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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...
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Lay Programmer

 Lay Programmer

I use the term lay programmer to mean someone who is programming without thinking themselves as a programmer. Someone who spends a large part of her day working on spreadsheets is doing programming, often very intense programming. Usually however she won't call herself a programmer, nor think of spending much time learning how to program better.

It's easy for professional programmers to get sniffy at lay programmers, but lay programmers usually are domain experts who know a great deal about what a program should do. Our challenge is to think of ways to engage them more effectively in software development, and provide tools that are easy for them to use, but also capable of being well structured programs that can evolve efficiently and integrate well into the wider software ecosystem.

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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...
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Business Capability Centric

 Business Capability Centric A business-capability centric team is one whose work is aligned long-term to a certain area of the business. The team lives as long as the said business-capability is relevant to the business. This is in contrast to project teams that only last as long as it takes to deliver project scope. For example, an e-commerce business has capabilities such as buying and merchandising, catalog, marketing, order management, fulfilment and customer service. An insurance business has capabilities such as policy administration, claims administration, and new business. A telecom business has capabilities such as network management, service provisioning and assurance, billing, and revenue management. They may be further divided into fine-grained capabilities so that they can be owned by teams of manageable size. Business-capability centric teams are “think-it, build-it and run-it” teams. They do not hand over to other teams for testing, deploying or supporting what they...
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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...
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