MPP has evolved because traditional computing paradigms (indexes, distributed data sets, etc.) did not provide acceptable response times on massive tables. Massively Parallel Processing (MPP) Shared-nothing Database technologies have become …
Big Data – Activities
Define Big Data Strategy and Business Needs An organization’s Big Data strategy needs to be aligned with and support its overall business strategy and business requirements and be part of …
Big Data Strategy – Criteria
A Big Data strategy must include criteria to evaluate: What problems the organization is trying to solve. What it needs analytics for: While one advantage of Data Science is that …
Predictive Analytics Vs. Prescriptive Analytics
Predictive Analytics Predictive Analytics is the sub-field of Supervised Learning where users attempt to model data elements and predict future outcomes through evaluation of probability estimates. Rooted deeply in mathematics …
Sentiment Analysis and Data/Text Mining
Sentiment Analysis Media monitoring and text analysis are automated methods for retrieving insights from large unstructured or semi-structured data, such as transaction data, social media, blogs, and web news sites. …
Machine Learning
Machine Learning explores the construction and study of learning algorithms. It can be viewed as a union of unsupervised learning methods, more commonly referred to as data mining, and supervised …
Services-Based Architecture – SBA
Services-based architecture (SBA) is emerging as a way to provide immediate (if not completely accurate or complete) data, as well as update a complete, accurate historical data set, using the …
Big Data Characterized in terms of V’s
Volume: Refers to the amount of data. Big Data often has thousands of entities or elements in billions of records. Velocity: Refers to the speed at which data is captured, …
Data Science – Process and Iterative Phases
The Data Science process follows the scientific method of refining knowledge by making observations, formulating and testing hypotheses, observing results, and formulating general theories that explain results. Within Data Science, …