With support for Machine Learning data pipelines, Apache Spark framework is a great choice for building a unified use case that combines ETL, batch analytics, streaming data analysis, and machine learning. In this fifth installment of Apache Spark article series, author Srini Penchikala discusses Spark ML package and how to use it to create and manage machine learning data pipelines.
Smartphone users are estimated to number 3.5 billion by 2019, and the different usages (mobile is most common during morning commutes and late at night, for example) create new challenges and opportunities. Data collection via our devices, smart-home gadgets and even our cars allows software engineers to offer increasingly personalized user experiences.
The CMMI Institute has launched the Data Management Maturity (DMM)SM model. It can be used to improve data management, helping organizations to bridge the gap between business and IT. Using the DMM, organizations can evaluate and improve their data management practices. The model leverages the principles, structure, and proven approach of the Capability Maturity Model Integration (CMMI).
In this article, author talks about the need for change in predictive modeling focus and compares four types of data mining:algorithm mining, landscape mining, decision mining and discussion mining.
This article defines a Data Encryption Infrastructure (DEI) which encompasses technology components and an application architecture that governs the protection of sensitive data within an enterprise. 1