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LatestTopDiscussions
Do You Actually Need Real-Time Data?
Why “real-time” is often a business requirement that hasn’t been defined yet
Sep 25 • SeattleDataGuy
37
8
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Engineers Are Obsessed With The How And Often Forget Why?
Why data teams need to spend less time debating the stack and more time questioning the work
Sep 5 • SeattleDataGuy
43
6
4
From Vibe Coding to the AI Software Factory
How I’ve Seen Engineering Teams Use LLMs Over The Past Few Years
Aug 26 • SeattleDataGuy
32
2
3
The Problem With Asking Employees to “Take Ownership”
And then punishing their initiative
Aug 10 • SeattleDataGuy
61
3
8
Your Data Warehouse Isn’t Integrated Just Because the Tables Are in One Place
Centralization is not integration!
Jul 18 • SeattleDataGuy
61
2
2
Forward Deployed Engineering Is About To Get Diluted
But AI vendors need enterprise adoption at any cost
Jul 6 • SeattleDataGuy
61
3
4
In 2026 The Data Fundamentals Matter More Than Ever
We are headed towards a massive data mess otherwise
Jun 13 • SeattleDataGuy
82
1
4
If AI Can Replace Workers, Why Is It Hiring Consultants?
Last week, I noticed that Anthropic was hiring for a partner success manager.
May 26 • SeattleDataGuy
53
4
What Leading a Data Team Actually Looks Like Right Now
Even with AI
May 14 • SeattleDataGuy
61
1
5
The 5 Silent Failures in Data Pipelines
How Your Pipelines Lie to You Without Throwing a Single Error
Apr 24 • SeattleDataGuy
68
5
4
Data Pipeline Foundations - Everything You Need To Know About Data Pipelines
Hi, fellow future and current Data Leaders; Ben here 👋
Apr 18 • SeattleDataGuy
107
1
6
Daily Tasks With Data Pipelines - Data Quality Checks And The Problem With Noisy Checks
Every morning, your team wakes up to 137 data quality alerts.
Apr 7 • SeattleDataGuy
55
2
4
You Will Know Nothing And Be Happy
It’s 2030, and your boss just asked you to pull data to help better segment your users and understand their behaviors.
Mar 25 • SeattleDataGuy
77
6
6
Full Refresh vs Incremental Pipelines
Tradeoffs Every Data Team Should Know
Mar 17 • SeattleDataGuy
72
4
6
Layer by Layer, We Built Data Systems No One Understands
How data stacks turn into fractals
Mar 2 • SeattleDataGuy
68
4
4
Backfills - The Necessary Evil of Data Engineering
Why backfills happen, why we hate them, and how to handle them without breaking trust
Feb 23 • SeattleDataGuy
97
4
9
Why Data Pipelines Exist
Beyond Moving Data From Point A To B
Feb 9 • SeattleDataGuy
77
4
9
5 Key Predictions for the Data Industry in 2026
Hype Cycles, Rebrands, and the Messy Reality of Data
Jan 31 • SeattleDataGuy
68
11
4
The Analytical Skills No One Teaches You
Estimation, Baselines, Root Cause Analysis, and Metrics That Actually Matter
Jan 23 • SeattleDataGuy and Olga Berezovsky
183
5
24
What It Actually Takes to Build a Data Pipeline System
A breakdown of the components, tradeoffs, and realities of building your own data pipeline system
Jan 14 • SeattleDataGuy
65
3
9
Common Data Pipeline Patterns You’ll See in the Real World
A practical look at the many ways data pipelines show up inside real companies
Jan 5 • SeattleDataGuy
86
3
8
Snowflake vs Databricks Is the Wrong Debate
Winning the Data Stack Role by Role
Dec 12, 2025 • SeattleDataGuy
54
2
4
How I Run System Design Interviews for Data Engineers
Why System Design Still Matters
Dec 9, 2025 • SeattleDataGuy and mehdio
129
4
12
Translating Data Buzzwords Into Real Requirements
Bridging the Communication Gap Between Data and the Business
Dec 2, 2025 • SeattleDataGuy
49
2
8
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