"Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing application software."
The use of cloud computing services to process and analyze large volumes of data, often with the help of specialized big data and analytics tools.
Cloud Computing: This topic covers the basics of cloud computing, its different models, its key characteristics, its benefits, and its challenges.
Big Data Analytics: Big data analytics covers the techniques and technologies used to analyze large volumes of data, including data storage, processing, and analysis.
Data Warehousing: Data warehousing is a technology used for storing and managing data from different sources.
Data Mining: Data mining covers the extraction of potentially useful information from large data sets.
Artificial Intelligence and Machine Learning: AI and ML are technologies used for automating processes and decision making.
Hadoop: Hadoop is an open-source software for distributed storage and processing of large data sets.
Spark: Spark is an open-source software for big data processing and analytics.
NoSQL and SQL databases: This topic covers the different types of databases that can be used for dealing with big data.
Python and R: Python and R are programming languages used for data analysis, scientific computing, and machine learning.
Visualization: Visualization involves the representation of data in graphical or other visual formats to facilitate understanding.
Data governance: Data governance covers the policies, practices, and procedures used to manage data.
Security: Security covers the measures used to protect data from unauthorized access, theft, or misuse.
Tools and platforms: This topic covers the tools and platforms that can be used to store, process, and analyze big data in the cloud.
Business intelligence: Business intelligence covers the use of data to inform business decisions and optimize performance.
Cloud infrastructure: Cloud infrastructure covers the different components and services that make up a cloud environment, including storage, networking, and computing resources.
Batch processing: Batch processing is a process where a large volume of data is processed in batches without any real-time analysis.
Real-time data streaming: Real-time data streaming processes data as it is generated, allowing for immediate insights and action in real-time.
Predictive analytics: Predictive analytics uses historical data to forecast future outcomes.
Prescriptive Analytics: Prescriptive analytics is a type of analytics that recommends certain actions based on the analysis of data.
Machine learning: Machine learning is an artificial intelligence technology that enables machines to learn from data and improve their decision-making abilities.
Artificial Intelligence (AI) analytics: AI analytics uses algorithms that simulate human intelligence to discover patterns and draw insights from data.
Data warehousing: Data warehousing is a process of organizing and storing large volumes of data from various sources.
Data mining: Data mining involves extracting valuable insights from large sets of data.
Text analysis: Text analytics involves analyzing and categorizing unstructured data, such as social media content or customer reviews.
Image analysis: Image analysis utilizes computer vision technologies to identify patterns and insights from visual data.
Video analytics: Video analytics uses artificial intelligence to analyze video data, from identifying objects to recognizing facial expressions.
Voice analysis: Voice analysis involves processing and analyzing vocal data, including tone, pitch, and frequency.
Sentiment analysis: Sentiment analysis is a type of text analysis that uses natural language processing to identify and categorize opinions and emotions expressed in text.
Web analytics: Web analytics involves analyzing data from websites and online platforms to understand user behavior and optimize digital experiences.
Social media analytics: Social media analytics is used to understand engagement, sentiment, and trends on social media platforms.
Geospatial analytics: Geospatial analytics involves analyzing data that is connected to a specific geographic location, such as maps or GPS data.
"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."