In a nutshell _______________________________________________________________________________________________________________ Data Strategy Data Architecture Data Models You might be Interested in Reading of:
Oracle Autonomous Database
Oracle says, Autonomous Database is a mission-critical, converged database that runs transactional and analytic workloads. It automatically scales, tunes, patches, and secures all the workloads using machine learning to provide …
Big Data – Metrics
Metrics are vital to any management process; they not only quantify activity, but can define the variation between what is observed and what is desired. Technical Usage MetricsMany of the …
Big Data – Readiness and Risk Assessment
As with any development project, implementation of a Big Data or Data Science initiative should align with real business needs. Assess organizational readiness in relation to critical success factors: Business …
Big Data – Visualization Tools
Traditional tools in data visualization have both a data and a graphical component. Advanced visualization and discovery tools use in-memory architecture to allow users to interact with the data. Patterns …
Big Data – Distributed File-Based Databases
Distributed file-based solutions technologies, such as the open source Hadoop, are an inexpensive way to store large amounts of data in different formats. Hadoop stores files of any type – …
Big Data – MPP Shared-nothing Technologies and Architecture
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 …
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. …
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, …
Data Science – Dependency
Developing Data Science solutions involves the iterative inclusion of data sources into models that develop insights. Data Science depends on: Rich Data Sources: Data with the potential to show otherwise …