Statista report, the global big data market is forecasted to grow to $103 billion by 2027."
"In 2011 McKinsey & Company reported, if US healthcare were to use big data creatively and effectively to drive efficiency and quality, the sector could create more than $300 billion in value every year."
"In the developed economies of Europe, government administrators could save more than €100 billion ($149 billion) in operational efficiency improvements alone by using big data."
"And users of services enabled by personal-location data could capture $600 billion in consumer surplus."
"The processing and analysis of big data may require 'massively parallel software running on tens, hundreds, or even thousands of servers'."
"What qualifies as 'big data' varies depending on the capabilities of those analyzing it and their tools."
"For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options."
"The best interpretation is that it is a large body of information that cannot be comprehended when used in small amounts only."
"Analysis of data sets can find new correlations to 'spot business trends, prevent diseases, combat crime and so on'."
"Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, biology, and environmental research."
"One question for large enterprises is determining who should own big-data initiatives that affect the entire organization."