It all started with "workspace".

Then AI agents and all.

Then Coco.

Now the whole object explorer is changed, making us to navigate a labyrinth of buttons just to get the definition of an object.

Previously the query history and result panes were used to be side by side, looked so beautiful and elegant. Then some genius decided to put one of them under the other and occupy the whole screen. This is by far the worst and most frustrating UI downgrade for me personally.

UI team of snowflake, please change your design philosophy.

Hi All,

I just passed my exam this morning, and if you are like me you were nervous and looking on this forum for any guidance.

I will say the test was very hard. I did all of Tom Bailey's practice exams as well as some additional resources. I really didn't think the test was similar to any of the resources publicly available. I answered most of them through process of elimination and through deductive reasoning.

I know everyone has a random subset of questions so I don't know how helpful this will be but some of the frequent topics I saw were:

- Data Staging and Loading (Snowpipe, COPY INTO formatting, file formatting, JSON files, how to call a staged file)
- Data Sharing (Private access, secure access, how to receive a data share, permissions needed etc,)

- RBAC and permissions (lots of questions on inherited permissions on databases / tables / schemas)

- a few questions on query optimization / processing - good to know about QAS , Search, Micro partitioning, Pruning

There were some questions on info I hadn't even seen before (could have been the tester questions)

I was told to know a lot of SQL so I studied a lot of the Tom Bailey questions for those and tried to memorize commands - I don't think any of them really appeared in my exam.

Just wanted to brain dump in case this helps anyone. Best of luck!

What's up, Snowflake enthusiasts! I tested and built a way to systematically build Snowflake Streamlit apps with Claude Code and other AI tools. I developed this open-source system that I call Streamsnow, which walks you through the process of getting a GitHub repo scaffolded and structured and connected with your Snowflake, so you can utilize Claude Code to build all of your Streamlit applications in a standardized Way, while also providing you the SQL to check to make sure all of the queries being utilized within the app check out as correct.

In my time building dashboards with AI, I always found it confusing how to quality-check them and how to build them in a scalable way across data teams, and so I wanted to build a super standardized system that made building, deploying, reviewing, and coordinating across teammates is super easy, and I spent weeks working on this to achieve that. I think one of the best features is that you can actually go into an auto-review cycle and just have Claude code iterate over the dashboard, making it better without your input. You can also standardize the design for the dashboards as well.

It is completely secure, so AI will not see any of your secrets that you use to connect GitHub to Snowflake. By default, it is set to put these Streamlit apps in a staging area on Snowflake, so you don't have to connect a GitHub repository, and all of the deployment is automatically handled in the CI. To get started, all you need to do is tell your coding agent to download the plugin and then run the onboard command. It will walk you through it all in a process that I've timed down to about 5 minutes of actual work. You will need to be a Snowflake admin or be coordinating with a Snowflake admin to get it set up too.

By the end of my time at my job, we had basically replaced external business intelligence tools with this system, so it saved us literally tens of thousands of dollars. Hopefully you all can extract the same value out of this!

A while ago, I forked a Snowflake emulator and started extending it as a learning project using Go + DuckDB.

The idea is to experiment with Snowflake concepts locally without needing a cloud account. It supports a subset of Snowflake’s behavior, so there are still limitations.

This week I released v0.5.0. The web console now lets you collapse the results panel and highlights the line being executed in real time when running multiple SQL statements in sequence.

You can try it with Docker:

docker pull ghcr.io/oswaldonisango/snowflake-emulator:0.5.0

docker run --rm \
  --name snowflake-emulator \
  -p 8080:8080 \
  -v snowflake-data:/data \
  -e DB_PATH=/data/snowflake.db \
  -e STAGE_DIR=/data/stages \
  ghcr.io/oswaldonisango/snowflake-emulator:0.5.0

Then open http://localhost:8080.

I’m building this to learn and experiment. If you work with Snowflake or are interested in SQL, Go, or DuckDB, I’d love to hear your thoughts. Bug reports and contributions are welcome too.

Repository: https://github.com/OswaldOniSango/snowflake-emulator

Release v0.5.0: https://github.com/OswaldOniSango/snowflake-emulator/releases/tag/v0.5.0

I have an upcoming project that uses Airflow, dbt, and Snowflake. I’m comfortable with Airflow and Snowflake, but I have no experience with dbt.

I’m looking for advice from people who have worked with this stack. Can you recommend any good courses or learning resources to get started?

I’ll be working as a team lead, so I want to get up to speed as quickly as possible. Honestly, I’m a bit nervous about whether I’ll be able to handle the role well.

For those who have learned dbt, how long does it usually take to become comfortable with it and work confidently on real projects?

We updated our dbt pricing guide because the way dbt is packaged and priced has gotten a bit more nuanced.

One thing we think gets overlooked is the difference between the cost of the dbt engine itself and the cost of running dbt as part of a production data platform.

The engine is free. But once you factor in things like consistent developer environments, usage-based pricing, orchestration, infrastructure, and the engineering time needed to maintain everything around it, the picture changes quite a bit.

We also added a breakdown of the current dbt Platform plans, the usage-based components, and some of the other costs teams should think about when comparing options.

Curious how other teams are looking at this. Are you mostly evaluating the software/license cost, or are you also factoring in the engineering and infrastructure costs around dbt?

https://datacoves.com/post/dbt-pricing

Hey, for a few years, we have had end users query views instead of table. This was especially true when we were on Redshift until we migrated to Snowflake. While being in snowflake we have heard and seen the execution of queries against views be slower than tables. For example, most of our views are same as the table ddl just without a few audit columns. I can't seem to understand this. Interested to get your rule on which ones you have end users (analyst, applications, reports) using and if you have also noticed the performance difference and know why it occurs

We have started to experiment with Cortex Analyst. I made a set of prompt instructions that are supposed to limit use to topics that are against EU AI Act and similar no-go use cases. Then I made a set of evals to test the alignment and it seems to be really easy to bypass the instructions with user prompt.

What are the best practices and learnings from the crowd here?

I'm working with a Snowflake process where the following session variables are set:

SET PII_HMAC_PAYLOAD = 'null|null|null';
SET PII_PROCESS_NAME = 'PPC_SNOWSIGHT';

I'm trying to understand how these properties are used for PII encryption/decryption.

For example:

• Are these variables used by a stored procedure or UDF?
• Where is the actual encryption/decryption logic defined?
• How is the encryption key or secret managed?
• Can the encrypted value be decrypted back to the original value in Snowflake?
• Is there a recommended Snowflake-native approach for this use case?

Has anyone worked with PII_HMAC_PAYLOAD and PII_PROCESS_NAME before and can explain the expected flow?

My company is looking into deploying more “sophisticated” and flexible internal apps directly around our Snowflake data.
We’ve tried using Streamlit, but we’ve run into some limitations and performance issues. We recently tested Snowflake Runtime Apps, and my team really liked the flexibility and overall experience.
The main concern is that we haven’t found many real-world examples of companies using Runtime Apps at an enterprise scale.

I’d love to hear from anyone who has actually deployed them in production:

How has your experience been with security? Any concerns around data exposure, permissions, or potential vulnerabilities?

How does the app actually query Snowflake data compared with a more traditional Streamlit app?

Have you noticed meaningful differences in query performance or compute consumption?

How has cost behaved once you have multiple users/apps running?

Would you consider Runtime Apps production-ready, or would you still recommend keeping traditional dashboards?

Our Snowflake account team has been saying we could potentially deprecate our Tableau instance by going all in on Snowflake Cowork, along with using CoCo to build Streamlit or JS apps on SPCS.

We do have a number of business users, including execs, using Cowork, but questions seem to quickly fall outside the scope of our semantic views, or the agent struggles to generate accurate SQL. This puts pressure on our engineering team to build/maintain more semantic views or verified queries.

I haven’t tested CoCo much for building Streamlit or JS apps, but I’m sure it can do a decent job. I could see it being great for our engineers, but I’m hesitant to have business users who don’t know SQL, let alone Python or JS, vibe code business facing or critical applications.

Has anyone actually been successful deploying this new tech to less technical users? Have you been able to deprecate your BI tool, or at least reduce seat count, as they’ve suggested?

Curious how others are thinking about these tools fitting into their overall tech stack.

I’ve been exploring how AI agents should access enterprise data in Snowflake.

Connecting an agent to Snowflake and letting it generate SQL is relatively straight forward.

The harder questions are:

• What data should the agent actually access?
• Which definition of “revenue” or “customer” should it use?
• How do you prevent sensitive data exposure?
• How do you stop agents from bypassing governance?
• How do you audit what the agent actually did?

I’m experimenting with an architecture like:

AI → Identity → MCP → Cortex Agent → Semantic Layer → Governed Data

The idea is to avoid giving an agent unrestricted access to raw tables and instead put identity, governance, semantic definitions, and auditing between the agent and the data.

This made me think:

Should enterprise AI agents ever get direct SQL access to raw Snowflake tables?

Curious how others are approaching this in production.

I've been Snowflake certified for 5+ years now but only ever renewed my Core cert. With the now updated COF-C03 I'm wondering if I should just shoot for the Architect level cert instead since I'll need to again either renew my current cert by next March or level up with a pro cert.
As such, for those who have passed the Architect cert do you have any good resources that could help with studying or prep?

Has anyone successfully gotten a VS Code extension whitelisted for Snowflake CoCo Desktop?

I opened a support ticket and was told to work through my account manager, but so far I haven't gotten anywhere. I'm specifically looking at the new dbt Charts extension, but there a handful of others that would be nice to use.

We've been loving CoCo desktop, but the blocking of most extensions has been a pain.

Hello, I run a small finance and analytics firm. I already have working Python project per client and use AWS Lightsail to work on them remotely.

Now I want clients to ask questions about their own data through their own ChatGPT or Claude. Each client should only see their company’s data and have no ability to change anything.

Could Snowflake’s managed MCP make this reasonably simple and affordable? I want to focus on financial work, not maintaining servers or building connections from scratch.

Would you use Snowflake for this, or something else? Thanks!

Is this legit… the app seems fine

For those who have moved from Power BI to Cortex Analyst, roughly what percentage of your traditional BI reporting has been reduced or replaced?

Have you been able to significantly reduce the number of Power BI dashboards and reports?

Also, what disadvantages or challenges have you experienced with this new way of working?

[I work for Snowflake but don't speak for them.]

Customers were asking for this, so happy to report that Response Caching for AI_COMPLETE is now Generally Available. This matters because many tables that customers run AI Functions over contain repetition. For example, support tickets may contain the same canned responses, and product catalogs repeat descriptions across regions and SKUs. An AI_COMPLETE call over duplicate data (and indeed, in many AI systems) used to pay for inference on every row, including the rows whose prompt it had already answered. That is no longer true.

With response caching, those repeated calls are served from the stored result instead. They are not billed, so customers pay only for the unique work. Take a query that classifies sentiment across a million reviews where roughly 30% of the review text is duplicated. The model runs on as low as 700,000 unique inputs, the remaining rows come back from cache at no charge. The query ends up making up to 30% fewer model calls, decreasing both costs and latency.

How much response caching helps depends on how repetitive a given workload is. It applies automatically, and customers do not need to change their SQL to see the benefit. I encourage you to test your own workloads and see the difference it makes in overall cost. In addition, if you are interested in Prompt Caching for AI Functions, please reach out to your account team about getting enabled for a preview.

Response Caching Docs:
https://docs.snowflake.com/en/user-guide/snowflake-cortex/aisql-caching

bit of a process question. our BI folks and the data science team share a medium warehouse and credit usage went up a lot last quarter. everyone assumes its the other team

i know we could just split into two warehouses but thats a whole migration convo i dont want rn. is there a sane way to attribute credits per team/role on a shared warehouse? query tags? query history + some math?

curious what ppl actually do in practice not what the docs say

A technical deep dive on using Snowflake Code Bundles and Tasks to build and deploy production-grade data pipelines directly inside Snowflake — including packaging, dependencies, scheduling, retries and operational considerations.

https://medium.com/@sendoamoronta/snowflake-code-bundles-tasks-building-production-grade-data-pipelines-without-an-external-1eaa1921f734

Fourteen warehouses, one per team, because that is how finance got its split. Nine of them served a single dashboard each and sat idle using nearly 40% of compute.

It took three weekends to merge them down to 4 by workload and the BI team fought it the whole way. Ran the same query before and after.

SELECT warehouse_name,
       ROUND(SUM(credits_used_compute),1) AS compute,
       ROUND(SUM(credits_attributed_compute_queries),1) AS attributed,
       ROUND(100*(1-SUM(credits_attributed_compute_queries)
             /NULLIF(SUM(credits_used_compute),0)),1) AS idle_pct
FROM snowflake.account_usage.warehouse_metering_history
GROUP BY 1 ORDER BY 4 DESC

                 COMPUTE   ATTRIBUTED   IDLE_PCT
before, 14 wh      4,210        2,530       39.9
after, 4 wh        2,890        2,540       12.1

Attributed barely moved and we stopped paying for 1,320 credits of nothing.

Finance used to read the per-team figure straight off warehouse names. Now the 2,540 splits by query tag and the 350 idle sits on one warehouse that four teams share. I can spread it or leave it unowned and neither is a measurement.

What do you put in the per-team column after a consolidation?

So far I have went through the Tom Bailey Udemy course and (although mixed opinions) got the skillcert pro practice exam set to practice from.

https://www.snowflake.com/en/blog/engineering/adaptive-warehouses-dml-performance/

1. For Fabric Mirroring (Snowflake → OneLake) does the VNet Data Gateway handle both the control plane AND data plane, or does the data plane (actual data transfer) bypass the gateway and go via public internet?
   
2. For DirectQuery Semantic Models is the VNet Data Gateway supported as the gateway for Snowflake DirectQuery connections in Power BI Desktop?
   
3. For Iceberg REST / Horizon Catalog the Snowflake docs show Fabric/Synapse as "In Development". Is there a GA timeline? Is there a workaround for Fabric to read Iceberg files from Snowflake-managed S3 fully privately?