I struggle with the phrase “everyone’s a coder now.” And I hesitate to post because I don’t want you to read this as gatekeeping. If anything, I want more people to build, but in a stronger, more functional way. Building any sort of software is incredibly empowering. Even creating a tiny tool gives folks what I call “pro-poster syndrome”, where they feel more capable and competent than ever. What was solely reserved for the most technical among us is now - at least at a basic level - becoming accessible to anyone with a few bucks a month to spare. But overwhelmingly, and especially in the last few weeks, I am getting more and more frustrated notes from developers at large companies. Yesterday, I heard about salespeople at one company asking for repo access. Earlier, a startup engineer told me his life has been hijacked by non-engineers. “All of their vibe coded apps don’t work.” I spoke with one company whose marketing and finance and partnership teams dropped the ball on their product launch tasks in favor of tinkering with Claude Code / Codex / Replit. Product and design seem to navigate this better. They’re closer to the work, and in many orgs, already have a path to contribute responsibly. Maybe this is a blip and the energy among business users will die down, but I would bet against that. Companies need to figure out how to enable AI-first problem-solving without wrecking the sanity of an entire department, turning engineering into an endless support desk, and derailing critical work in the business. The future is more builders, yes. But most companies are still missing the systems.
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This is an interesting thread. Everyone is suggesting tools to solve the problem. I’d start by asking more about the data and the questions the customer is trying to answer or problems they are trying to solve first before recommending...
Not all retries are created equal. Immediate retry: usually fails again Exponential backoff: gives systems time to recover Exponential backoff with jitter: prevents thundering herd Most orchestrators have this built in. But you need to understand what's happening or you'll wonder why your retries...
Test for superintelligence: when the data in Fivetran’s salesforce is 100% accurate and up to date at all times, I’ll know we’re there.
The semantic layer is like a restaurant menu: you know what you're ordering, but not how it's made. This analogy comes from Maxime Beauchemin and I think it's perfect. Users shouldn't need to understand your star schema to calculate revenue. They should...
You've got data spread across geographies. What happens when you want to bring that data together? Usually ETL jobs or other mechanisms. We just launched @googlecloud BigQuery global queries. Do multi-location analysis with a single query: https://t.co/F3p2mn5SjZ
Data Quality and Data Governance are two of the most underrated but important areas in the data space There are other areas to explore in data outside of Analytics.
Hot take: Pivot tables are the REPL for business data. Just like programmers use REPLs to quickly test code, business users use pivot tables to quickly test hypotheses about their data. Drag a field. See a result. Adjust. Repeat. This feedback loop is...

AI teams love tuning models. But they ignore the bike chain: data. Outsourcing labeling to people that care much less on the app’s success. Messy internal docs. No structured knowledge base. No call transcripts. No clean SOPs. Then they ask: “Why isn’t the model improving?” The highest ROI in...

I did some digging with the help of ChatGPT and Claude Here are 4 tech areas you can still explore in 2026 backed by data: • AI/ML – Data Analytics falls here • Cloud & Infrastructure • Security & Governance • Data Engineering Let me...
Working in entertainment analytics I am often asked how best to position a title for success. But data can help you aim more accurately and efficiently. What it can’t do is provide the single most important element to success: a...
I see data contracts and data quality as overlapping but different: Data contracts: what is the data and how do we enforce it Data products: why do we need this data In practice, I'd argue for asset-based data quality assertions. Every time a...
eczachly I hate Snowflake micro partitions and optimizations for a few reasons - they make data modeling lazy If you don’t have to understand the partitioning or shape of your data. You can just slap the data into Snowflake and call it a...
From Zach Wilson, three signs your pipeline isn't idempotent: 1. It uses INSERT INTO instead of INSERT OVERWRITE or MERGE 2. Date filters have "date > start" but no "date < end" - this causes exponential backfill costs 3. Source tables are always...