BT

InfoQ Homepage Data Content on InfoQ

Articles

RSS Feed
  • Scaling Agile in a Data-Driven Company

    The IT department of Cerved Group experimented with Scrum, Kanban, Lean, SAFe, and Nexus, to learn what works for them and fine-tune and continuously improve their way of working. In their transformation, they focused on the culture and mindset to cultivate high-performing teams, to improve the quality of products for customers, and to help managers transforming themselves in servant leaders.

  • Managing Data in Microservices

    This article provides practical examples of how to manage data in microservices, with an emphasis on migrating from a monolithic database. It is recommended to build a monolith first, and only migrate to microservices after you actually require the scaling and other benefits they provide.

  • Big Data Processing with Apache Spark - Part 5: Spark ML Data Pipelines

    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.

  • Personal UX -- Solving Unique Problems Created by Widespread Global Mobilization

    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.

  • Improving Data Management with the DMM

    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).

  • Beyond Data Mining

    In this article, author talks about the need for a change in the predictive modeling community’s focus and compares the four types of data mining: algorithm mining, landscape mining, decision mining, and discussion mining.

  • Automating Data Protection Across the Enterprise

    This article builds on the foundational Regulatory Compliant Cloud Computing (RC3) architecture for application security in the cloud by defining a Data Encryption Infrastructure(DEI) which is not application specific. DEI encompasses technology components and an application architecture that governs the protection of sensitive data within an enterprise.

BT

Is your profile up-to-date? Please take a moment to review and update.

Note: If updating/changing your email, a validation request will be sent

Company name:
Company role:
Company size:
Country/Zone:
State/Province/Region:
You will be sent an email to validate the new email address. This pop-up will close itself in a few moments.