Practical DevOps for Big Data/Review of UML Diagrams Course

Welcome to Practical DevOps for Big Data/Review of UML Diagrams Online Course with live Instructor using an interactive cloud desktop environment DaDesktop. Experience remote live training using an interactive, remote desktop led by a human being!

#big data Training #software architecture Training

14 hours


What is Practical DevOps for Big Data/Review of UML Diagrams?

Documenting Big Data Architectures can entail re-use of classical notations for software architecture description augmented with appropriate notations aimed at isolating and identifying the data-intensive nature of Big Data applications. In this vein, the DICE ecosystem offers a plethora of ready-to-use tools and notations to address a variety of quality issues (performance, reliability, correctness, privacy-by-design, etc.). In order to make profit of these tools, the user has to use the explicit notation we have defined to support their scenario. The notation in question entails building specific UML diagrams enriched with specific profiles, that is, the standard UML mechanism to design domain-specific extensions --- in our case, the mechanism in question was used to define stereotypes and tagged values inside the DICE Profiles and specific to model data-intensive constructs, features, and characteristics. The DICE profiles tailor the UML meta-model to the domain of DIAs. For example, the generic concept of Class can become more specific, i.e., to have more semantics, by mapping it to one or many concrete Big Data notions and technical characteristics, such as, compute and storage nodes (from a more abstract perspective) or Storm Nimbus nodes. Besides the power of expression, the consistency of the models behind the DICE profile remains guaranteed thanks to the meta-models and their relations we defined using the UML standard. In essence, the role of these diagrams and their respective profiles is twofold:

  1. Provide a high level of abstraction of concepts specific to the Big Data domain (e.g., clusters, nodes…) and to Big Data technologies (e.g., Cassandra, Spark…);
  2. Define a set of technical (low level) properties to be checked/evaluated by tools.


  • Introduction
  • Methodological Overview
  • Existing Solutions and UML Modelling Summary
  • Quick Reference Scenario
  • DICE UML Modelling in Action: A Sample Scenario
    • Step a: DPIM Model
    • Step b and c: DPIM Model Refinement
    • Step d: DTSM Model Creation
    • Step e: DDSM Model Creation


Course Category:

   Big Data Training

Last Updated:


Course Schedules

Date Time
June 13, 2023 (Tuesday) 09:30 AM - 04:30 PM
June 27, 2023 (Tuesday) 09:30 AM - 04:30 PM
July 11, 2023 (Tuesday) 09:30 AM - 04:30 PM
July 25, 2023 (Tuesday) 09:30 AM - 04:30 PM
August 8, 2023 (Tuesday) 09:30 AM - 04:30 PM
August 22, 2023 (Tuesday) 09:30 AM - 04:30 PM
September 5, 2023 (Tuesday) 09:30 AM - 04:30 PM

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