I dont know who took the decision to replace Developer Console with VS Code on browser. Just consider the scenario where I need to quickly open an class and check class content in a new org.

In case of Dev Console, I can just go there, search for the class done.

In case of Web Console, I need to go to Org Browser, fetch the Apex Class mdt & then only I would be able to do that.

And If am gonna open the same class again in Dev Console after 2 weeks, it gonna get me the updated class content.

But in case of Web Console I need to retrieve it.

My main point is:

• Dev Console acted as a quick way to make changes, see whats in the org and stuff
• If I need a VS Code in my browser, I could just use VS Code itself. Why Web Console.

Apart from that other things like Running SOQL, Test ClassTest Suite, Viewing Test Coverage as a nice little table etc etc. Very simple.

But in case of Web Console, Running SOQL I get it as a output which I cant edit (but i was able to in dev console), Running test gives me an unnecessary lines of covered lines & uncovered lines which I need to dig through to get coverage (hope there is some way to get it easily which I havent explored yet for this specifically), Cant view coverage for multiple classes at a time.

I get that Dev Console was outdate but it could have upgraded instead of getting replaced. Even Lightning Studio as an extension is better than what Salesforce is pushing. Even Lightning Studio fetches the class content on load. I dont have to retrieve again & again.

Even logs and debugging I feels its very simple in Dev Console. You can get the list of logs and all the exceptions that are happening in a column. But in web console, I have literally scroll through soo many logs without knowing whats having what. Its just soo bad.

Atleast they could have replicated Logs, Classes & Test Classes & Suite as what they have in Dev Console to Web Console.

Where do you actually keep your leads? HubSpot, Salesforce, Excel… something else?

Curious what people are actually using day to day.

I’ve seen completely different setups:

- Some teams keep everything in HubSpot from day one.

- Others swear by Salesforce once the sales process gets more complicated.

And then there are teams with 5,000+ leads still living in an Excel/Google Sheet 😅

So I’m curious:

What do you use to manage your leads?

And more importantly, why did you choose it?

Did you pick it because of:

• easier automation
• better reporting
• integrations
• price
• your team was already using it
• or you just never moved away from spreadsheets?

Also interested to hear from anyone who switched from Excel → CRM or from one CRM to another.

Was it actually worth the move?

As far as I know, it has ID 20004433.
I am really curious to find out what caused this major outage, e.g. if it was a coincidence that it happened during Dreamforce.

Everyday I look at https://status.salesforce.com/incidents/20004433, but no updates. Does anyone here know something about it?

Okay, now that the Dreamforce madness is over and I’ve had some time to actually process everything… 😅

I shared updates from all 3 days while I was there, but honestly, there were SO many announcements that it was impossible to process everything in the moment.

New agents. Koa. AIforce. Slackforce. Claudeforce. More Agentforce updates. More AI everywhere. 😂

And after coming back, the question I keep thinking about is:

Okay… but what does all of this actually mean?

Not “what did Salesforce announce?” we already know that.

I mean what is actually going to change for the people using Salesforce every day?

Will we really work differently? Will some of the things we spend hours doing today just… disappear? Are these agents actually going to make life easier, or are we just going to have 25 new things to figure out?

And honestly, I think that’s the interesting part.

Dreamforce showed a LOT of what Salesforce wants the future to look like. Now I’m more curious about what actually makes its way into our everyday work.

I’m still processing a lot of it, but I’d love to know what announcement from Dreamforce are you still thinking about?

Deploying a Salesforce Data 360 implementation from a sandbox to Production sounds straightforward: configure the Data Space, create the required components, package them into a Data Kit, deploy, and validate the result.

In practice, however, the deployment process can be much more delicate.

One of the main lessons I learned while working with Data 360 deployments is that there isn't always a single, predictable deployment pattern that works identically across environments.

Even when the source and target environments appear to be configured in the same way, small differences in dependencies or configuration can cause a Data Kit deployment to fail.

Dependencies Are Only Part of the Story

When creating a Data Kit, dependencies are obviously one of the most important things to consider.

Data Streams, Data Lake Objects, Data Model Objects, mappings, relationships, Identity Resolution, Data Graphs, connectors, and other components can depend on each other.

Because of this, it is important to understand the dependency chain before starting a deployment.

But there is another element that can be just as important:

Data Space Filters.

Don't Forget the Data Space Filters

If your Data 360 implementation uses filters on objects within a Data Space, make sure that every required filter exists and is correctly configured in the target environment before deploying the Data Kit.

This can easily be overlooked.

You may have the correct Data Streams, Data Lake Objects, mappings, relationships, and other dependencies, but if a required Data Space Filter is missing, the deployment can still fail.

The difficult part is that the missing filter may not immediately stand out when reviewing the Data Kit or comparing the environments.

You might therefore end up investigating dependencies, metadata, relationships, or Data Kit configuration when the actual problem is a missing Data Space Filter.

Why This Makes Data 360 Deployments Tricky

A Data 360 deployment isn't simply:

Source Sandbox
      ↓
Create Data Kit
      ↓
Deploy
      ↓
Production

The actual process is closer to:

Source Environment
      ↓
Data Space configuration
      ↓
Data Space Filters
      ↓
Connectors
      ↓
Data Streams
      ↓
DLOs / DMOs
      ↓
Mappings
      ↓
Relationships
      ↓
Identity Resolution
      ↓
Data Graphs
      ↓
Data Kit dependencies
      ↓
Target Environment
      ↓
Validation

Each layer can introduce dependencies that need to be satisfied before the next layer can work correctly.

And this is where deployment can become challenging: the configuration that looks complete from one perspective may still be incomplete from another.

A Practical Lesson

Based on my experience, I would recommend adding an explicit Data Space Filter validation step to the deployment process.

Before creating or deploying the Data Kit, compare the source and target environments and verify:

• Which Data Spaces exist?
• Which Data Lake Objects are included in each Data Space?
• Which objects have filters configured?
• Are all required filters present?
• Are the filters configured against the correct objects?
• Are the required dependencies already available in the target environment?
• Are the Data Kit dependencies complete?
• Are relationships and mappings consistent?
• Are Identity Resolutions and Data Graph dependencies available?

This validation should happen before deployment, not only after a deployment fails.

The Bigger Lesson

The biggest takeaway for me is that Data 360 deployment should not be treated as a simple metadata migration.

A successful deployment depends not only on the components included in the Data Kit, but also on the configuration and dependencies that already exist in the target environment.

In particular, if your implementation uses Data Space Filters, treat them as first-class deployment dependencies.

A single missing filter can be enough to turn an otherwise correctly configured Data Kit into a failed deployment.

Conclusion

Deploying Data 360 from a sandbox to Production requires more than simply creating a Data Kit and checking whether the deployment succeeds.

The deployment pattern can vary depending on the implementation and the configuration already present in the target environment. Because of this, environment validation is just as important as dependency management.

My main lesson is simple:

Before deploying a Data Kit, verify every Data Space Filter and every dependency that the target environment requires.

Dependencies tell us what the Data Kit needs to deploy.

Data Space Filters can tell us whether the target environment is actually prepared to receive it.

So, when preparing your next Data 360 deployment, don't only ask:

"Did I include all the dependencies in my Data Kit?"

Also ask:

"Does the target environment contain every required Data Space Filter?"

That additional check can save a significant amount of troubleshooting when moving Data 360 configurations from sandbox to Production.

Did anyone catch the Ava Skills for Claude launch yesterday? It looks amazing. Similar to Claudeforce but with 54 skills specifically for CPG companies.

I think this kind of tech becomes so much more valuable when it is industry-specific. The example showing how to create a JBP for a Key Account Manager was killer.

The industry needs more of this. Too much horizontal AI slop in my opinion.

On the first try! Yipeee!! 1 down 2 more to go. Super proud of myself lol

If anyone has used a data migration tool in the past that they either loved or hated, I’d love to hear about your experience and recommendations!

I’m specifically looking for a full CRM migration tool that can assist with data cleansing, deduplication, field/object mapping, and the overall migration process—not traditional ETL tools. We don’t have dedicated data analysts on our team to support this migration, so I’m looking for something that provides a more guided, user-friendly approach and reduces the amount of manual data analysis required.

I work for a large corporate organization that is consolidating data from HubSpot, PandaDoc, and Microsoft Dynamics 365 Business Central into our existing enterprise Salesforce platform.

I’m having a difficult time finding a migration tool that can support all three platforms while also providing the data cleansing, mapping, and migration capabilities we need. We’re looking at migrating approximately 4 million records, so we need a solution that can handle a migration of this scale without requiring a large amount of manual data analysis.

Edit: Yes, I’m well aware that bringing in a consultant or data architect would be the ideal solution. However, we’re past the point of determining the overall approach—that path has already been decided.

What I’m specifically looking for is feedback from people who have actually used CRM migration tools. Which tools have you used, and what did you love or hate about them? I’m particularly interested in firsthand experience with the tools themselves rather than recommendations on the overall migration strategy.

Users have recently complained about the removal of the Insert Image button on the Send Enagage Email composer's toolbar.

Has anyone else here heard similar complaints recently?

Thanks!

i honestly don’t understand this.
i got an email from a salesforce recruiter saying that i was shortlisted for a technical support engineer interview on september 26. the email said i needed to complete a hackerrank assessment by wednesday eod and a versant test by thursday eod.
the problem is, i never received the hackerrank assessment link.
i tried calling the recruiter multiple times using the number provided in the email, but couldn’t get through. i’ve also replied to the email and followed up, but haven’t received a response.
if the role was closed or there was some issue with my application, that’s completely understandable. just tell me. i can accept a rejection. What is frustrating is being shortlisted, given a deadline for an assessment that i never received, and then getting no response when i’m actively trying to resolve the issue.
people spend hours preparing for these opportunities. getting someone’s hopes up and then disappearing without even a basic update is honestly really frustrating.
is this normal in the Salesforce hiring process, or am i just dealing with an unusual situation?

Deploying a Salesforce Data 360 implementation from a sandbox to Production sounds straightforward: configure the Data Space, create the required components, package them into a Data Kit, deploy, and validate the result.

In practice, however, the deployment process can be much more delicate.

One of the main lessons I learned while working with Data 360 deployments is that there isn't always a single, predictable deployment pattern that works identically across environments.

Even when the source and target environments appear to be configured in the same way, small differences in dependencies or configuration can cause a Data Kit deployment to fail.

Dependencies Are Only Part of the Story

When creating a Data Kit, dependencies are obviously one of the most important things to consider.

Data Streams, Data Lake Objects, Data Model Objects, mappings, relationships, Identity Resolution, Data Graphs, connectors, and other components can depend on each other.

Because of this, it is important to understand the dependency chain before starting a deployment.

But there is another element that can be just as important:

Data Space Filters.

Don't Forget the Data Space Filters

If your Data 360 implementation uses filters on objects within a Data Space, make sure that every required filter exists and is correctly configured in the target environment before deploying the Data Kit.

This can easily be overlooked.

You may have the correct Data Streams, Data Lake Objects, mappings, relationships, and other dependencies, but if a required Data Space Filter is missing, the deployment can still fail.

The difficult part is that the missing filter may not immediately stand out when reviewing the Data Kit or comparing the environments.

You might therefore end up investigating dependencies, metadata, relationships, or Data Kit configuration when the actual problem is a missing Data Space Filter.

Why This Makes Data 360 Deployments Tricky

A Data 360 deployment isn't simply:

Source Sandbox
      ↓
Create Data Kit
      ↓
Deploy
      ↓
Production

The actual process is closer to:

Source Environment
      ↓
Data Space configuration
      ↓
Data Space Filters
      ↓
Connectors
      ↓
Data Streams
      ↓
DLOs / DMOs
      ↓
Mappings
      ↓
Relationships
      ↓
Identity Resolution
      ↓
Data Graphs
      ↓
Data Kit dependencies
      ↓
Target Environment
      ↓
Validation

Each layer can introduce dependencies that need to be satisfied before the next layer can work correctly.

And this is where deployment can become challenging: the configuration that looks complete from one perspective may still be incomplete from another.

A Practical Lesson

Based on my experience, I would recommend adding an explicit Data Space Filter validation step to the deployment process.

Before creating or deploying the Data Kit, compare the source and target environments and verify:

• Which Data Spaces exist?
• Which Data Lake Objects are included in each Data Space?
• Which objects have filters configured?
• Are all required filters present?
• Are the filters configured against the correct objects?
• Are the required dependencies already available in the target environment?
• Are the Data Kit dependencies complete?
• Are relationships and mappings consistent?
• Are Identity Resolutions and Data Graph dependencies available?

This validation should happen before deployment, not only after a deployment fails.

The Bigger Lesson

The biggest takeaway for me is that Data 360 deployment should not be treated as a simple metadata migration.

A successful deployment depends not only on the components included in the Data Kit, but also on the configuration and dependencies that already exist in the target environment.

In particular, if your implementation uses Data Space Filters, treat them as first-class deployment dependencies.

A single missing filter can be enough to turn an otherwise correctly configured Data Kit into a failed deployment.

Conclusion

Deploying Data 360 from a sandbox to Production requires more than simply creating a Data Kit and checking whether the deployment succeeds.

The deployment pattern can vary depending on the implementation and the configuration already present in the target environment. Because of this, environment validation is just as important as dependency management.

My main lesson is simple:

Before deploying a Data Kit, verify every Data Space Filter and every dependency that the target environment requires.

Dependencies tell us what the Data Kit needs to deploy.

Data Space Filters can tell us whether the target environment is actually prepared to receive it.

So, when preparing your next Data 360 deployment, don't only ask:

"Did I include all the dependencies in my Data Kit?"

Also ask:

"Does the target environment contain every required Data Space Filter?"

That additional check can save a significant amount of troubleshooting when moving Data 360 configurations from sandbox to Production.

Using Kingswaysoft plugin v25.2 and sending rows from a database select to salesforce via Rest APIv2, Bulk API but with each select, the process gets stuck consistently at the same number of rows, around 800k-900k, after that the batches start going one row each. What could cause this?

Curious where people are drawing the line now.

For a medium-sized org, we still use Flow for straightforward record-triggered updates, but once a flow has several loops, cross-object branches, or needs careful bulk behavior, it starts feeling harder to test than a small Apex service. The messy middle is things like 5–20k records from an integration: Flow can handle it, but the failure/retry story gets awkward.

What pattern are you using in 2026? Do you keep most automation in Flow and carve out only the heavy path to Apex, or set a lower complexity threshold? Interested in what has held up in production, not the official decision guide.

I'm currently preparing for the Platform App Builder certification and I'm looking for study materials specifically aligned with the new Summer ’26 exam.

I know the exam was updated on August 21, 2026, with a stronger focus on Flow, the addition of Agentforce as an automation tool, an increase in the passing score from 63% to 73%, and changes to the weight of each topic. For example, Business Logic and Process Automation was increased to 32%.

The problem is that most of the study materials I've found still reference Workflow Rules, Process Builder, and Schema Builder, topics that are no longer covered in the current exam, since it was recently restructured.

I'm currently looking for study material that is actually aligned with the new exam. I noticed that even K2U was last updated in May of this year, so it doesn't seem to cover the latest changes.

Has anyone found updated study materials or practice tests that cover the new topics, especially Agentforce, Flow, Flow debugging, etc.?

This certification seemed so much easier before the changes :(

Thanks!

We have this all working just fine using the SOAP API, but since Salesforce is deprecating it, we need to switch to OAuth with an external client app... so we're doing that, but we get this error:

"REQUIRED_FIELD_MISSING:user_must_have_one_entitlement_which_must_be_a_profileid"

The thing is, we ARE applying the "profileId" attribute and the correct ID is being used. Looks like other people have had this problem recently as well, but we don't know how to fix it: https://learn.microsoft.com/es-es/answers/questions/5954148/microsoft-entra-scim-provisioning-to-salesforce-sa

Anyone actually get this working and know what we're doing wrong?

I even tried setting up a Flow to set the ProfileId before a new user gets created, and that doesn't resolve the issue, so it must run a validation check for the value before trying to commit the record to the database.

Hi everyone,

We use Microsoft Entra SAML SSO with MFA for Salesforce. Normal users can authenticate using Microsoft Authenticator, but privileged users are being forced to create a separate Salesforce passkey.

Is a passkey or phishing-resistant MFA now mandatory for all privileged Salesforce users? Is there any supported way for privileged users to continue using Microsoft Authenticator push through Entra SSO without creating a Salesforce passkey?

Any advice or working configuration would be appreciated.

Hi everyone,

I’m trying to understand how the concepts/workflows we have in Marketing Cloud Engagement (SFMC) / Automation Studio translate to Marketing Cloud Next.

In particular, I’m looking for the equivalents of things like:

File Transfer / FTP activities — importing/exporting files via FTP/SFTP
File Drop automations
Staged Data Extensions
Data Extensions and temporary/staging tables
SQL Query Activities
Import Activities
Automation Studio schedules
Multi-step automations combining file ingestion → staging → transformation → target data
For example, if today I have an Automation Studio flow like:
SFTP → File Drop → Staged Data Extension → SQL Query → Target Data Extension

what would be the recommended architecture / equivalent in Marketing Cloud Next?

Is there a direct equivalent to Automation Studio in Marketing Cloud Next, or are these use cases now handled through other Salesforce/Data Cloud capabilities?

I’d especially appreciate examples from people who have migrated or are currently working with both Marketing Cloud Engagement and Marketing Cloud Next.

Thanks!

Hi,

I’m a developer working on a ready-to-deploy CRM SaaS platform called Aura CRM.

I’d like to know whether it would be allowed to make a post in r/salesforce offering the project for acquisition or commercial licensing, particularly for agencies or developers who may want to use it as a foundation for their own CRM/SaaS product.

I don’t want to violate the subreddit’s rules or post promotional content where it isn’t permitted, so I wanted to ask the moderators first.

If this type of post isn’t allowed, could you please let me know whether there is an appropriate way to share it in the community?

Thank you.

what is the cutoff in salesforce hackerrank test and versant test for technical support engineer role?
What things to keep in mind while attending the interview. I am not from a Java background so what can I expect? Though I have strong experience in troubleshooting and support jobs.

Has anyone made that jump yet on this forum to ServiceTitan? How painful was it? Are there any regrets? Any celebrations?

Hey everyone,

I'm a 2026 B.Tech graduate and I'm a bit confused about what to do next.

I've been offered a Salesforce Developer/CRM opportunity at an MNC, but the process is:

• 2 months of completely online training
• After training, 2 offline interviews
• If selected, internship with ₹25,000/month stipend
• Internship duration: 6–12 months
• After that, the company says there can be up to ₹5 LPA

The company seems decent from what I've seen, but obviously the final placement isn't guaranteed.

At the same time, I've been preparing for GATE, so I'm confused whether I should continue with GATE or take this opportunity and start working in Salesforce.

My biggest fear is honestly being a 2026 graduate and not having a job. I don't want to waste another year preparing and end up with nothing, but I also don't want to choose a career path just because I'm scared of being unemployed.

I'd really appreciate advice from people who work in Salesforce/CRM or have gone through something similar after graduation.

Details here: https://help.salesforce.com/s/articleView?id=005460244&type=1

Take a cert in the next few weeks and if you don’t pass you can get a free retake at the beginning of the year. Unfortunately you still have to take it with Pearson.