Category: Architecture

Uli Homann and Eric Charran (via Ben Brauer) talk about the concept of architectural erosion:

The way Eric thinks about architectural erosion is when architects and engineers work together, they construct a system or solution. They have launched that solution into production. It’s performing well for a period of time and then change happens. Whether it’s a change in requirements, a change in infrastructure, or a change in customer habits, DevOps signals saying that people are using a certain feature versus they’re not using a certain feature. What ended up happening is there’s a moment in time in which we look at velocity around how do we implement this change and make the applications experience, do what the customer or the end user wants as quickly as possible. Then there’s the strategic picture of managing things like technical debt, which is if I do something tactical, I’m probably going to do it fast and cheap and not necessarily the “right way.” This then accrues to the architectural patterns, longevity and scalability and all those other types of things, and then that goes into my pile of technical debt.

Read on to learn more about the topic and what we, as technical professionals, can do to mitigate this risk.

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Jean-Georges Perrin answers some questions:

How about data virtualization? If you have different Data Hubs with different data models, how do you integrate them?

As illustrated in the next figure, you can use data virtualization pointing to various physical data stores. Your onboarding pipeline can be “virtual” or at least leveraging virtualized data stores. You will gain in data freshness by reducing latency but you may be limited in the number of data transformations you want to perform towards your interoperable model.

Read on for the full set of questions and answers.

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Koen Verbeeck lays out a recommendation:

In this blog post I’ll talk about another of those rules/mantras/patterns/maxims:

build once, add metadata

I’m not sure if I’m using the right words, I heard something similar in a session by Spark enthusiast Simon Whiteley. He said you should only write code once, but make it flexible and parameterized, so you can add functionality just by adding metadata somewhere. A good example of this pattern can be found in Azure Data Factory; by using parameterized datasets, you can build one flexible pipeline that can copy for example any flat file, doesn’t matter which columns it has. I have blogged about this:

Click through to learn more about the concept, as well as some tips on how you’d do that in various data movement products (e.g., SSIS, ADF, Logic Apps).

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Brendan Tierney looks at the data:

Getting back to the topic of this post, I’ve gathered some data and obtained some league tables from some sites. These will help to have a closer look at what is really happening in the Database market throughout 2022. Two popular sites who constantly monitor the wider internet and judge how popular Databases area globally. These sites are DB-Engines and TOPDB Top Database index. These are well know and are frequently cited. Both of these sites give some details of how they calculate their scores, with one focused mainly on how common the Database appears in searches across different search engines, while the other one, in addition to search engine results/searches, also looks across different websites, discussion forms, social media, job vacancies, etc.

I don’t necessarily believe that these are totally accurate, though on the whole, I do expect the results to be directionally accurate. I’ve used DB-Engines data several times in the past and like to point out that, for any given year, 7 or 8 of the top 10 database engines are relational.

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