Skip to main content

#Data Cloud79 discussing

Subject: Unable to Access PDF Content in Data Cloud Data Explorer Using Azure Blob Storage and UDLO

Product:

Salesforce Data Cloud 

Issue Type: Unstructured Data Ingestion / Search Index / Retriever Configuration

Description

We are implementing an integration to ingest PDF files from Dropbox into Salesforce Data Cloud through Azure Blob Storage.

We have configured the Azure Blob Storage connection, created the Unstructured Data Lake Object (UDLO) and Unstructured Data Model Object (UDMO), and configured the search index.

We have also implemented Azure Event Grid notifications and an Azure Function to notify Data Cloud when files are created or deleted in Azure Blob Storage. The ingestion API returns accepted: true, indicating that Data Cloud has accepted the notification.

However, we are unable to verify that the actual PDF content has been processed and made available for retrieval through Agentforce.

The UDLO displays the PDF file metadata, and the search index status is Ready. However, we need clarification on how to access and validate the extracted PDF content and generated chunks in Data Cloud.

We found a similar issue discussed in the Salesforce Trailblazer Community: Not able to ingest the Unstructured (PDF) data from Azure.

Current Configuration

  1. Azure Blob Storage is configured as the source for PDF files.
  2. The Azure connection and SAS authentication are configured.
  3. A UDLO is created with the directory path and PDF file pattern (*.pdf).
  4. The UDLO is mapped to a UDMO.
  5. A search index is configured on the UDMO.
  6. Azure Event Grid is configured to notify Data Cloud about file creation and deletion events.
  7. An Azure Function sends notifications to the Data Cloud unstructured ingestion API.
  8. The API returns accepted: true.
  9. PDF file metadata is visible in the UDLO, and the search index status is Ready.

Issue

Although the PDF files are listed in the UDLO and the search index shows a Ready status, we need to confirm whether the actual PDF text has been extracted, processed into searchable chunks, and made available through the configured retriever.

We also need guidance on how to validate the generated chunks and retrieve the PDF content through Agentforce.

Assistance Required

Could you please help us clarify the following?

  1. What is the expected processing workflow for extracting PDF content from Azure Blob Storage when using a UDLO and UDMO?
  2. How can we verify that the PDF text has been extracted and that the corresponding chunk objects have been generated successfully?
  3. Is it expected that unstructured PDF content will not appear in Data Explorer in the same way as structured CSV data?
  4. If the search index status is Ready but the PDF content is not retrievable, what additional configurations or troubleshooting steps should we perform?
  5. How should we configure and validate the search index and retriever so that Agentforce can retrieve relevant PDF content and generate answers based on the documents?
  6. Are there any additional permissions, file notification requirements, ingestion steps, or processing delays that could prevent PDF content from becoming searchable?

Expected Outcome

We would like to confirm that PDF content is being processed correctly in Data Cloud and obtain the recommended configuration and troubleshooting steps required to retrieve relevant PDF passages through Agentforce.

Please provide guidance on the expected behavior of UDLO, UDMO, search index processing, generated chunks, and retriever configuration for unstructured PDF data stored in Azure Blob Storage. 

 

#Salesforce Developer  #Data Cloud

2 answers
  1. Today, 11:19 AM

    This is a detailed issue. Checking the PDF extraction process, generated chunks, and retriever configuration would be a good starting point to identify why Agentforce cannot retrieve the content. 

0/9000

We're in the process of connecting Salesforce Data Cloud to our WordPress marketing site to track web engagement — page activity, CTA clicks, and form fills. We want to capture behavioral data and properly identify visitors when they submit a form, so we can stitch browsing activity to an identified record. (All tied to our Marketing Cloud Next/Advanced platform analytics)    We're evaluating two paths and would love input from anyone who has been through this decision, particularly in a B2B context.     

 

Path 1 — GA4 Connector

Path 2 — Manual Web SDK with Sitemap     

 

Questions   

1. For a B2B site focused purely on page engagement and form fills, does the GA4 connector capture an equivalent level of behavioral data compared to a manually configured sitemap? Or are there meaningful gaps?     

 

2. Identity resolution is our biggest concern with the GA4 path. When a visitor submits a form, will the GA4 connector properly stitch that known identity (email) back to their anonymous browsing history?    

 

3. Are there any known data quality issues with the GA4 path — such as bot traffic inflating profiles?     

 

4. For those who've implemented the manual SDK: how complex was the sitemap build for a non-ecommerce site? We'd need to cover page type definitions, button/CTA clicks, and form submission events (for our MCA forms)       

 

@* Data 360 *

  

 

#Data Cloud

4 answers
  1. Yesterday, 10:34 AM

    Hi  @Chris Mauro

    , 

     

     

    Hidden fields are a valid approach and probably the cleanest one in your setup.  

    Here is what I would use: 

    1. Read the Web SDK’s anonymous device ID on the main site and pass it into the MC Next form, either as a URL parameter on the CTA link or written into a hidden field. The form submission then carries both the email and the device ID, and identity resolution in Data Cloud can link the anonymous browsing to the known individual. You don’t need the SDK to fire an identity event on the subdomain.
    2. Also check the SDK’s cookie domain setting. If the cookie is set on the root domain (yourdomain.com) and not the www host, the subdomain can read the same device ID. If you’re allowed to load the SDK on the form subdomain too, that closes the gap completely.
    3. For the Pardot-style activity view on Lead/Contact, Data Cloud won’t surface it the way Pardot did. Use a Related List enrichment on the engagement DMO to show page views in sequence, and a Copy Field enrichment for single values like first referrer.

    Test it with one form first. Verify that the device ID lands on the submission record and that the unified profile actually merges before rolling out. 

     

0/9000

Hi Everyone, 

 

Could you please assist me about this Semantic Model Error "

An error occurred in Semantic Layer. TraceId: 625f999de597e45396ce7008b8c62eb6 Status: INVALID_ARGUMENT Error Message: QUERY_EXECUTOR|table "ssot__Account__dlm" does not exist Caused by: line 1, column 2420: ...yee_Count4" from "ssot__Account__dlm" as "Account4" li... ^~~~~~~~~~~~~~~~~~~~".

Thanks,

 

#Data Cloud

1 answer
  1. Oct 22, 3:39 PM

    The "table does not exist" error typically indicates that the target Data Lake Object (DLO) name referenced in your query or semantic model needs to be verified, or the data stream mapping needs to be refreshed. Please double-check that `ssot__Account__dlm` is correctly mapped and active in your Data Cloud org.

0/9000

Trailhead のモジュール「Data Graphs in Data 360」の単元「Create a Data Graph」で、ハンズオン Challenge に合格できず困っています。 

 

■ 表示されるエラー 

Challenge は Data Cloud ではまだ完了していません 

Make sure the my_test_dg data graph is saved, built, and active. If the Last Run Status still shows 'Processing', wait a few more minutes. 

 

■ データグラフの状態(Data Graphs の一覧) 

・Data Graph Label / API Name:my_test_dg 

・Primary DMO:Individual 

・Data Space:default 

・Status:Active 

・Last Run Status:Success 

・Type:Standard Data Graph 

・Schedule:Weekly 

・Record Count:1,467 

 

■ データグラフの構成 

・Individual(主 DMO):Birth Date、Created Date、First Name、Last Name 

・Contact Point Address(Individual の下) 

・Account Contact(Contact Point Address の下):Account、Contact Email、Last Activity Date 

 

■ 実施したこと 

・ブラウザのキャッシュクリア 

・データグラフの再構築(Save and Build) 

・データグラフを削除し、教材の手順通りに作り直し 

・Challenge 欄の「起動」ボタンから開いた組織で作業していることを確認 

・ステータスが Active / Success になり、処理中のアイコンが消えてから Challenge を確認  

・Trailheadを言語設定を英語に変更し確認

 

■ 環境 

・Data Cloud Playground(期限:2026/10/20 午前9:00 JST) 

 

同じ現象に遭遇された方や、解決方法をご存じの方がいらっしゃいましたら、ご教示いただけますと幸いです。 

よろしくお願いいたします。 

Trailhead「Create a Data Graph」の Challenge が Active ですが合格しませんimage.png

 

image.png

 

#Trailhead Challenges  #Trailhead  #Data Cloud  #Data360  #Salesforce Developer  #Salesforce_developer

1 answer
0/9000

Hi Trailblazers, 

 

I recently started learning about Salesforce Data 360 on Trailhead.

From what I understand, Salesforce CRM already stores customer information in objects like Accounts, Contacts, and Opportunities.

Data 360 also works with customer data from different sources.

My question is, if customer information is already available in Salesforce CRM, why do companies need Data 360?

Can someone explain with a simple real-world business example? 

 

Thanks for sharing your knowledge! 

 

#Data Cloud

1 answer
  1. Oct 19, 10:40 AM

    Hi @Chaitanya d

    Good question! Salesforce CRM and Data 360 both work with customer data, but they serve different purposes. 

     

    1. Salesforce CRM – Manage Customer Relationships

    • Stores Accounts, Contacts, Leads, Opportunities, and Cases.
    • Helps Sales and Service teams manage daily business activities.
    • Mainly works with customer information available in Salesforce.

    2. Data 360 – Unify Customer Data

    • Connects customer data from Salesforce and external systems.
    • Uses Identity Resolution to link records belonging to the same customer.
    • Creates unified customer profiles.
    • Supports segmentation, insights, personalization, and AI use cases.

    3. Simple Real-World Example

    Consider an e-commerce company:

    • Salesforce CRM: Stores customer contact details and support cases.
    • Website: Stores browsing activity.
    • Order Management System: Stores purchase history.
    • Marketing System: Stores campaign interactions.

    Without Data 360, this information may remain in separate systems.

    With Data 360, the company can connect these sources and build a unified customer view.

    For example, when a customer contacts support, the agent can see their purchase history, recent interactions, and support cases if those data sources are connected and configured. 

     

    4. When Do We Need Data 360?

    • When customer data exists across multiple systems.
    • When we need Identity Resolution and unified profiles.
    • When businesses need advanced segmentation and personalized experiences.
    • When Agentforce needs additional customer context from multiple sources.

    If your requirement is simply managing Accounts, Contacts, Opportunities, and Cases, Salesforce CRM may be enough.

    If your requirement is connecting data from different systems and creating a unified customer view, Data 360 becomes valuable. 

     

    Simple Difference:

    Salesforce CRM = Manage customer relationships.

    Data 360 = Connect, unify, and activate customer data. 

     

    Salesforce Documentation:

    Related Trailblazer Discussions:

    1. Why Do We Need Data Cloud When We Already Have Data Storage?
    2. Data Cloud vs CRM Analytics
    3. How Does Data 360 Help Agentforce?

    Hope this helps you understand the difference!

0/9000

Builder Central (Beta) shows "Failed to fetch" on the welcome screen in my Agentforce Developer Edition org. 

Console: CORS preflight to

https://api.salesforce.com/platform/dx-workspaces-proxy/v1/api/workspaces-proxy/<workspaceId>/project-service/v1/projects

 

is blocked. The OPTIONS response is HTTP 204 with no Access-Control-Allow-Origin header. 

Reproducible with curl from outside the browser for any Origin, so it is not a client-side issue. 

"Connect an org" logs in successfully but never returns to the app. 

Org: nexustrade-portal-dev-ed, Home Org ID 00DgK00000WFVD2. Workspace ID: b6f6c7b9-dd87-4ca5-99e9-0d74efb79d36. 

 

Builder Central (Beta) shows

 

 

image.png

 

 

 

I have followed the steps - 

  1. enable data cloud - https://help.salesforce.com/s/articleView?id=data.c360_a_setup_provision.htm&type=5
  2. enable einstein AI from einstein setup - https://help.salesforce.com/s/articleView?id=ai.generative_ai_enable.htm&type=5
  3. enable agentforce - 
  4. enable builder central
  5. assign permission set - Access Builder Central
  6. launch the builder central(beta) from setup

 

 

 

#Agentforce  #Salesforce Developer  #Data Cloud  #AI

5 answers
0/9000

Salesforce Developer Roadmap in the AI Era (2026–2027) 

 

Hi Trailblazers, 

 

With Agentforce, Data 360, and AI becoming part of Salesforce development, I believe developers need to strengthen their core technical skills while learning new AI capabilities.

Here is my suggested learning roadmap for beginners and working professionals. 

 

1. Salesforce Fundamentals

  • CRM basics, Objects, Fields, and Relationships
  • Security, OWD, Profiles, and Permission Sets
  • Flow Builder and Validation Rules

Salesforce Developer Beginner

 

2. SOQL & Apex Development

  • SOQL, SOSL, and Relationship Queries
  • Apex Classes, Triggers, and Collections
  • Bulkification, Governor Limits, and Test Classes

Apex Basics

 

3. Lightning Web Components (LWC)

  • HTML, CSS, and JavaScript
  • Components, Events, and Data Binding
  • LWC with Apex and Lightning Data Service

LWC Developer Guide

 

4. Salesforce Integrations

  • REST APIs, JSON, and OAuth 2.0
  • Named Credentials and Apex Callouts
  • Platform Events and Asynchronous Apex

Apex Integration Services

 

5. Salesforce DevOps

  • VS Code, Salesforce CLI, and Git
  • Sandboxes, Deployments, and Testing
  • CI/CD and DevOps Center

Salesforce CLI

 

6. Agentforce & AI Development

  • Agentforce Builder, Subagents, and Actions
  • Agent Script and Prompt Builder
  • Apex and Flow Actions for AI Agents
  • Agent Testing, Security, and Debugging

Agentforce Developer Guide

 

7. Data 360

  • Data Streams and Data Lake Objects
  • Data Mapping and Identity Resolution
  • Data Graphs, Retrieval, and Agentforce Grounding

Data 360 Learning

Data 360 Overview – SFDCINDIA

 

8. Real-World Projects

Build at least three practical projects:

  1. Student Management System – Admin, Apex, and LWC
  2. Customer Support Application – Flow, Integration, and Security
  3. AI Customer Support Agent – Agentforce, Actions, and Data 360

My Suggestion:

Don't jump directly into Agentforce without understanding Salesforce fundamentals.

Learn Salesforce → SOQL → Apex → LWC → Integration → DevOps → Agentforce → Data 360

AI can help developers write code faster, but understanding business requirements, architecture, security, debugging, and testing will remain essential.

My Key Point: The future belongs to developers who understand both Salesforce Development and AI.

What skills are you focusing on to stay relevant in the AI era? 

 

Venkat Yadav | SFDCINDIA

#Salesforce Developer #Salesforce Admin #Agentforce #Data Cloud #TrailblazerCommunity #Trailhead Challenges

1 comment
0/9000

Hi Trailblazers!  

 

If you're planning to learn Salesforce Data 360 (formerly Data Cloud), here is a simple roadmap to follow.

1. Data 360 Basics

  • Introduction, Architecture and Real-Time Use Cases
  • Key Features and Benefits

2. Setup and Data Ingestion

  • Data 360 Setup and Permissions
  • Data Spaces, Data Streams and Connectors
  • Zero Copy Integration

3. Data Modelling

  • Data Lake Objects (DLOs)
  • Data Model Objects (DMOs)
  • Data Mapping and Transformations

4. Identity Resolution

  • Matching and Reconciliation Rules
  • Unified Customer Profiles

5. Insights and Segmentation

  • Calculated Insights and Streaming Insights
  • Segments and Data Graphs

6. Data Activation

  • Activation Targets
  • Data Actions and Flow Integration

7. Data 360 with Agentforce

  • Structured and Unstructured Data
  • Search Indexes and Retrievers
  • Data Libraries and Agentforce Integration

8. Security and Practical Experience

  • Data Security and Governance
  • Monitoring and Troubleshooting
  • End-to-End Real-Time Project

 Useful Learning Resources 

 

🔗 SFDCINDIA – Salesforce Data 360 Blog

https://sfdcindia.com/blog/salesforce-data-360

 

🔗 Salesforce Data 360 Learning Trail

https://trailhead.salesforce.com/content/learn/trails/data-cloud-explore-setup-to-activation

 

🔗 Data 360 Fundamentals

https://trailhead.salesforce.com/content/learn/trails/unlock-your-data-with-data-cloud

 

🔗 Salesforce Official Documentation

https://help.salesforce.com/s/articleView?id=data.c360_a_data_ingestion.htm&type=5

 

🔗 Data 360 Consultant Certification

https://trailhead.salesforce.com/credentials/data360consultant

 

My Suggestion:

Start with Data Streams, DLOs, DMOs, and Data Mapping before moving to advanced topics.

Focus on hands-on practice and try to build at least one end-to-end project. 

 

 Which Data 360 topic do you find most challenging? 

 

Let's share our experiences and learn together! 

 

#Data Cloud #Data360 #Agentforce #Trailhead Challenges #TrailblazerCommunity

0/9000

Good afternoon everyone, how are you? 

 

We have some questions regarding the Repeater Block and its pre-configuration. We saw in a documentation that it was necessary to force a relationship between our Products DMO and the Individual to be able to associate it with the Data Graph, and then personalize our abandoned cart emails. 

 

The question we have is:  

 

Is this step of forcing a relationship really necessary? Or do we need a separate Data Graph for each function? Will each Data Graph work as an activation (comparing it to the use of Data Cloud activation to MCE) ? 

 

 

Thank you.

 

 

 

Repeater Block - MC Next

 

 

 

#Trailhead Challenges  #Trailhead  #Salesforce Developer  #Salesforce Admin  #Salesforce  #TrailblazerCommunity  #Data Cloud

2 answers
  1. Oct 15, 3:18 PM

    @Matt Thomas

    I have a few final considerations, if you could help me:  

     

    1 - When you mention "product", are you referring to the Product Catalog DMO or Shopping Cart Product Engagement? 

     

    2- Do we need to create a relationship linking Shopping Cart Product Engagement to the Product DMO?

     

    3- Individual > Shopping Cart Engagement > Shopping Cart Product Engagement > Product. Is this the expected structure? Or does there need to be a direct relationship between Product and Individual? Can it be indirect?

0/9000

Hi Trailblazers, 

 

Salesforce Data 360 helps businesses bring customer data from different sources into one place. 

 

But I'm interested in understanding its real value beyond data integration. 

 

From your project experience, which features provide the most business value: Identity Resolution, Data Graphs, Segmentation, or real-time data activation? 

 

How important is Data 360 for making Agentforce agents more accurate and useful? 

 

Would love to learn from your implementation experiences. 

 

#Data Cloud #Agentforce  #Data360

1 answer
  1. Oct 15, 8:39 AM

    Hi @Galla Rajeswari Good question! Data 360 is not just about combining data. It helps businesses use customer data more effectively.

    1. Identity Resolution: Combines customer records to create a unified customer profile.
    2. Data Graphs: Connect related data and help Agentforce access customer information faster.
    3. Segmentation: Helps businesses identify and target the right customers.
    4. Real-Time Activation: Helps businesses take action using updated customer data.
    5. Agentforce: Provides better customer context, helping agents give more relevant and accurate responses.

    In my view, Identity Resolution and Data Graphs are especially useful for Agentforce.

    Key Point: Data 360 provides the right customer context, while Agentforce uses that data to answer questions and perform actions.

0/9000