All Get Started Discussions posts

Re: Cells only shows 20 lines

aayush_410 — Tue, 22 Sep 2026 18:57:30 GMT

A few things worth checking before assuming a bug, since none of these are the auto-collapse-on-hundreds-of-lines feature you're already aware of, but any of them can make a genuinely short cell render taller than its line count and trip that same collapse threshold:

Check line wrapping. If Wrap lines is on (Profile → Settings → Developer settings → Wrap lines) and any of your 22 lines are long, each wrapped line counts toward the rendered line count the collapse feature measures — not your actual code line count. A handful of long lines wrapping to 2-3 visual rows each can easily push a 22-line cell past the same internal threshold meant for hundred-line cells. Try toggling it off and see if the cell expands fully.
Check your editor font size. Same mechanism from a different angle — Settings → Developer → Editor font size. A larger font increases the pixel height per line, which can trip a pixel-based max-height cap sooner than expected even without wrapping.
Try toggling the New cell UI. View → Developer settings → New cell UI (experimental). If you're on the new cell UI, try switching back to classic (or vice versa) — this is a newer feature and layout/height behavior differs between the two; if the shrink only happens on one, that narrows it to a UI-specific regression rather than something in your control.
Confirm it's every notebook, not just one. Since you said "all of my notebooks," that argues against a per-notebook setting and toward a workspace-level Developer setting (or a browser cache/zoom issue) — worth also checking your browser zoom level is at 100%, since a non-100% zoom can distort the pixel math the same way font size does.

If none of the above changes the behavior, this looks like a rendering bug rather than an intended feature working as designed (a 20-22 line cell shouldn't hit the same limit as a 700-line one) — worth filing it with Databricks support/the community moderators with a screenshot and your notebook UI version (classic vs. new), since there's no documented manual override for that collapse threshold.

Re: Migrate Azure Databricks between cross tenant

Martínezluas — Tue, 22 Sep 2026 17:05:48 GMT

Yes, DAB is a good approach if the new workspace is already built and you mainly need to migrate notebooks, jobs, pipelines, and other workspace resources. You can use databricks bundle generate to create configuration for existing resources, put it under version control, then deploy the bundle to the new workspace; just keep in mind that identities, secrets, Unity Catalog objects, and other external dependencies may need to be handled separately.

Re: Cells only shows 20 lines

Ashwin_DSA — Tue, 22 Sep 2026 15:15:23 GMT

Hi @andersloren,

Thanks for flagging this. Having checked this internally, I can see this isn’t the intended collapsed-cell behaviour. A recent notebook UI change introduced a fixed-height editor with an internal scrollbar, which can affect even relatively short cells. The code itself is still there, but the display is being capped.

The team has identified the issue, and a fix is being rolled out. As a temporary workaround, try opening the cell in focus or full-screen mode, which should display the full cell. Sorry for the inconvenience, and thanks for calling it out.

Cells only shows 20 lines

andersloren — Tue, 22 Sep 2026 13:51:00 GMT

Hello,

Suddenly, the visible portion of the cells in all of my notebooks have shrinked to only 20 lines of code. I know this is a feature when a cell stretches over hundreds of lines. But makes no sense why this should happen to a cell with only 22 lines (please see embedded picture). How do I fix this?

Check my blog on Databricks Unity Catalog

kapil_dua — Mon, 21 Sep 2026 11:55:47 GMT

Struggling with a legacy Hive Metastore? I just published a deep dive on modernizing your Data Governance on Databricks. Read how we executed a Unity Catalog migration for a complex, multi-tenant enterprise without disrupting daily business operations.

Check it out here: https://medium.com/@kapil.dua/navigating-multi-tenant-data-governance-transitioning-to-databricks-unity-catalog-7fd90b4fe48f

Re: Unexpected Script Execution Differences on databricks.com vs Mobile-Triggered Runtimes

tonyjaskcxom — Sun, 20 Sep 2026 16:58:45 GMT

One thing I’d add is that reproducibility also depends on keeping track of software versions over time. A test that behaves consistently today may produce different results after an application, dependency, or runtime update, even when the original code hasn’t changed. Keeping a simple record of the versions and configuration used for each test makes those changes much easier to trace. That’s also why I tend to check Delta Executor updates when looking at changes in Roblox-related software rather than assuming every release behaves the s

Serverless compute returns 403 on S3 buckets in the account-regional namespace

alexgfowler_ld — Sun, 20 Sep 2026 09:10:34 GMT

SUMMARY

Serverless SQL warehouses and serverless notebook/jobs compute cannot read S3 buckets created in the AWS account-regional namespace (names ending -{accountId}-{region}-an). Classic compute reads the same buckets through the same IAM role without trouble. The failure is a 403 Forbidden with a null S3 request ID, raised by the bundled aws-sdk-java/1.12.681.

Measured across two days, same region, same role, same credential, same file, with the reads interleaved seconds apart:

Global namespace buckets: 15 of 15 reads succeeded

Account-regional namespace buckets: 0 of 14 reads succeeded

ENVIRONMENT

Cloud: AWS

Workspace and UC metastore: ap-southeast-1 (Singapore)

Affected compute: Serverless SQL warehouse (PRO, 2X-Small), serverless notebook/jobs compute

Unaffected compute: Classic job clusters, non-serverless PRO SQL warehouse

Client named in errors: Hadoop 3.5.0, aws-sdk-java/1.12.681

Storage access: Unity Catalog storage credential over a cross-account IAM role, external locations, managed and external tables

THE SYMPTOM

Any serverless read of an object in an account-regional bucket fails at the first

getFileStatus call:

[UNAUTHORIZED_ACCESS] Unauthorized access: s3://my-bucket-ACCOUNTID-ap-southeast-1-an/bronze/.../_delta_log: getFileStatus on s3://my-bucket-ACCOUNTID-ap-southeast-1-an/bronze/.../_delta_log: shaded.databricks.awssdk.com.amazonaws.services.s3.model.AmazonS3Exception: Forbidden; request: HEAD https://my-bucket-ACCOUNTID-ap-southeast-1-an.s3-ap-southeast-1.amazonaws.com Hadoop 3.5.0, aws-sdk-java/1.12.681 ... credentials-provider: ...BasicSessionCredentials credential-header: AWS4-HMAC-SHA256 Credential=ASIA...../20260919/ap-southeast-1/s3/aws4_request signature-present: true (Service: Amazon S3; Status Code: 403; Error Code: 403 Forbidden; Request ID: null; S3 Extended Request ID: null; Proxy: 192.168.200.20) SQLSTATE: 42501

Two details are worth noting immediately.

1. Request ID: null. A genuine IAM denial from S3 returns a real request ID. We have one in our records from a deliberately under-permissioned probe. A null request ID is the signature of this problem and the fastest way to tell the two apart.

2. signature-present: true, with a real ASIA session credential correctly scoped to the

region. Credentials were vended and the request was signed. This is not a

missing-credential problem.

FINDING 1: ACCOUNT-REGIONAL BUCKETS FAIL, GLOBAL-NAMESPACE BUCKETS SUCCEED

AWS lets you create a general-purpose bucket in either the shared global namespace or your account-regional namespace. The latter requires the name to end -{accountId}-{region}-an and is created with --bucket-namespace account-regional. It is a security feature: the name can only ever be claimed by your account, so it cannot be re-registered by a stranger after deletion.

Serverless cannot read them.

Evidence: Two buckets created 90 seconds apart in ap-southeast-1, holding a byte-identical file,

accessed through the same IAM role, the same Unity Catalog storage credential, and the

same external-location and volume mechanism. The only difference is the namespace.

Measured across two separate days, on cold warehouse sessions, with the two reads

interleaved seconds apart:

Global namespace: 15 of 15 successful reads

Account-regional namespace: 0 of 14 successful reads

Both credentials were tested against both buckets, in both directions, and the result never moved. The same holds for real Delta tables, not just probe files. A catalog whose managed location is a global-namespace bucket serves a 1,634-row Delta table to serverless without issue, while the identical data in an account-regional bucket returns 403 in the same session.

What this is not

We eliminated these by test, not by argument.

IAM permissions. Classic compute reads and writes the same bucket through the same role. The two bucket policies involved are structurally identical, same actions including s3:ListBucket, differing only in the bucket ARN.
The storage credential. Two different UC storage credentials, both over the same role, give identical results on both buckets. Neither credential is impaired: both succeed elsewhere.
Unity Catalog configuration. A brand-new external location, volume, schema and catalog reproduce it. A brand-new external location over a working bucket succeeds. Object age is not a factor.
Bucket configuration. Identical AES256 default encryption, no bucket policy, BucketOwnerEnforced, identical public access block on both.
Workspace identity. Reproduced on a second, independently created workspace with its own VPC.
Access method. Fails through external tables, managed tables and UC volumes alike.

FINDING 2: WHY ONE REGION IS UNAFFECTED, AND THE SDK ANGLE

ap-southeast-5 (Malaysia, GA August 2024) is unaffected even for account-regional buckets. Same account, same role, same credential. It simply works, and still does.

The bundled client named in every error is aws-sdk-java/1.12.681

Extracting com/amazonaws/partitions/endpoints.json from that exact artifact shows its partition metadata is dated 2024-03-15, five months before ap-southeast-5 became generally available, and the region is absent from it entirely.

That gives a coherent, testable hypothesis: regions known to the pinned SDK route through

a legacy client path that does not handle account-regional bucket addressing, and regions

unknown to it fall through to a different path that does. The AWS SDK for Java v1 final

release (1.12.797, December 2025) does contain ap-southeast-5, so if that is the

mechanism, refreshing the dependency would move ap-southeast-5 into the failing set rather

than fixing anything.

We cannot verify this from outside. We cannot inspect which client handles the request,

and no supported Spark configuration on serverless lets us influence the storage path. We

offer it as a starting point, not a conclusion. The measured facts are that one region

works, others fail, and the difference tracks that SDK release date.

CONFOUNDING FACTORS, PLEASE READ BEFORE REPRODUCING

These cost us several wrong conclusions.

1. A newly created S3 bucket is unreadable from serverless for up to about 2 hours

Regardless of namespace, and with the identical 403. We measured a brand-new bucket failing at 2 minutes, 27 minutes and 42 minutes old, then succeeding consistently from about 2 hours. Create two buckets, test them immediately, and both fail, leading to the confident and wrong conclusion that namespace is irrelevant. Three of our own experiments were voided this way. It is consistent with S3 virtual-hosted-style DNS propagation, given the failing requests use the legacy s3-REGION.amazonaws.com form.

Wait at least 2 hours after bucket creation before drawing any conclusion.

2. Control-plane credential validation passes regardless

Validating the storage credential returns READ, LIST and PATH_EXISTS all PASS on locations

that serverless compute cannot read. Validation exercises the role without the compute

session scoped-down policy. A green validation is not evidence.

3. Direct file queries swallow the real error

Querying a path directly returns a generic FAILED_TO_CREATE_PLAN_FOR_DIRECT_QUERY. Use an

external table, a managed table, or a UC volume. Otherwise the underlying S3 exception

never surfaces and you cannot tell this apart from anything else.

4. Single trials are not enough

We once observed a global-namespace bucket succeed twice and then fail 90 seconds later,

at the tail of the propagation window above. Run at least three trials per configuration

and treat any inconsistency as a result in itself.

5. S3 server access logging will not answer whether the request reached S3

We enabled it and planted a known-good control request. The control never appeared in the

delivered logs either, so the absence of the failing requests proves nothing. Server access

logging is best-effort by design. Use CloudTrail S3 data events if you need this question

answered.

6. Unity Catalog itself is fine

Listing catalogs, schemas, tables, external locations and storage credentials all work on

serverless, and lineage is captured normally. Only the reading of objects out of S3 fails.

Do not go hunting a Unity Catalog misconfiguration.

REPRODUCTION

Step 1. Create two buckets that differ only in namespace.

ACCOUNT_ID=your-aws-account-id REGION=ap-southeast-1 AN=repro-$ACCOUNT_ID-$REGION-an GL=repro-global-$ACCOUNT_ID aws s3api create-bucket --bucket $AN --region $REGION \ --create-bucket-configuration LocationConstraint=$REGION \ --bucket-namespace account-regional aws s3api create-bucket --bucket $GL --region $REGION \ --create-bucket-configuration LocationConstraint=$REGION

Any region that predates roughly March 2024 should show the behaviour.

Step 2. Put an identical file in each.

printf 'id,label\n1,test\n' > /tmp/c.csv aws s3 cp /tmp/c.csv s3://$AN/files/c.csv --region $REGION aws s3 cp /tmp/c.csv s3://$GL/files/c.csv --region $REGION

Step 3. Grant your existing Unity Catalog role read access to both buckets: s3:GetObject

on the object ARN, plus s3:ListBucket and s3:GetBucketLocation on the bucket ARN.

Step 4. In Unity Catalog, create one external location per bucket using the same storage

credential, and an EXTERNAL volume over each.

Step 5. Wait at least 2 hours. Then, on a serverless SQL warehouse or serverless notebook:

SELECT count(*) FROM read_files('/Volumes/CAT/SCH/global_vol/', format => 'csv'); SELECT count(*) FROM read_files('/Volumes/CAT/SCH/account_regional_vol/', format => 'csv');

The first returns 1. The second returns 403 with Request ID: null.

Control: point the same two queries at a non-serverless warehouse or a classic cluster. Both succeed. That is what localises the fault to serverless compute rather than to IAM, the bucket, or Unity Catalog.

WORKAROUNDS WHILE THIS IS UNPATCHED

1. Use a global-namespace bucket for anything serverless must read.

This is the only real fix available to customers today. Verified end to end: a catalog with

its managed location on a global-namespace bucket in the same region serves Delta tables to

both serverless SQL warehouses and serverless notebooks, with data loaded and written from

serverless too.

The cost is real and worth stating plainly. The account-regional namespace exists so that a

bucket name can never be claimed by another AWS account. Moving to the global namespace

gives that up. The namespace also cannot be changed in place, so this is a data migration,

not a rename.

2. Use classic compute for existing account-regional buckets.

Classic job clusters and non-serverless PRO SQL warehouses read them without trouble. The

trade-offs are minutes rather than seconds to start, a 10-minute minimum idle timeout on

non-serverless warehouses, and cluster management you may not want.

One trap worth knowing: a workflow task that omits its job-cluster setting silently falls

back to serverless and then fails at run time. Assert the setting in whatever you use to

define jobs.

3. Keep an eye on fine-grained access control.

Databricks documents a dedicated classic cluster as delegating row filters, column masks

and dynamic views to the workspace serverless compute. If that path applies, a table with a

row filter could fail on a classic cluster that otherwise works. We have no such filters and

have not tested this. Flagging it because classic compute is otherwise the safe ground.

A SEPARATE ISSUE WE HIT ALONG THE WAY: TRIAL ACCOUNTS AND SAME-REGION S3

Independent of the above, and worth recording because the two are easy to conflate. We lost time doing exactly that.

While our account was on a trial, serverless could not read any customer bucket in the workspace own region, global-namespace buckets included. Cross-region buckets were unaffected:

ap-southeast-5, account-regional, customer bucket A -> works ap-southeast-5, global, customer bucket B (new) -> works ap-southeast-1, global, customer bucket C -> 403 ap-southeast-1, account-regional, customer bucket D -> 403

Bucket C, a global-namespace bucket in the metastore own region, returned 403 during the trial. After the trial concluded, the same bucket, unchanged, reads reliably. Bucket D still fails, which is Finding 1.

Databricks documents serverless as reaching same-region S3 through a Databricks-owned private gateway, and different-region S3 out through NAT. A trial-level network restriction on the private gateway path would produce exactly the table above while leaving the NAT path alone. Two staff posts on this forum state that trial accounts carry network restrictions regardless of configured policy settings, and the serverless SQL warehouse requirements state the account must not be on a free trial.

This is inference, not measurement. We cannot put the account back on trial to test it. The before and after on bucket C is measured, and the trial end date is confirmed in writing. The mechanism is not.

Note the shape carefully. It is not that trial accounts cannot use customer S3 buckets. Cross-region customer buckets worked fine throughout. Stating it too broadly sent us chasing the wrong mechanism for days.

WHAT WOULD HELP FROM DATABRICKS

1. Confirm whether account-regional namespace buckets are supported by serverless compute. If not, this belongs in the documented limitations. The namespace is a first-class AWS S3 feature and nothing currently warns against it.

2. Identify the component returning the 403. The request is signed, the credential is valid, and the same address answers correctly when the request comes from other compute, but the response carries no S3 request ID.

3. Comment on the SDK hypothesis in Finding 2, which we cannot test from outside.

4. Confirm whether a trial account restricts same-region serverless S3 access. If it does, that belongs in the published trial limitations. We could find nothing stating it.

Re: Migrate Azure Databricks between cross tenant

stephen4 — Sat, 19 Sep 2026 10:12:58 GMT

A fresh workspace makes DAB a reasonable option, especially since you mainly need to migrate notebooks, jobs, policies, clusters, and permissions.

Looking for Opportunities in BI Modernization | Databricks-Focused Projects

nafikazi — Fri, 18 Sep 2026 13:55:31 GMT

Hello everyone,

Good morning! I’m looking to connect with professionals and consulting firms working on Databricks and BI modernization initiatives across Canada and the U.S.

In my recent engagements, I have:

Supported legacy Power BI modernization and migration to Databricks
Worked on enterprise analytics tool offboarding and modernization
Built usage telemetry and analytics to support leadership decision-making and modernization strategy

I’m particularly interested in Databricks implementation, BI modernization, migration, and analytics consulting opportunities.

If your team is working on similar initiatives or looking for an experienced resource, I’d be happy to connect, exchange ideas, and explore how I can contribute.

I appreciate being part of the Databricks community.

Thanks,
Nafi

Re: help me how to fix

data_pulse — Thu, 17 Sep 2026 12:54:02 GMT

@FastFoodBro

Check this community Post as it talks about issues on compute and the limitations on Free edition. Also it has some recommendations on clearing the sessions state and if none of them works then can sign up for another Free edition.

Currently there isn't a quota visibility mechanism available in Free edition to check if you have really exhausted it. Try next day after the quota resets or registering new edition if something that can't wait. Also raise the issue in below channel for better visibility and response from databricks.

Databricks Community > Databricks Free Edition Help

Re: help me how to fix

FastFoodBro — Thu, 17 Sep 2026 10:40:55 GMT

How long will it be before I can resume my activities?

Re: help me how to fix

Satyasai — Thu, 17 Sep 2026 10:11:28 GMT

Hi @FastFoodBro
The Databricks Community and Community/Free tier workspaces have hard limits on total cluster compute time per organization ID, measured in hours or days. Once you hit your continuous job limit, the next runs are blocked from launching in the workspace.

help me how to fix

FastFoodBro — Thu, 17 Sep 2026 03:13:58 GMT

"Triggering new runs for organization 7474657040075149 is currently disabled temporarily." , Why did my job get blocked while running? You clearly said it could run continuously, so why was it blocked?

Migrate Azure Databricks between cross tenant

smukherjee — Wed, 16 Sep 2026 23:53:02 GMT

How to migrate an Azure Databricks to a different Azure tenant?

Does DAB is the best method if a databricks workspace is freshly built on top of just migrating the the workspace resources only like notebook, jobs, cluster, policies, permissions etc?
Please share detailed steps to achieve this.

Re: ODBC setup

Louis_Frolio — Wed, 16 Sep 2026 10:34:10 GMT

Hi @duzen , welcome to Databricks. You've hit a screen that trips up a lot of folks, so you're in good company. Your screenshot was very helpful.

The short version: "User Name and Password" expects actual Databricks credentials, and your account doesn't have any. You sign in through a browser with an emailed code, which points to Databricks Free Edition. That's fine. The ODBC driver can piggyback on that same sign-in using OAuth.

Before touching authentication, grab your connection details. In your workspace, go to SQL Warehouses, click your warehouse, and open the Connection details tab. You'll find the Server Hostname and HTTP Path there. Copy both.

Then in the ODBC DSN setup screen:

Enter the Server Hostname in the Host field (leave off the https://) and set Port to 443.
Click HTTP Options and paste in the HTTP Path.
Click SSL Options and make sure Enable SSL is checked (the OAuth flow requires it).
Change Mechanism from "User Name and Password" to "OAuth 2.0".
Click OAuth Options and set the Authentication Flow to "Browser Based Authorization", then click OK. Leave the username and password fields blank; do not enter your email or code there.
Click Test. A browser window will pop open and ask you to sign in the same way you always do, with your emailed code. Once you approve it, the driver handles the tokens from there, including refreshing them.

A few heads ups. Browser-based OAuth needs ODBC driver version 2.8.2 or newer, so since you just downloaded it you should be fine. If you're ever configuring this through a config file or connection string instead of the dialog, the equivalent setting is AuthMech=11 with Auth_Flow=2. And if you notice a PWD value hanging around with that setup, it's just a passphrase the driver uses to protect its cached refresh token, not a Databricks password.

There's a second option worth knowing about. If your workspace lets you generate a personal access token (check under Settings > Developer > Access tokens > Manage), you can keep the mechanism as "User Name and Password", enter the word token (literally) as the username, and paste the access token as the password. If you don't see an option to generate tokens, your workspace doesn't allow them and OAuth is your path. Either way, OAuth is what Databricks recommends these days.

One last thing: this browser-based flow only works for applications running on your local machine. If you're connecting from a server or cloud app down the road, you'll want a non-interactive method like a service principal instead. Also make sure your user has permission to use the warehouse you're pointing at.

References:

Authentication settings for the Databricks ODBC Driver: https://docs.databricks.com/aws/en/integrations/odbc/authentication
Create an ODBC DSN: https://docs.databricks.com/aws/en/integrations/odbc/dsn
Compute settings for the ODBC Driver: https://docs.databricks.com/aws/en/integrations/odbc/compute
Download and install the ODBC Driver: https://docs.databricks.com/aws/en/integrations/odbc/download
Personal access tokens: https://docs.databricks.com/aws/en/dev-tools/auth/pat
Free Edition limitations: https://docs.databricks.com/aws/en/getting-started/free-edition-limitations

Give the OAuth route a shot and let us know how it goes.

Cheers Lou.

Re: ODBC setup

duzen — Tue, 15 Sep 2026 21:29:19 GMT

This is the screen:

ODBC setup

duzen — Tue, 15 Sep 2026 21:26:14 GMT

Greetings!

I am just starting with databricks. I created an account, and I can run sql queries.

I downloaded the ODBC driver (64bit version). I am not sure how to set it up.

The configuration screen shows "Mechanism" set to "User Name and Password."

Then there are two fields for User name and Password. I don't have these. I login to databricks when they email me a code and I enter it in my browser.

Can anyone help?

Re: Broken x-axis sort order in combo chart

treesloth — Mon, 14 Sep 2026 13:06:24 GMT

Thank you. I appreciate the detailed response. I'd already done the second option as what I hoped would be a temporary workaround. Sadly, the categories are far from stable, and orders can switch (although that's not quite as common). But, at least now I know I'm not crazy (or, at least, that this is not what proves it). Thank you.

Re: Is the Semantic Layer the Real Challenge With Databricks Genie?

ThomasMonie — Sun, 13 Sep 2026 12:30:01 GMT

wrote:
I've been thinking about this while looking at different Genie use cases.
It seems like the hardest part of implementing Genie isn't getting users to ask questions in natural language. It's making sure Genie understands what those questions actually mean in the context of the business.
For example, if someone asks:
“Why did our performance drop this week?”
Genie still needs to know:
Which metric are we talking about?
Which data is authoritative?
How are the tables related?
What does “performance” actually mean for that business?
Are there business rules that should affect the answer?
This becomes even more important for operational use cases. Questions around NPT, ROP, equipment performance, or cost can require data from multiple systems.
That makes me wonder if we're putting too much emphasis on the conversational interface and not enough on what sits underneath it.
Maybe the real Genie implementation project isn't building the Genie space. website It's building the context that allows Genie to give a trustworthy answer.
Curious what others are seeing:
What has been the bigger challenge in your Genie projects — configuring Genie itself, or getting the underlying data, metrics, and business definitions ready for it?

I agree that the semantic layer is probably the part that requires the most careful planning. A natural language interface can make querying easier, but if the underlying metrics and business definitions are inconsistent, Genie can still produce an answer that sounds correct but is based on the wrong context. Starting with one well-defined business area and expanding gradually seems like a much safer approach.

Re: How Can I Become a Databricks Partner Champion Through Technical Expertise?

ThomasMonie — Sun, 13 Sep 2026 11:50:16 GMT

@ChiranjeeviKudu wrote:
Hello Databricks Community,
I am actively building my technical expertise in the Databricks ecosystem and would like guidance on the roadmap to becoming a Databricks Partner Champion, with a strong focus on technical skills and hands-on implementation.
I understand that Databricks has different recognition programs, including Community Champions. However, my question is specifically about becoming a technically recognized Databricks Partner Champion through certifications, partner learning, solution architecture, and real-world implementation experience.
I would appreciate guidance on the following:
What are the current eligibility requirements for the Databricks Partner Champion Program?
Which Databricks Partner Academy courses or learning paths should I complete?
Are there specific instructor-led courses or badges, such as Solution Architect Essentials, that are required?
Which paid Databricks certifications are recommended or required for the Partner Champion journey?
Should I focus on Data Engineering, Data Analytics, Machine Learning, Generative AI, or Solution Architecture certifications?
How much hands-on customer or implementation experience is expected?
Is nomination by my organization or a Databricks Partner Solutions Architect required before joining the program?
After completing the required courses and certifications, what is the official process for applying or being nominated?
Is there an official roadmap or checklist that explains the complete journey from Databricks Partner learning to Partner Champion recognition?
My goal is not specifically to become a monthly Community Champion. I am primarily interested in developing strong technical Databricks expertise, gaining relevant certifications, working on real-world implementations, and following the correct path toward the Databricks Partner Champion Program.
If anyone from the Databricks team or experienced Partner Champions can share the latest roadmap, prerequisites, recommended certifications, and nomination process, I would greatly appreciate your guidance.
Thank you in advance for your support! Looking forward to learning from the Databricks Community.

This is really helpful, especially the clarification that the program is invitation and nomination-only. The certification requirements also make the path much clearer. I think having the freedom to choose the Associate and Professional specialization based on your skills is a good approach, rather than forcing everyone into the same technical track.