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Data Mesh Principles and Logical Architecture

 Data Mesh Principles and Logical Architecture The great divide of data What do we really mean by data? The answer depends on whom you ask. Today’s landscape is divided into  operational data  and  analytical data . Operational data sits in databases behind business capabilities served with microservices, has a transactional nature, keeps the current state and serves the needs of the applications running the business. Analytical data is a temporal and aggregated view of the facts of the business over time, often modeled to provide retrospective or future-perspective insights; it trains the ML models or feeds the analytical reports. The current state of technology, architecture and organization design is reflective of the divergence of these two data planes - two levels of existence, integrated yet separate. This divergence has led to a fragile architecture. Continuously failing ETL (Extract, Transform, Load) jobs and ever growing complexity of labyrinth of data pipel...

Out come Oriented

 Out come Oriented

 effort, better sales conversion, greater customer satisfaction, i.e business outcomes. Outcome-oriented teams are those that are mandated and equipped to deliver business outcomes, such teams have people with the capability to carry out all necessary activities to realize the outcome.. By contrast, ActivityOriented teams are neither equipped nor mandated to do so. They can only perform one of several activities required to realize an outcome.

A mandate to deliver a business outcome is very different from a mandate to deliver a certain amount of scope. Scope delivery is easy, relatively speaking. Outcome realization requires real collaboration between those who understand the problem and those who can fashion various levels of solution for it. Initial attempts at solution lead to a better understanding of the problem which leads to further attempts at better solutions. This doesn’t work where the product management organization is separate from the development (scope-delivery) organization.

Outcome-oriented teams are necessarily cross-functional (multidisciplinary) whereas ActivityOriented teams are typically mono-functional (single specialty). In the most traditional scenario, an outcome might simply be defined in terms of a project. The project is funded on the basis of a business case and therefore the desired outcome is to realize what is promised in the business case. However, depending on the size of the project it may be organized as one or more teams. When these teams are set up along activity boundaries it becomes an activity-oriented project (or program) organization. On the other hand, we achieve an outcome-oriented organization by dividing the overall outcome into sub-outcomes and assigning sub-outcomes to cross-functional teams that are self-sufficient in terms of people required to deliver the sub-outcome.

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ActivityOriented

  ActivityOriented Any significant software development effort requires several different activities to occur: analysis, user experience design, development, testing, etc. Activity-oriented teams organize around these activities, so that you have dedicated teams for user-experience design, development, testing etc. Activity-orientation promises many benefits, but software development is usually better done with   OutcomeOriented   teams. Traditionally, big businesses with large IT departments (Enterprise IT) have tended to execute IT development projects with a bunch of activity-oriented teams drawn from a matrix IT organization (functional organization). The solid-lined arms of the matrix (headed by a VP of development, testing and so on) are usually along activity boundaries and they loan out “resources” to dotted-lined project or program organizations. Common justifications for doing so include: It helps standardization of conventions and techniques in development if a...

Data Mesh Principles and Logical Architecture

 Data Mesh Principles and Logical Architecture The great divide of data What do we really mean by data? The answer depends on whom you ask. Today’s landscape is divided into  operational data  and  analytical data . Operational data sits in databases behind business capabilities served with microservices, has a transactional nature, keeps the current state and serves the needs of the applications running the business. Analytical data is a temporal and aggregated view of the facts of the business over time, often modeled to provide retrospective or future-perspective insights; it trains the ML models or feeds the analytical reports. The current state of technology, architecture and organization design is reflective of the divergence of these two data planes - two levels of existence, integrated yet separate. This divergence has led to a fragile architecture. Continuously failing ETL (Extract, Transform, Load) jobs and ever growing complexity of labyrinth of data pipel...

Pair Programming Misconceptions

Pair Programming Misconceptions A bunch of common misconceptions about Pair Programming. You have to do pair programming if you're doing an agile process. This is utterly false. 'Agile' is a very broad term defined only in terms of values and principles, most notably in the Manifesto for Agile Software Development. The manifesto doesn't mention pair programming and most agile methods don't make it part of their approach. Since pair programming is a practice of XP it's had a lot of influence in the agile community. As a result it's often mentioned as an agile practice - meaning a practice that's commonly used by people on agile projects. But that's an observation not a prescription. Extreme Programming forces you to do Pair-Programming This is much more nuanced issue. Pair-Programming is one of the practices of XP and has been since its inception. The nuance here is whether XP practices are mandatory for a team that claims to be doing XP. This is actu...