r/snowflake

• Enhanced dbt DAG with column-level lineage: The DAG visualization now shows columns on each model node (powered by Horizon Catalog), highlights upstream/downstream column usage on click, and adds search, depth controls, a unified side panel, and a higher model display limit of 300.
• dbt Fusion engine: A Rust-based rewrite of the dbt runtime is now supported with faster compilation for complex projects, available to all users at no extra cost.
• Multi-version dbt support: You can pin a specific dbt version (Core 1.9.4, 1.10.15, or Fusion 2.0.0-preview) per project via DBT_VERSION, override at runtime, or set an account-wide default with DEFAULT_DBT_VERSION.
• Cortex Code for dbt: Cortex Code now covers the full dbt lifecycle in Workspaces and CLI — scaffolding models, adding tests, running commands, generating docs, and inspecting deployed project files.
• Run a single dbt file in Workspaces: A run button on individual model files lets you run or build just that model without executing the entire project.
• Partial dbt project execution from the DAG: The "…" menu on a DAG node lets you run the selected model alone, its upstream parents, its downstream dependents, or all three.
• Import a dbt project object into a Workspace: You can pull the contents of an existing deployed dbt project object into a Workspace as a new folder for editing.
• SYSTEM$GET_DBT_LOG max_num_lines argument: The function now accepts an optional max_num_lines argument (default 1,000) to control how many trailing log lines are returned, capped at ~10 MB with oldest lines dropped first.

Curious if anyone here has experience building and deploying dbt-based bioanalytical/life sciences data models in Snowflake.

I’m particularly interested in learning how others have approached a few challenges:

• Incremental modeling when source systems lack reliable change tracking (e.g., no updated_timestamp, weak CDC support, limited audit metadata). How did you handle incrementals, late-arriving changes, reconciliation, or full vs hybrid refresh strategies?

• Architecting across multiple source systems, especially in environments supporting legacy-to-modern migrations. How did you model, harmonize, and reconcile data across systems while maintaining lineage, historical continuity, and a stable consumption layer?

• Life sciences / regulated environments (GxP, LIMS, clinical, bioanalytical, etc.): any lessons learned around validation, auditability, governance, semantic modeling, or balancing flexibility with compliance?

Would love to hear about patterns, tradeoffs, war stories, or things you wish you had done differently. Thanks in advance!

The new workspace functionality (that becomes mandatory on June 22nd!) does not seem to work with a reader account, it looks like it can't save workspace data to the PDB.

Anyone else hit this? Did you manage to overcome it? I can disable workspaces for now, but come June, no dice.

This on top of the "use Streamlit, not dashboards" is some piss poor customer service, IMO, I just spent two weeks trying to figure out Streamlit permissions that let people share visualizations because Snowflake said so - I really wish they'd stop forcing changes on us that are half-baked and don't provide any benefit for customers.

Hey everyone,

First off, sorry if this kind of post isn't normally allowed here, but I wanted to see if anyone would be down to team up and learn Snowflake together.

I’m completely new to Snowflake myself. I've found that picking up a new data platform is a lot easier (and honestly, way more fun) when you have a small group to bounce ideas off of and keep each other accountable.

I set up a Discord server for this. The plan is to keep it super casual and completely free—no paid courses or weird upsells, just hitting the official documentation, the free trial, and whatever free resources we can find.

Here’s the basic idea:

Low commitment: Aiming for about an hour a day, so it’s easy to fit into a normal schedule.

Go at your own pace: If you miss a day, no big deal at all. You can just grab the agenda, go through it on your own time, and catch back up whenever.

Open to everyone: It's going to be geared toward beginners like me, but if you’re already a seasoned data pro or SQL expert and just want to hang out, share some best practices, or offer a little mentorship, we’d love to have you.

Learning this stuff solo can be a grind, so if you're interested in joining the group or helping out, just drop a comment below or shoot me a DM and I'll send over the Discord invite.

Thanks!

Hi, I got an invite on LinkedIn for Dev Day (like many others, I’m sure) and am curious if it would be a good fit for me. I recently moved to the Bay Area, so it wouldn’t be anything to travel for, and it’s super dope that it’s free, but my work is mostly centered around people analytics. I’m not a developer, architect, or engineer; I know SQL decently enough, and am attempting to learn Python, but otherwise, I don’t code. I mostly clean, analyze, and build dashboards or decks dependent on the ask.
Would this be something worth going to for me? Or am I going to feel like a fish out of water and most of it go way over my head?

Thanks in advance!

tl;dr: Each sessions starts with ~25,000 tokens of system prompt overhead before the model reads your question. 56% of which is skill descriptions for tools most users will never touch.

I tried out Snowflake's AI tool, Cortex Code CLI, which was created specifically to help with data engineering and Snowflake related coding tasks. However, compared to a plain Claude Code session, it provides sub-par performance.

I've asked Cortex Code to write a Snowflake stored procedure that finds and recreates broken views (this is common issue in our environment if DDL of upstream objects is changed). What I got back was broken SQL. It tried to create a stored procedure that executes ALTER VIEW sub_view COMPILE; which is a valid command on Oracle, but not on Snowflake.

The funny thing is that it has a dedicated /sql-author skill, a /sql-verify subagent designed to catch exactly these kinds of errors, and access to Snowflake's own documentation via cortex search docs. It used none of them before it started working. 

My first instinct was to work within the system. Cortex Code has a context rule system mechanism:

cortex ctx rule add "Always check Snowflake documentation using cortex search docs before writing SQL"

It didn’t help. I quickly realized that context rules aren’t loaded by default when starting a new session, they depend on the model deciding to run cortex ctx rule list first, which is not a mandatory step.

So I added an instruction to always run cortex ctx rule list into ~/.claude/CLAUDE.md, the persistent instruction file that gets injected into every session. 

It was ignored. Not always, but often enough to be unreliable. I tweaked the wording, I restructured my CLAUDE.md. The reliability improved, but the fundamental problem remained: my instructions were not always applied, CoCo has not read the docs, and created broken SQL.

At one point I confronted Cortex Code directly about its failure, it replied:

>Context is ~800+ lines across multiple <system-reminder> blocks. Impact: Attention dilution;
Mandatory action buried in nested file contents. 

The model itself was telling me the context was too large. It even recommended: “Reduce context noise. Many system reminders repeat or overlap.” 

Cortex Code source code is not available, so getting the actual system prompt was a bit tricky, but I succeeded:

Skill description 56%, Tool schemas 29%, Fake system reminder messages 15%

I only typed 4 characters. The model received ~25 700 tokens of context.

More than half the context is consumed by skill descriptions: verbose paragraphs explaining 68 bundled skills, most of which any given user will never touch.

My CLAUDE.md directive to "always check Snowflake docs before writing SQL" was competing with 17 system-reminder blocks, 32 tool definitions, and 60+ skill descriptions. The model's attention to any single instruction drops as the total volume increases. 

That's not a model quality problem. It's a context design problem. And it directly explains the hallucinated SQL syntax I kept running into.

Happy to discuss the technical findings. Criticism welcome, especially if you've seen different behavior.

original article

Hello All,

I have experience in BI reporting across multiple tools and technologies. I recently started learning Snowflake through Tom Bailey’s Udemy course and have been preparing for the certification for the past two months.

I feel reasonably prepared conceptually, but I am a bit unsure about the actual certification exam pattern and preparation approach, as I currently do not have access to good mock exams or practice question sets.

Could you please suggest:

Reliable and economical sources for mock tests or practice questions

Any useful dumps/question banks for reference and learning purposes

The best way to practice exam-oriented questions effectively

Preparation strategies that helped you clear the certification in the first attempt

- Udemy practice test are enough to clear exam?

Your guidance and recommendations would be greatly appreciated. Thanks in advance!

Hi all,

I’ve been lurking in this sub for a while, and the previous answers were really helpful during my prep. I wanted to share my study tips and what I did.

Just to be clear, I took COF-C02, not C03, but the underlying principles should still apply. You just have to focus more on Snowflake Cortex AI for O3.

I started with Tom Bailey’s course, but I could not complete it because I started to lose focus. Then my friend gave me the idea to get transcripts of the videos section-wise, organize them into folders, and ask Claude CLI to prepare notes for each topic according to the transcript.

This gave me an insane advantage. First, I didn’t miss any content that was spoken in the video but not covered in the slides. Tom Bailey says many things that are either not in the slides or are only briefly mentioned. I am not saying his slides are bad, but they are structured in a way that is more surface-level and skims through topics.

The advantage of taking notes this way is that you can open a topic in VS Code, open Claude CLI or any assistant, and ask doubts as you study. You can also add those doubts to your notes, prepare quizzes, and create key facts that you need to remember. I also prepared a Quizlet quiz from the notes. I took those quizzes after each section, then created another set of quizzes for each section and took them all at once. This helped me identify topics that I thought I understood but actually needed to review more.

I studied for about a month. Initially, I prepped around 10–20 hours per week, then increased it to about 30 hours during the week before the exam.

After the second set of quizzes, I revised the notes. Then I started with Skillset Pro questions and took all 28 quizzes. You should be able to score above 80–85%, and in some exams, you should be able to get 90%+.

Then I took Chris Garcia’s mock exams. I got only 70% on the first one, which really worsened my mood. But I realized the mock exam was too hard, so I stopped worrying about the scores and treated them as practice. Once I shifted my mindset, I started taking the rest of the five quizzes, and my scores improved from around 70% to 84% on the final quiz.

A couple of days before the exam, I went through all the Skillset Pro and Chris Garcia quizzes. Don’t redo the quizzes; go through them. I noted important points in a notes app. I used Notion and planned to revise it later, but I didn't get a chance. Then I just revised the notes at a surface level before the exam.

I don’t study anything right before the exam because I don’t believe in last-minute revision. So I chilled for half a day and then took my exam.

I also forgot to add one more thing: I took the Snowflake mock exam. I got it for free since my company is a Snowflake partner. This helped me assess the platform and understand how the questions would be structured.

As for the actual exam, some questions felt like pure BS, at least according to me. For example, one question asked what gets created when connecting with Spark for the first time. I had no idea about it.

A few questions were straightforward. For the rest, I was usually able to eliminate two options and then choose between the remaining two. I sped through the questions and completed the first pass in about 40 minutes. Then I took another 40 minutes for a second pass to review the rejected options and my second-choice answers.

Thanks to this community, I was able to choose Tom Bailey’s course, Skillset Pro exams, and Chris Garcia’s exams.

Hey everyone,

I recently finished Tom Bailey’s udemy course for the snowflake snowpro core certification. I feel pretty confident with the overall concepts and understanding of Snowflake, but I’m not at the same confidence level when it comes to remembering things like privileges, commands, syntax and other small details that need memorization.

Because of that, I’m still not fully sure whether I’m actually ready for the real exam or not.

For those who have already taken the exam, was Tom Bailey’s course enough for you or did you use other resources as well? I’d also like to know how similar the actual exam is compared to the course and whether the questions are more scenario based or theory focused.

Would really appreciate some honest feedback before I schedule the exam. Thanks!

Hi everyone,

I’ve been working with Snowflake for the last 4 years, but I’ll admit I was always a bit intimidated by the SnowPro Core exam. I finally took the plunge and passed today with a score of 862 after a focused 10-day study sprint!

How I used AI to prepare: Since I use VS Code daily, I leveraged GitHub Copilot as a personalized tutor. It was incredibly helpful for:

• Generating mock questions: I asked it to quiz me on the toughest Snowflake concepts.
• Topic summaries: It provided concise pointers on specific topics.
• Explaining nuances: When I was stuck on specific architectural details, Copilot helped break them down into digestible bites.

For those of you sitting on the fence—don't wait 4 years like I did! If you have hands-on experience, a structured 30-day plan and a good AI "study buddy" can definitely get you over the finish line.

Happy to answer any questions about the exam or my study process!

Hi everyone, I have written another article with regards to the new COF-C03 version of the SnowPro Core exam, with what's new/changed and how to maybe modify your preps or start fresh.

Hope it helps! Do provide feedback, helps me improve future articles, and contribution links in the article 😃

https://medium.com/@nikskamath/snowpro-core-is-getting-an-upgrade-heres-everything-that-s-new-in-cof-c03-7e53eecc8184

Hi all,
I'm stuck with a client's request.
Context: Ingestion of CSV files from a S3 bucket to a table in Snowflake. In addition, I need to add three columns, one with the current date, one with the name of the csv file, and one with the date retrieved from the csv file.
Issue: The client told me that the structure of the files could change in future, for example by adding one or more columns to the structure.
I already create a table with the desired columns and it works at the moment. In the future, some columns might be added:

• If they're added at the end, it stills work.
• But if they're added in the middle of the structure or in random positions, all the data will be shifted.

I tried with MATCH_BY_COLUMN_NAME but due to the three new columns that i have to create (cause in two of them I use metadata$filename), it doesn't work.
I also tried with schema evolution, but I obtain the same result as above.
Do you know if there is a solution to these problems or if you know some similar cases that have been resolved?
thank you :)

Hi Reddit,

I am new to this community please guide me where can I cross-post this as well

Please help me with knowing how to get free pass for Snowflake summit 2026. I am really interested in joining the summit this year in-person but don't know how to?!

TIA!

Snowflake's new Workspaces UI is a step backward — what's broken:

1. SQL compilation error :"Object does not exist" or operation cannot be performed error appears on hover in the Database Explorer, even for tables that exist and query fine

2. No "Last Modified" time on hovering over tables

3. Can't view stored procedure definitions like before — now forced to either run GET DDL() or open Edit Stored Proc just to see the body

Classic Worksheets handled all of this fine. Anyone else hitting these? Any workarounds?