Big Data Data Analytics and Applications
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- Overview
Big data is a large and complex collection of data that businesses are constantly generating and collecting. Data analytics is the process of extracting meaningful information from data.
The main purpose of big data is to store and process large amounts of data. The main purpose of data analytics is to analyze raw data and discover insights.
Big data can help industries such as banking and retail by providing important technologies such as fraud detection and operational analysis systems. Data analytics can help industries such as banking, energy management, healthcare, tourism and transportation achieve new advances.
Machine learning (ML) is a specific subset of artificial intelligence (AI) used to train machines how to learn. This makes it possible to quickly and automatically generate models that can analyze larger and more complex data.
Analytics and ML have been around for decades, but the use of ML has been limited due to the challenges associated with it. ML is processor-intensive and requires specialized skills and tools that are not yet widely available. However, the arrival of cloud computing has made it cheaper and easier for more organizations to obtain the computing power needed to run ML.
The processing power of our smartphones and laptops makes it possible for almost anyone to access and use ML analytics and make smarter, data-driven decisions.
- Big Data Analytics
The Harvard Business Review once called the role of the Data Scientist "The Sexiest Job of the 21st Century." That’s because it takes someone with the skills of a scientist to make something useful out of big data.
Big data analytics applications enable big data analysts, data scientists, predictive modelers, statisticians and other analytics professionals to analyze growing volumes of structured transaction data, plus other forms of data that are often left untapped by conventional BI and analytics programs.
This encompasses a mix of semi-structured and unstructured data - for example, Internet clickstream data, web server logs, social media content, text from customer emails and survey responses, mobile phone records, and machine data captured by sensors connected to the internet of things (IoT).
The value of the data is tied to comparing, associating or referencing it with other data sets. Analysis of big data usually deals with a very large quantity of small data objects with a low tolerance for storage latency.
- Synchronous and Asynchronous Analytics
Big data analytics is the use of advanced analytic techniques against very large, diverse big data sets that include structured, semi-structured and unstructured data, from different sources, and in different sizes from terabytes to zettabytes.
There are two basic types of big data analytics - synchronous and asynchronous - but both have big data storage appetites and specialized needs.
Synchronous and asynchronous are distinguished by the way they process data. But they both have big data storage appetites and specialized needs. S
- Synchronous Analytics:
- Asynchronous Analytics:
[More to come ...]
