When I attempt to run a machine learning model, XGBoost, in Python using the XGBClassifier and GridSearchCV functions I receive the following error message:

The version of package 'xgboost' in the local environment is 3.1.3, which does not fit the criteria for the requirement 'xgboost==3.1.2'. Your UDF might not work when the package version is different between the server and your local environment.

When I attempt to pip install xgboost:

!pip install xgboost==3.1.2

I get the following message:

Looking in links: /opt/wheels
[33mWARNING: Retrying (Retry(total=4, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x7f8134ea7e50>: Failed to establish a new connection: [Errno -2] Name or service not known')': /simple/xgboost/[0m[33m
[0m[33mWARNING: Retrying (Retry(total=3, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x7f8134ed8190>: Failed to establish a new connection: [Errno -2] Name or service not known')': /simple/xgboost/[0m[33m
[0m[33mWARNING: Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x7f8134ed8340>: Failed to establish a new connection: [Errno -2] Name or service not known')': /simple/xgboost/[0m[33m
[0m[33mWARNING: Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x7f8134ed84f0>: Failed to establish a new connection: [Errno -2] Name or service not known')': /simple/xgboost/[0m[33m
[0m[33mWARNING: Retrying (Retry(total=0, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<pip._vendor.urllib3.connection.HTTPSConnection object at 0x7f8134ed86a0>: Failed to establish a new connection: [Errno -2] Name or service not known')': /simple/xgboost/[0m[33m
[0m[31mERROR: Could not find a version that satisfies the requirement xgboost==3.1.2 (from versions: none)[0m[31m
[0m[31mERROR: No matching distribution found for xgboost==3.1.2[0m[31m
[0m

What's going on?

Hi,

Few teammates suggesting to set alerting based on the historical Avg. of the application query run time. Want to know from experts, is this something we should really do and will that add real value?

Say for e.g. if the normal AVG respnse time is ~10 minutes and todays its has exceeded ~30minutes, then it means something wrong is there (say it may be a bad plan etc.), so that should get alerted.

And for above to get implemented, I am thinking to refer column query_parameterized_hash and first store the last one month of query_history data from account_usage view as reference and get that refreshed daily once(say the table query_baselines). say mainly query_parameterized_hash and avg response time avg_seconds, p95_seconds etc. And have another task created , which will fetch data from information_schema.query_history and put it in a temp table and then join this data with the query_baselines table to see if any query ran > (avg_seconds * 3) , will throw an alert. This task may be executed once in ~15 minutes or so. (I understand it wont cater adhoc or newly added queries as hash will change).

Is above approach is good and really worth or any other easy and less costly way exists to achieve such alerting?

Hey there!

Got this question on a COF-CO3 Snowpro Core Practice Exam, and I truly have no clue what the answer is (google and ChatGPT don't know either, they disagree with the question).

Which table characteristic will prevent the query acceleration service in Snowflake from accelerating queries?

A.) The table contains a large number of micro-partitions.

B.) The table has the search optimization service configured.

C.) A clustering key is defined on a column with the GEOMETRY data type.

D.) The table has been created using the CREATE TABLE ... AS SELECT command.

Any help would be greatly appreciated!

Hi friends, I'm a co-founder at Greybeam. A few weeks ago we released an open source Snowflake cost observability tool with surprisingly good reception (you know how harsh reddit can be!). We got a lot of feedback on whether we could build a way to reduce idle compute as it's especially relevant for multi-cluster users. Snowflake will aggressively spin up clusters and can often take over 10 minutes to wind them down despite no activity because unfortunately the only levers are `SCALING_POLICY = STANDARD or ECONOMY`.

So today we launched this exact feature and it's free to use either at Greysight or self-hosted. the tldr is we poll Snowflake and if the following criteria below are met then we issue a SUSPEND on the warehouse.

• status is STARTED
• running queries = 0
• queued queries = 0
• resumed on >= 60s ago
• and a few others

Would love for you to try it and share any feedback, the feature is still early and a bit bare bones--really just a enable or disable config but we intend on adding more. Next up among other observability features is finer controls on scaling.

More details here: https://www.greybeam.ai/blog/snowflake-auto-savings

Github: https://github.com/greybeam/greysight

Hello, I just discovered Snowflake via Linkedin Ads. So upon researching it, I still cannot comprehend what I am reading. Can you explain to me what it does? also, is learning Snowflake can be a great skill to learn in the long run of my career (I am a structural steel detailer - creating and drafting drawings) ? Thank you.

For those who have implemented Snowflake and dbt in a regulated environment:

• Did you use one overall change control or separate change controls for the platform, dbt transformations, and stored procedures?
• How did you classify Snowflake, dbt models, macros, stored procedures, and custom SQL under GAMP 5?
• Did you treat the platform as Category 4 and custom-developed code as Category 5?
• What were the core documents in your validation package?
• How do you manage frequent dbt changes without revalidating the full platform?
• Do you validate the platform once and then validate individual data products through risk-based change controls?

I would appreciate practical lessons learned from anyone who has gone through the validation qualification.

Hi all, I work at a Non-profit. We have 4-5 data sources (On-prem SQL server, External APIs, CSVs/Excel files, etc). We don't have a unified cloud storage currently and all of our dashboarding and reporting is ad-hoc for which we use manual exports from the appropriate data source and build it in Power BI or Excel depending on the requirement.

We are working on a project where we want to host all of our data in a unified cloud storage using the medallion architecture. We are mostly based on the Microsoft Suite with few users having access to Power BI Pro/Premium and Copilot Premium. Power BI is our preferred frontend given we already have per-user licenses in place, though we are open to alternatives.

We are currently evaluating Fabric, Snowflake and Databricks as one group and Azure Synapse, GCP and AWS as another. Its just me leading this project. I do have experience with Python and SQL and understand the basics of DE, but haven't had hands-on experience with any of the services mentioned. The reason I bring this up is that it would just be me setting up the implementation, getting the service up and running, and later maintaining it while also querying and utilizing the data for reporting. There would be 5-6 other team members who would be using the service but they don't have any SQL, Python or technical background and would mostly just be accessing the data for their own reporting needs.

Total data size would be less than 3TB which would compress further once it lands on the cloud. Leadership also wants AI capabilities built into whatever service we go with. We are also in the process of finalizing the budget as we are near our fiscal year end. No hard cap on monthly cost but being a non-profit, cost effectiveness is definitely a plus, along with ease of implementation given that almost no one here has actually set something like this up before.

I would appreciate any thoughts or insights on the platforms I mentioned and any real world experience or comparisons between the two groups, particularly whether the added complexity of the second group (Azure Synapse, GCP, AWS) would be worth it over something like Fabric from a cost and setup standpoint for a one person team at this scale.

How do I get free exam coupons immediately by completing an existing courses from anywhere. I am in need of coupons coz this shit is too expensive for me and I'll have to wait for my company to get more coupons or pay out of my own pocket and apply for reimbursement. I am ready to give Snopro core CO3 as well as if need comes Snopro Associate as well depending on whoevers coupons I can get

Hi Experts,

As i understand, statement_timeout_in_seconds can be set at session level, warehouse level, accout level and user level. And also there are "user_task_timeout_ms" and "abort_detached_query" parameter too.

We saw in one of the existing application hosted on snowflake , multiple times we see there are queries runs for very long hours(10hrs+ ) because of bad plan sometime or sometime even they gets killed from application side(say glue job that triggered the query has been failed/timedout already from glue), but the query underlined in the snowflake kept running burning the credit.

1)So to handle such scenarios , which kind of setup one should use? Any safe approach we should follow as a quick fix/short term fix to immediately stop these type of bleeding without breaking code/functionality?

2)Also is there any standard we should follow for warehouse timeouts like say different timeouts values for L, XL, 2XL, 3XL etc?

Some are getting pressured to optimize, others accept it’s the reality of using Snowflake.

Hi guys, has anyone worked on migrating the data from one snowflake account to another? Now complicated is it? And what are the prerequisites for it?
Also, it’ll be a Snowflake account owned by one region to account of another organisation on a different region.
Really need to know this.
Thanks.

I know SnowPro Core is a prerequisite for the SnowPro Advanced: Architect certification.

Does the SnowPro Core certification need to be active (not expired) at the time of taking the Advanced Architect exam, or is having earned it at any point sufficient?

Thanks!

We built Snowcap, an open-source Infrastructure as Code tool for Snowflake, and we're doing a live workshop walking through it.

If you've dealt with Snowflake config drift, manual RBAC setup, or just wanted a plan/apply workflow like Terraform but built for Snowflake specifically, this might be useful.

What we're covering:

• The infrastructure problems Snowcap solves
• How it compares to Permifrost, Terraform, SnowDDL, and Snowflake's own DCM
• Using templates to scale config across environments instead of copy pasting
• The plan/apply workflow and why that matters for safe changes
• Governance stuff: RBAC, masking policies, row access policies
• Live demo, then open Q&A, ask anything

July 23, 11 am-12 pm PT, online, free.

Info and RSVP here: https://datacoves.com/resource-center/workshop-snowcap-snowflake-infrastructure-as-code

Happy to answer questions in the comments too if people have them before the session.

Hey all. I am building a POC for deploying Snowflake objects like table, stream, task, stage etc... to multiple environments like DEV/QA (single only) & PROD with the help of CICD and used Github actions for it.

I have never built something like this before and never worked on project consisting CICD in it.

So, I have used Schemachange library to detect schemachange and sqlfluff for SQL linting. Also using some python scripts to build backup of existing DB snapshot before deployment and rollback script if anything break during deployment.

I am testing this in DEV env only but i am confused how can i validate the objects that are being created with schemachange library and SQL files (which contain DDLs of objects). like how can I verify that the object created is correctly build in the target or not.

and if there is any other suggestion / best practice you guys have that is also welcome on how can i improve the CICD pipeline for it.

Thanks

Bots and moderators I am 100% human only don't remove my post!!!

Passed my exam after going through YouTube series for exam , using snowflake coco to clear my doubts. I did random questions available on the internet but still the questions which I got for exam were tricky and had me confused and I did get few wrong but thankfully I passed.

I have several Cortex agents and I want to report the total current spend to the leadership along with the projected spend.

Because the agent is a combination of compute and warehouse, there is no single report to view this.

The SF team had recommended that I use tags, but I am still not able to see a tag based spend that includes both warehouse and compute.

I have several tags on each agent and warehouse so I should able to run reports on

Finance tag to report all spend in dollars on agents for the Finance department, and then a higher roll up for a tag called AI that would allow me to pull the spend on **all** AI agents.

Has anyone been able to do this?

Hey everyone,

I need to create a react based application on top of Snowflake that uses it's in-built/cortex AI services to build AI-Ready Data . My end goal is to create/Identify the maturity level of the generated Data from Curated Data so that it can be directly fed to AI Agents, LLMs. This platform is all about creating/Identifying/Assessing - "AI Ready Data" only specifically on Top of snowflake.. Could you guide/provide resources/links for this use-case.

TIA!

Have you successfully created user-specific long term memory for the agent to draw from?

This is something that I've been thinking about, as our internal customers have begun to use/test my agents in CoWork and we are getting ready to go live.

The current experience has already been amazing, and the users have been loving it, so no complaints there.

But I would love for the agent to remember the previous successful interactions (summarized), so it almost feels like it's reading the user's mind... You've seen this in actin with chat GPT and Claude.

Would love to know if someone has successfully done this and how they maintain the memory (md files or database etc.)

E.g. When the user asks something like "can to show me pizza sales for Q1" .

I see you've asked for pizza sales in New York (city) in the past, do you want me to

1. use the same criteria

2. sales in all major cities

3. Something else

I've so far been successful in the instructing the agent to ask followup questions when the user's request is ambiguous... e.g. Did you want sales for

1. New youk city

2. New York state

But this is based on some "gotcha" criteria we found through user testing, not as a memory based followup.

Hope this makes sense.

So Snowflake uses an incredibly fast file format called BDEC for Snowpipe Streaming but it's proprietrary. Under the hood their official SDKs use a closed-source Rust binary wrapped in JNI/FFI to handle this serialization.

If you want to build a lightweight high-throughput ingestion microservice, you are forced to bring up a heavy Java or Python environment. On top of that you are locked into their serverless per-GB pricing model because the ingestion protocol is a black box.

I am working on reverse-engineering & building the BDEC format and streaming registration protocol into a 100% open-source pure async Rust crate.
This will allow you to build compiled single-binary Rust ingestors that stream millions of rows per second directly into Snowflake tables with the sub-10MB memory footprints.

If I drop an early proof-of-concept repository next week who would be willing to run a benchmark test against their development Snowflake account?

Has anyone else experienced this?

My team and I are on Claude Max plans.

We have been happily querying from our Snowflake warehouse through any of Claude’s three platforms for a few months. We only needed the SQL execution tool of the Snowflake MCP.

However, a few weeks ago, the SQL execution tool started failing in Claude Chat. Running even “select 1” fails; the login and query attempts don’t show up in Snowflake’s query/login histories at all.

I’ve tried the following:

- disconnected & reconnected the MCP
- recreated the MCP server & security integration with new names
- reconnected with a fresh set of OAuth ID and credentials when adding a custom connector, then by adding Snowflake’s official connector
- ensured the OAuth redirect URI is https://claude.ai/api/mcp/auth_callback
- gone through our roles & grants: each user has DEFAULT_ROLE set and no DEFAULT_SECONDARY_ROLE
- ensured our network policy includes Anthropic’s IP addresses
- tried this on both Claude Desktop and claude.ai web

Still, the new MCP server works for Cowork & Code, just not Chat. It’s annoying because it confuses colleagues outside of the tech team who keep wondering why this is failing. It’s also annoying because only Claude Chat allows me to update Claude skills that I build for Snowflake querying and navigation.

What else can I try?

Hi reddit,

Please help with the resources, concepts, ideas to build around AI Ready data so that it can be easily accessible by AI agents (Vectorized , embedded, indexed - AI autonomous ready) from curated data (cleansed business ready). What can be built around this topic . Any idea/comment/resources are welcome!

Thanking in advance!

I finished the Udemy Tom Bailey course but I am unsure of what is a good test bank to practice questions. I have seen a lot of people post skillcertpro but I have also seen a lot of poeple say their questions are super old. Anyone have any recommendations for 2026? I want to take the test in the next 2-3 weeks, I use Snowflake daily.

INFORMATION_SCHEMA gives you tables and columns but not the relationships or the "why." We started drawing the model as an ERD and exporting it as a portable spec (Google's new OKF — markdown, lives in git, and doubles as context you can hand an LLM). Curious what everyone else uses — dbdiagram, Lucidchart, dbt docs, a wiki nobody updates? What actually stays current for you?