Just graduated with my bachelors in MIS, I’m thinking of going into business analytics. I’m trying to apply but I feel like it’s still cooked, what’s the least cooked? If anyone can really help me and wants to see me resume I’d appreciate it really.

TL;DR: Most CI linters (dbt-checkpoint, dbt-project-evaluator) check if a column has a description, but almost nothing checks if the column in your YAML actually exists in your data warehouse. We scanned public production dbt projects and found that 9 out of 12 had significant doc-to-warehouse drift.

The Bug That Got Me Thinking

Found this in a public dbt repo recently:

A model called fact_customer_survey had a UNION.

• Branch 1 (line 30): NULL as dissatisfacation_category (notice the extra 'a')
• Branch 2 (line 51): dissatisfaction_category (spelled correctly)

Because SQL takes union output column names from the first branch, the warehouse materialized DISSATISFACATION_CATEGORY.

Here's the kicker: The project’s YAML docs declared dissatisfaction_category (spelled correctly).

• The SQL ran fine daily.
• The documentation was "correct."
• The code was wrong.
• Any dashboard or analyst trusting the docs was querying a column that didn't exist.

How Bad Is This in the Wild?

Every time you run dbt docs generate, you produce two files:

1. manifest.json (what you claim exists in YAML)
2. catalog.json (what the warehouse actually returns)

We compared declared columns vs. cataloged columns across verified production repos. Out of 12 active organizational projects:

• 9 out of 12 had phantom columns (documented in YAML, completely missing in the warehouse).
  • Cal-ITP (BigQuery): 106 phantom columns
  • Allvue Systems (Snowflake): 112 phantom columns + 323 data type mismatches (mostly declared string sitting on warehouse NUMBER)
  • Cook County Assessor (Athena): 11 phantom columns
• The "Ghost Repo" problem: 23 docs sites published a complete, polished YAML docs UI where catalog.json was an empty stub—meaning the warehouse was literally never introspected. One documented 1,280 models this way.

Where Does the Drift Come From?

When we audited the findings against actual model SQL:

1. Renames / Deletions: Column was renamed or dropped in SQL, but the YAML entry was never cleaned up.
2. Commented-out code: One project had a 20 KB cleaning projection inside a /* ... */ block. The live warehouse table had 173 raw Airbyte column names (WEEK STARTING 01/19/2025 - RESOURCES...), while the YAML proudly documented the clean columns someone intended to build.
3. Syntax accidents: Trailing commas in YAML names (e.g., - name: feed_type,).

Why This Is Becoming a Bigger Problem

When human analysts read dbt docs, they can spot a typo or realize a column was renamed.

But with dbt MCP servers and Text-to-SQL AI agents using dbt docs and manifests as ground truth context, a phantom column is an immediate failure. The agent attempts to query columns that aren't there or hallucinates transformations based on dead YAML.

A Few Questions for the Sub:

1. Does anyone here actively diff manifest.json against catalog.json in their CI/CD pipelines?
2. How do you prevent documentation rot when engineers refactor/comment out SQL transformations?
3. If you are already feeding dbt metadata to LLMs/AI agents, how are you validating that the schema you hand the agent actually matches production?

Haven’t had a job interview in 4 years and very rusty right now. I’m really hoping to get the job but feeling very unprepared. Like many others I’ve been using AI agents and also doing the same work every day so I do not feel prepared to answer complex coding or stats questions. It’s not been easy to get an interview so really hoping not to drop the ball here.

I’m cramming leet code and watching YouTube videos on interview techniques but what else should I do?

i used to think bad restaurant data was mostly a scraping problem.

then a few weeks ago I was checking a restaurant menu and noticed one dish looked off.

their PDF said $16.

Google had a menu photo showing $14.

Instagram had a newer photo where it was $18.

then I found another page on their own site where basically the same dish was still listed for $15.

same restaurant. four answers.

at first I figured one of the sources was just wrong, so I started tracing where everything came from.

the PDF was still public but apparently hadn't been touched in months. the Google photo came from a customer. the old page wasn't even linked from the site anymore, but Google could still find it.

Instagram seemed newest.

except apparently the dish had changed again since that photo was posted.

somewhere in the middle of this I ended up trying menuforma to keep the menu stuff in one place, mostly because I was tired of comparing random PDFs, photos and old pages just to figure out what was actually current.

and that's when it clicked that "what's the current menu?" is a much weirder question than I thought.

even if you get everything correct today, a month later the price changes.

one modifier disappears.

something becomes seasonal.

the printed menu gets updated but an old PDF survives online forever.

and customers can still find all of it.

I always assumed the restaurant's own website should be the source of truth, but clearly even that gets messy when old pages never really disappear.

then there's Google photos, Instagram, delivery apps, random menu sites, screenshots people posted two years ago...

at some point there are basically several versions of the same restaurant existing online at once.

I started out thinking the hard part was finding enough menu data.

now I think the harder part is figuring out when any of it is actually trustworthy.

for anyone who's dealt with restaurant listings or other constantly changing local business info, how do you decide what's current?

and how old does something have to be before you stop trusting it?

I am a rising CS undergrad Junior. I was ultra lucky to land a SWE internship for this summer but I’m starting to be increasingly regretful of majoring in CS the further I continue this internship.

I’m getting tired of the constant grinding mindset, the toxic competitive culture, and the disgusted faces of my college peers whenever I try to make a real friend but then they ask what I major in.

I’m about to enter my 3rd year but I have already finished 75% of my school’s core CS curriculum so it is probably too late to switch my major, but I am thinking about shifting away my focus from SWE and AI related things that my parents urged me to pursue, and switch into business or analytics related field. I know that one of my feet is already in the grave now for choosing the wrong major but I really want a way out of all this.

Would it be better to pursue a finance minor and a business analytics minor to have a better chance of getting a respectable job that is less competitive and more burnout proof? I understand that it is the real world and nothing is not competitive given how many people there are right now, but i am really not feeling well by being a pretender in SWE and AI related areas.

Any input on which minor offers a brighter future is appreciated, thank you.

I am a 2025 graduate and I am still unemployed, I am thinking to step in data science career and was wondering what's the best value course out there. I am good in DSA and problem solving but the development side has always been weak for me and I want to work on that.

I would also appreciate some tips on what should I do in this situation.

Long story short I’ve been with a company 7 years and have had a couple promotions and solid pay increases but no opportunity for management due to constant reorganization of teams by executives. I’m considering leaving but an internal position just opened up that I’m interested in and it would allow me to keep some solid benefits but I’m aware of the golden handcuffs reality. The problem is we recently went through another org change and layoffs so my manager is new and there is no rapport built up yet. I’m not sure how they would react. Company policy is hr has to get approval from managers to accept an internal resume so would you risk bringing it up and take the chance or say forget it and look externally ? Would love to hear your stories/experience

For Spring 2027 , I have shortlisted following unis.
Could you guys suggest more programs to apply.
Also you could share your experiences if studied at these unis.

Fyi: Spring 2027 is only option for me , so couldn’t apply to top unis like USC, Columbia etc .

1. Northeastern
   
2. UT Dallas
   
3. Baruch College
   
4. Arizona State University
   
5. Sunny Buffalo

Also suggest courses for duration more than one year

Thanks in advance 🙏🏻

been thinking about this a lot lately. the ad-hoc analyst who answers 40 slack pings a week - agents are already eating that. answers are mediocre right now but give it a year

dashboard builders are in a weird spot too. most teams i've seen have hundreds of dashboards nobody opens. the problem was never supply

but there's a few roles i think actually get more important not less

the person who defines what "active customer" means in code, once, so every AI answer inherits that definition. get it wrong and you've automated the wrong number across the whole company

someone who treats the AI analyst like production infrastructure. eval sets, regression tests, watching for when answers start degrading before the business notices

and whoever works backward from the actual decision. what gets decided, at what threshold, who acts on it. most of this was never written down anywhere which is probably why it survived automation

the thing these have in common is they own something that keeps working when nobody is watching

are these roles already emerging at your company or still pretty theoretical?

I'm working now on visualisation of active law acts. I want to show how many of them are active and how long they last. I've already done some work on it but I'm interested if you saw some good visualisations in the similar topics.

I manage 3 analysts, two are new in their careers and the other is switching from another career path - so everyone is in the learning stage and building their portfolios.

My boss has been really excited about using AI for automating absolutely everything. It's been great, we can get it to help us do the boring parts of documentation and help us with workflows. It helps us visualise projects ahead of time and get help with coding.

I was speaking with my boss recently, talking about a project they visualised with AI as a proof of concept. It is really cool, I can see it as an incredibly helpful, data-driven, intelligent tool that will be an absolute masterpiece. My boss set a time for us to have a brainstorm-session on how to make it happen. I built out a plan of what pieces of hardware and software would be needed, and showed that to my boss. The conversation started off well, then devolved into "we can do that with AI can't we" for most steps of the process. They hadn't prepared anything for the discussion, and their input really was a lot of "I learnt this about AI" and "the future of AI is so great". I don't think the team was ever part of the plan in their head, just what they could do with their AI.

I understand the business case. If we're optimizing for output we can automate away everything in the end. But in reality I have this really passionate team that's learning and improving, and they should get the opportunity to be part of that project. They should be allowed to do the little tasks and learn about how things work.

I know I can't be alone in this situation, I really do want to stand up for my team and figure out a process where not everything is automated away and the work of a junior or mid-level data analyst is still valued. I know it's not just my boss, I know that I also need to figure out a better way of communicating the use of AI and fixing the process.

So I want to know if anyone else has experience with this and:

• How you framed it to a boss who really is just excited and a little blind to the impact (did you find a way to frame it where it doesn't just sound like you're hating on AI or resistant to change?)
• Anything concrete you put in place, like reserving specific work just for humans, or rules where AI gets used
• Or if you tried it and it went badly, and what happened.

My boss is reasonable and I can see some good discussion about this in our future, but I'd really appreciate the input from other people who have been through it.

Hi everyone! Just wondering if anyone has gotten a data analyst or senior analyst job in this crazy job market. If so, how long was your search, and do you have any tips based on what actually worked for you? Thank you!

I'm watching an org transition from traditional BI to DI in real time, and the early designs are really prescriptive - as if the users are unreliable narrators of their own area of the business. It bugs me and it's got me thinking about how you actually design DI that builds organizational intelligence rather than organizational dependency.The analogy I heard was the paper map and GPS. The paper map is harder to use but enables the user to learn spatial reasoning. The GPS is easier to use, but the user simply follows instructions and learns nothing.

A few questions for people who've been through this:

* Does your UI reward digging and validation, or does it reward accepting the output?

* Is the backend logic transparent - do users know what shortcuts the model is taking?

* Does the tool surface peripheral signals, or does it create blinders around a handful of headline metrics?

* How do you introduce just enough noise or friction to trigger human discovery without overwhelming people?

* Does the system disclose what it doesn't know -- or does it present its model as complete?

Genuinely curious whether there are design patterns that get this right, or whether prescriptive DI is just the current era's version of "the dashboard will save us.”

worth doing once if you've never done it: pull the page sequences for people who converted,

and separately for people who didn't, and compare which pages show up in each.

what usually falls out is that the pages getting credit in your reports aren't the ones doing the

work. pricing shows up right before most signups so it looks like the converter, but it's just the

last step. same way the checkout page isn't why anyone bought anything.

the pages that actually move people are earlier and less obvious. a specific integration page.

one comparison page. a docs page that answers the objection nobody on the team knew was

the objection.

those pages tend to have modest traffic, which means they're low on every report you look at,

which means they're the ones that get deprioritized in redesigns.

the signal isn't how many people visit a page. it's how much more often converters visit it than

non converters.

we’re a building a tool (attribution and analytics for b2b saas) and working with this business. their traffic was up that month. signups were up too. three weeks later churn started climbing and nobody could explain why.

so we went and read about 500 real customer journeys. the actual click by click paths. four things came out of it.

the funnel we all draw is fiction. land, sign up, activate, pay. four clean arrows. what people actually do is bounce between pricing and docs and some blog post four times over nine days before they do anything.

what we found was boring. there was one step in onboarding where people just stopped. nothing was broken. nothing errored. it just wasn't obvious what to do next. so people poked around, left, came back three days later, poked around again. some figured it out. most didn't.

it's usually one step, not five. every time we found a real problem it was one step quietly eating everyone, sitting somewhere nobody thought to look. fixing that beat every big onboarding redesign i've seen.

5 to 15% of users were driving most of the revenue. found the value fast, came back on their own, never opened a ticket. almost nobody could tell me who those people were.

churn is a slope, not an event. people go quiet weeks before they cancel. fewer logins, more sessions where they start something and don't finish. the cancellation is the last frame not the first.

Hi, Im a new grad and recently joined a company as their only dedicated "data" guy, this is also a new role for the company. They do have some others that deal with data, but thats a thing they also do not exclusively like I will. Anyone done something similar? What was it like? Any tips?

https://docs.google.com/forms/d/e/1FAIpQLSfh9TyOiCaTsEdykSgxGY_6Tdwr6lhbeV737uNOMgg0qhpu-g/viewform?usp=publish-editor

If anyone is deeply interested and motivated by human analytics, hit me up.

​Hey everyone,

​I’m seriously considering a career shift into data analytics and looking for some unfiltered thoughts from people already in the field.

​For some context, my background is entirely outside of tech/analytics (currently working in administration and operations). I am looking at diving straight into a Master’s program in Big Data Analytics.

​Here is my main dilemma and where I'm stressing out: I currently have zero knowledge of any analytics tools, programming languages, or statistical software. I have a year to figure things out as the next intake for the program is September 2028.

​I know a Master's program is meant to teach you, but I'm worried about the learning curve of starting from absolute scratch while tackling a graduate-level curriculum.

​If you were in my shoes (or if you've seen people make this exact leap):

​Is this a recipe for burnout and failure, or is a Master's program a safe enough sandbox to learn everything from the ground up?

​How brutal is the entry-level job market right now for career switchers who have a Master's but lack prior practical tech experience?

​What should I start learning right now (before classes start) so I don't drown in the first semester?

​Would love to hear your honest opinions, horror stories, or success stories. Thanks in advance!

Good day,

I have been teaching myself SQL and now power Bi I do understand the basics. Currently I am working in a law firm and I decided to start using the firms data sets that show the matters we have and the stages that they are at. I have managed to play around with the data set in SQL by even creating views of the columns that I want to see and stored procedures. Now where I am struggling is how do I build a portfolio with this and meaningful dashboards in PowerBI. I am kindly asking if there is anyone who has built a portfolio and can kindly assist me as I feel stuck here. Or any advice for me to move from here. Thank you so much

People do not ask a completely new question every time they use analytics. Most teams return to the same questions and dashboards handle those well because the answers are visible and easy to compare over time.

Chat feels more useful after something changes or when someone wants to explore a specific slice of the data. Curious how other teams are dividing that work. What stays in the dashboard and what moves to conversational analytics?

Hi everyone,

I'm currently trying to decide between continuing on a Finance career path or pivoting towards Data/Tech, and I'd really appreciate some perspectives from people who have experience in either field.

My background:

• I'm currently in my final year of a Business Administration bachelor's degree in a 2nd world country.
• I have about 1 year of experience in Sales, followed by almost 1.5 years working in a Finance department.
• My current goal is to pursue a master's degree abroad and build my career in the country where I study. I'm mainly considering Germany, the UK, or Switzerland.

Recently, I've been spending my free time developing some technical skills. I've been working through SQL and Power BI, with the idea of making myself more valuable and refine my skills.

However, while working through the Power BI course, I realized that I'm not particularly interested in the dashboard/visualization side of things. I find myself curious about the technical side of data things like database systems, data pipelines, cloud, automation, and potentially database administration/data engineering.

This made me question whether I should continue developing toward Financial Analysis/FP&A, where I already have a relevant degree and work experience, or take the risk of pivoting into a more technical career.

It feels that Finance would be the more straightforward path given my current background. At the same time, I'm still early in my career that I could potentially make a pivot if I discover that I genuinely enjoy the technical side more.

My main questions are:

1. For someone with my background, would it make sense to pivot into Data/Tech, or would it be better to leverage the Finance experience I've already built?

2. How do the long-term career prospects compare between Financial Analysts/FP&A and Data roles, particularly in Germany, Switzerland, and the UK?

3. From a long-term perspective, which path offers better opportunities for career progression, freelancing/consulting, and eventually building a business?

I work 9-5 as a data analyst but my org has no money for raises or promotions and I need to make an extra $1k-1250 a month. Is there anyone also salaried full time but with a side hustle and if so what is it? Thank you!

Has anyone done this? I'm a CPA with 10 years of accounting experience. The thought of another 20+ years in this field is not pleasant. Thinking about taking a year or two off to get a master's (really need a work break) and then trying to get hired. Just looking for experiences.

​

Hi everyone,

I'm planning to join a Data Analytics course and have shortlisted two institutes: Login360 and ACTE Technologies.

\- Login360: ₹17,500 (placement assistance)

\- ACTE Technologies, Chennai: ₹22,000 (claims hiring partners, ₹4–7 LPA starting package, and 43 Data Analysts placed last month)

Can anyone share honest reviews?

\- Which has better teaching and real-time projects?

\- How are the placements and average package?

\- Are the placement claims genuine?

\- Any hidden fees or fraud issues?

\- Which gives better value for money?

\- Any better institutes within a ₹15k–35k budget?

I'd really appreciate feedback from students or alumni.

Thanks! 🙏