# SeattleDataGuy's Newsletter Learn About End-To-End Data Flows (Data Engineering, MLOps, and Data Science) This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Why Data Teams Should Focus on Business Value, Not Just Technical Decisions DevFeed: [Why Data Teams Should Focus on Business Value, Not Just Technical Decisions]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-09-05T16:08:27Z Content type: opinion Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [data](), [coding](), [Microservice](), [Cloud]() Tags: [architecture](), [business-value](), [data-platform](), [productivity](), [technical]() ### AI overview This opinion article argues that engineers and data teams often focus heavily on technical choices--such as architecture, platforms, and infrastructure--while neglecting the business reasons for building an application or feature. It encourages teams to prioritize meaningful outcomes and business value. ### Source excerpt Why data teams need to spend less time debating the stack and more time questioning the work ## From Vibe Coding to the AI Software Factory DevFeed: [From Vibe Coding to the AI Software Factory]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-08-26T22:10:01Z Content type: opinion Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [Artificial Intelligence](), [Large Language Model](), [Vibe coding](), [coding](), [Low-Code / Internal Tools]() Tags: [ai](), [developers](), [engineering](), [llms](), [vibe-coding]() ### AI overview The article examines how engineering teams progress from using LLMs for code completion and isolated experiments toward standardized, repeatable systems for producing software with AI. It describes this progression as a spectrum of software-factory practices and argues that many companies remain at the stage of disconnected individual workbenches and one-off macros. ### Source excerpt How I've Seen Engineering Teams Use LLMs Over The Past Few Years ## Your Data Warehouse Isn't Integrated Just Because the Tables Are in One Place DevFeed: [Your Data Warehouse Isn't Integrated Just Because the Tables Are in One Place]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-07-18T23:26:52Z Content type: opinion Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [centralization](), [data]() Tags: [centralization](), [data](), [warehouse]() ### AI overview The article argues that placing tables in one data warehouse centralizes data but does not by itself integrate it. ### Source excerpt Centralization is not integration! ## Forward Deployed Engineering Faces Rapid Expansion and Talent Constraints DevFeed: [Forward Deployed Engineering Faces Rapid Expansion and Talent Constraints]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-07-06T14:28:59Z Content type: opinion Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [Artificial Intelligence](), [Microsoft](), [Amazon Web Services]() Tags: [ai](), [aws](), [banking](), [engineering](), [enterprise](), [healthcare](), [microsoft]() ### AI overview The article examines the rapid growth of Forward Deployed Engineering (FDE) organizations as Microsoft and AWS invest heavily in the model. It argues that AI vendors need FDEs to help enterprises apply AI beyond chat, combining technical expertise with knowledge of domains such as healthcare and banking. The author predicts the role may eventually resemble systems integrators or contractors and become less distinct as adoption expands. ### Source excerpt But AI vendors need enterprise adoption at any cost ## In 2026 The Data Fundamentals Matter More Than Ever DevFeed: [In 2026 The Data Fundamentals Matter More Than Ever]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-06-13T22:51:35Z Content type: opinion Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [data-engineering](), [data-modeling](), [Data Science](), [Python](), [SQL](), [Artificial Intelligence]() Tags: [2026](), [data](), [data-engineering](), [data-science](), [developer](), [fundamentals](), [python](), [sql]() ### AI overview This opinion article argues that data fundamentals remain important in 2026 despite changing technology trends and job titles. It identifies messy data and weak data foundations as persistent bottlenecks, and emphasizes SQL, Python, data modeling, and related engineering skills. ### Source excerpt Otherwise we are headed towards a massive data mess ## Why AI Companies Partner With Consultancies to Help Enterprises Adopt LLMs DevFeed: [Why AI Companies Partner With Consultancies to Help Enterprises Adopt LLMs]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-05-26T17:00:41Z Content type: opinion Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [Artificial Intelligence](), [anthropic](), [Large Language Model](), [Claude](), [Software Engineering]() Tags: [ai](), [anthropic](), [claude](), [consulting](), [implementation](), [integration](), [llms]() ### AI overview The article examines why AI companies such as Anthropic invest in consultancies, systems integrators, and implementation partners despite claims that AI can replace knowledge workers. It argues that deploying AI in real organizations requires understanding business processes, edge cases, incentives, data, and existing systems. ### Source excerpt Last week, I noticed that Anthropic was hiring for a partner success manager. ## The 5 Silent Failures in Data Pipelines DevFeed: [The 5 Silent Failures in Data Pipelines]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-04-24T19:06:02Z Content type: article Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [Data pipelines](), [data](), [CSV]() Tags: [csv](), [dashboard](), [data-pipeline](), [data-pipelines](), [pipelines](), [reports](), [schema]() ### AI overview The article explains how data pipelines can fail silently without triggering errors or obvious warnings, causing stale or incorrect data to reach dashboards and reports. It introduces schema drift as one failure mode, including unexpected changes to CSV or XML files loaded from SFTP. ### Source excerpt How Your Pipelines Lie to You Without Throwing a Single Error ## Data Pipeline Foundations - Everything You Need To Know About Data Pipelines DevFeed: [Data Pipeline Foundations - Everything You Need To Know About Data Pipelines]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-04-18T15:50:56Z Content type: article Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [Data pipelines](), [data](), [API](), [Database](), [Amazon S3]() Tags: [api](), [data](), [data-pipelines](), [database](), [pipeline](), [s3](), [sftp]() ### AI overview This article serves as a central collection of introductory resources about data pipelines. It explains that pipelines move data from sources to destinations and highlights sources such as S3 buckets, SFTP files, APIs, and databases. ### Source excerpt Hi, fellow future and current Data Leaders; Ben here 👋 ## Daily Tasks With Data Pipelines - Data Quality Checks And The Problem With Noisy Checks DevFeed: [Daily Tasks With Data Pipelines - Data Quality Checks And The Problem With Noisy Checks]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-04-07T22:24:38Z Content type: article Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [Data Quality](), [Data pipelines](), [data]() Tags: [data-pipelines](), [data-quality](), [pipelines](), [quality]() ### AI overview The article discusses data quality checks in data pipelines and notes that teams may receive 137 data quality alerts every morning. ### Source excerpt Every morning, your team wakes up to 137 data quality alerts. ## How AI-Generated Code Could Erode Engineers' Understanding and Debugging Skills DevFeed: [How AI-Generated Code Could Erode Engineers' Understanding and Debugging Skills]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-03-25T23:15:59Z Content type: opinion Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [Artificial Intelligence](), [Code](), [Users](), [data]() Tags: [agents](), [ai](), [code](), [python]() ### AI overview This opinion article imagines a future in which AI agents generate data analyses and code with little human understanding or quality assurance. It argues that overreliance on AI-generated code could weaken engineers' ability to understand dependencies, diagnose failures, and assess whether changes are correct. ### Source excerpt It's 2030, and your boss just asked you to pull data to help better segment your users and understand their behaviors. ## Full Refresh vs Incremental Pipelines DevFeed: [Full Refresh vs Incremental Pipelines]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-03-17T20:40:38Z Content type: article Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [data](), [datasets]() Tags: [data](), [incremental](), [pipelines]() ### AI overview An article about the tradeoffs between full-refresh and incremental pipelines for data teams. ### Source excerpt Tradeoffs Every Data Team Should Know ## How Layered Data Systems Create Complexity and Sprawl DevFeed: [How Layered Data Systems Create Complexity and Sprawl]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-03-02T22:56:11Z Content type: opinion Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [data](), [systems](), [Data Infrastructure](), [Development](), [databricks](), [dashboards]() Tags: [architecture](), [article](), [dashboards](), [data](), [data-infrastructure](), [databricks](), [development](), [systems]() ### AI overview This opinion article examines how data stacks accumulate layers of roles, tools, and platforms. It argues that although these layers can simplify development and speed experimentation, they can also create BI, pipeline, model, agent, cost, and system sprawl. ### Source excerpt How data stacks turn into fractals ## Backfills - The Necessary Evil of Data Engineering DevFeed: [Backfills - The Necessary Evil of Data Engineering]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-02-23T23:37:52Z Content type: tutorial Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [data-engineering](), [Data pipelines](), [data](), [datasets](), [systems]() Tags: [bug](), [data-engineering](), [data-pipelines](), [data-type](), [databases](), [engineering](), [pipelines](), [schema]() ### AI overview This article explains why data teams perform backfills and why data engineers often dislike them. It describes backfills as rerunning or rebuilding tables and pipelines to account for corrected source data, pipeline bugs, schema changes, logic changes, or required data-type conversions. ### Source excerpt Why backfills happen, why we hate them, and how to handle them without breaking trust ## 5 Key Predictions for the Data Industry in 2026 DevFeed: [5 Key Predictions for the Data Industry in 2026]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-01-31T19:29:24Z Content type: opinion Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [data](), [Microsoft](), [Artificial Intelligence](), [export](), [Azure]() Tags: [2026](), [ai](), [azure](), [data](), [export](), [microsoft](), [predictions]() ### AI overview A commentary piece offers predictions about the data industry over the next year or two, including a possible Microsoft Fabric rebrand and the continued gap between demand for AI and companies' reliance on traditional data workflows such as ERP exports to Excel, SFTP, and APIs. ### Source excerpt Hype Cycles, Rebrands, and the Messy Reality of Data ## Analytical Skills for Data Professionals: Estimation, Baselines, Root Cause Analysis, and Metrics DevFeed: [Analytical Skills for Data Professionals: Estimation, Baselines, Root Cause Analysis, and Metrics]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-01-23T16:49:29Z Content type: tutorial Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [Data Science](), [Math and Logic](), [data](), [dataset]() Tags: [analysis](), [critical-thinking](), [example](), [framework](), [how-to](), [metrics](), [root-cause-analysis]() ### AI overview This article discusses analytical skills that data professionals often develop on the job, including analytical intuition, estimation with limited information, baseline reasoning, critical thinking, root cause analysis, and selecting meaningful metrics. ### Source excerpt Estimation, Baselines, Root Cause Analysis, and Metrics That Actually Matter ## What It Actually Takes to Build a Data Pipeline System DevFeed: [What It Actually Takes to Build a Data Pipeline System]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-01-14T17:59:10Z Content type: article Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [data]() Tags: [building](), [components](), [data](), [data-pipeline](), [pipeline](), [system]() ### AI overview The article breaks down the components, tradeoffs, and practical realities of building your own data pipeline system. ### Source excerpt A breakdown of the components, tradeoffs, and realities of building your own data pipeline system ## Common Data Pipeline Patterns You'll See in the Real World DevFeed: [Common Data Pipeline Patterns You'll See in the Real World]() Original publisher: [Read original article]() Author: SeattleDataGuy Published: 2026-01-05T19:58:06Z Content type: article Language: en Sources: [SeattleDataGuy's Newsletter]() Topics: [Data pipelines]() Tags: [data](), [data-pipeline](), [patterns](), [practical](), [real-world]() ### AI overview The article provides a practical overview of the different ways data pipelines appear within real companies. ### Source excerpt A practical look at the many ways data pipelines show up inside real companies