Erik on Software

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architecture

A collection of 133 issues

Community Comment: Part 43 - M&A technical due diligence starting point

* Initial technical due diligence guidance offered to fellow acquisition entrepreneur * Caveat: I don't suggest software heavy business acquisitions for non-technical buyers * As a starting point, it's important to get code access, and understand integrations and hosting / environments * When quickly evolving third-party products like AI are involved,

Media Query Source: Part 54 - Reworked (US digital magazine); Can AI add value before digital transformation is complete?

* Reworked (US digital magazine) * Can AI add value before digital transformation is complete? * It's a good idea to define "AI" and "digital transformation" first * An entire business doesn't need to be digitally transformed before AI work starts The query responses I provided

Community Comment: Part 40 - Being bold & clear helps avoid the traps of enterprise architecture

* Enterprise architecture traps and how to avoid them * Be bold and direct when it comes to describing traps and cures * Ivory tower architectures are traps, but who actually creates architectures? * If you want to make progress, avoid usage of unclear or circular definitions The comments I provided in reaction to

Media Query Source: Part 51 - CIO (US digital magazine); The cost of digital inefficiency: tens of millions of dollars

* CIO (US digital magazine) * The cost of digital inefficiency: tens of millions of dollars * Governance providing guidelines for approved software usage mitigates risk * However, care should be taken to minimize risks caused by overly rigid governance My responses ended up being included in an article at CIO (March 10, 2025)

Media Query Source: Part 49 - InformationWeek (US digital magazine); Effective ways to tame technical debt

* InformationWeek (US digital magazine) * Effective ways to tame technical debt * Technical debt involves weighing trade-offs between short-term and long-term goals * Collaboration is especially critical when the technologies used to build a product drive what the product can provide for customers The query responses I provided to InformationWeek on February 7,

Community Comment: Part 34 - Popular data engines use Java / the JVM because it ruled the enterprise for many years

* Reasoning behind popular data engines & Java * Java / the JVM long ruled the enterprise * Spark, Flink, Presto & Trino are all Java-based * Remember: Presto & Trino share some history The comments I provided in reaction to a community discussion thread. Head of Product at Data Lakehouse Product Firm: 🤷‍♂️ GitHub PRs

Community Post: Part 3 - Delta Lake 3.0 is like an electric car charging standard

* The power of open source * Delta Lake 3.0 is just one example * Parquet-based data lakehouse file format * Similar to electric car charging standard A well-received community post I recently made: https://www.linkedin.com/posts/erikgfesser_snowflake-deltalake-iceberg-activity-7080482215016476672-Xrh0 Just over two months ago in mid-April of this year, I couldn&

Community Comment: Part 30 - With respect to Azure, Databricks Unity Catalog & Microsoft Purview is not an either-or proposition

* Databricks Unity Catalog & Microsoft Purview * Not an either-or proposition for Azure * Likely need to integrate these products * Integration dependant on several factors The comments I provided in reaction to a community discussion thread. Cloud Solutions Architect at Consulting Firm: Read my latest post if you want to know what

Community Comment: Part 28 - Technical debt is actually a subset of organizational debt

* Not all technical debt is the same * Discussion with purported CTOs * Technical debt is subset of organizational debt * Business + technology = "our business" The comments I provided in reaction to a community discussion thread. Fractional CTO and "Engineering Management Expert": "Technical debt? Is that a joke?

Community Comment: Part 27 - Assessment is needed when deciding between transactional and analytical modeling

* Transactional versus analytical modeling * Discussion with purported staff data engineer * Making broad generalizations doesn't help * Quoting $ savings in vacuum not advised The comments I provided in reaction to a community discussion thread. Owner of Data Engineering Training Firm and former Staff Data Engineer at Airbnb: If you model