Data Streams Archives - gettectonic.com
Public Group vs Queue in Salesforce
14Apr

Transforming Crisis Management with Intelligent Technology

Transforming Crisis Management with Intelligent Technology In high-pressure disaster scenarios where every second counts, AI is emerging as a force multiplier for response teams. From predictive analytics to real-time decision support, artificial intelligence is revolutionizing how organizations prepare for, manage, and recover from catastrophic events. Here are seven pivotal areas where AI delivers measurable impact across the disaster lifecycle. Here is a new Public Sector Solution from AI 1. Predictive Scenario Planning & Stress Testing AI Advantage: Dynamically generates realistic disaster simulations 2. Autonomous Response Systems AI Advantage: Subsecond reaction times with precision execution 3. Intelligent Log Analysis & Threat Detection AI Advantage: Pattern recognition across petabyte-scale telemetry 4. Crisis Communication Orchestration AI Advantage: Multi-channel coordination at scale 5. Real-Time Situational Awareness AI Advantage: Fusion of disparate data streams 6. Resource Optimization Engine AI Advantage: Calculates optimal recovery sequences 7. Continuous Improvement Loop AI Advantage: Institutionalizes lessons learned Implementation Roadmap The Future of AI in Disaster Response Emerging capabilities include: While AI won’t replace human judgment in crises, it’s becoming an indispensable force multiplier. Organizations adopting these tools gain measurable advantages in response speed, resource efficiency, and long-term resilience building. The key lies in strategic implementation – using AI where it excels while maintaining human oversight where nuance matters most. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

Read More
April 14, 2025in Artificial Intelligence, Data, Enterprise, Technology0 Comments
AI Agents Are the Future of Enterprise
05Mar

AI Agents Are the Future of Enterprise

AI Agents Are the Future of Enterprise—But They Need the Right Architecture AI agents are poised to revolutionize enterprise operations with autonomous problem-solving, adaptive workflows, and scalability. However, the biggest challenge isn’t improving models—it’s building the infrastructure to support them. Agents require seamless access to data, tools, and the ability to share insights across systems—with outputs usable by multiple services, including other agents. This isn’t just an AI challenge; it’s an infrastructure and data interoperability problem. Traditional approaches—like chaining commands—won’t cut it. Instead, enterprises need an event-driven architecture (EDA) powered by real-time data streams. As HubSpot CTO Dharmesh Shah put it, “Agents are the new apps.” To unlock their potential, businesses must invest in the right design patterns from the start. This insight explores why EDA is critical for scaling AI agents and integrating them into modern enterprise systems. The Evolution of AI: From Predictive Models to Autonomous Agents AI has progressed through three key waves, each overcoming—but also introducing—new limitations. 1. The First Wave: Predictive Models Early AI relied on traditional machine learning (ML) for narrow, domain-specific tasks. These models were rigid, requiring extensive retraining for new use cases. Limitations: 2. The Second Wave: Generative AI Generative AI, powered by large language models (LLMs), introduced general-purpose intelligence. Unlike predictive models, LLMs could handle diverse tasks—from text generation to code synthesis. Limitations: For example, asking an LLM to recommend an insurance policy based on a user’s health history fails—unless the model can dynamically retrieve personal data. 3. The Third Wave: Compound AI & Agentic Systems To overcome these gaps, Compound AI systems combine LLMs with: But even RAG has limits—it relies on fixed workflows, making it inflexible for dynamic tasks. Enter AI agents: autonomous systems that reason, plan, and adapt in real time. Why Agents Are the Next Frontier Salesforce CEO Marc Benioff recently noted that LLMs are hitting their limits, and the future lies in autonomous agents. Unlike static models, agents: Key Agent Design Patterns These patterns enable Agentic RAG, where retrieval isn’t fixed but adaptive—agents decide what data to fetch based on context. The Scaling Challenge: It’s an Infrastructure Problem Agents need real-time data access and seamless interoperability—but connecting them via APIs creates tight coupling, leading to: The Solution: Event-Driven Architecture (EDA) EDA decouples agents using asynchronous event streams (e.g., Kafka, Redpanda). Benefits:✅ Loose coupling – Agents communicate without direct dependencies.✅ Real-time reactivity – Instant responses to changing data.✅ Scalability – New agents join without redesigning the system.✅ Resilience – Failures don’t cascade. Example: An agent analyzing customer data publishes an event—other agents, CRMs, or analytics tools consume it without explicit coordination. Why EDA is the Future for AI Agents Just as microservices replaced monoliths, EDA will replace rigid AI pipelines. Early adopters (like Facebook with scalable infrastructure) outcompeted those that couldn’t scale (like Friendster). The same will happen with AI agents. Enterprises that embrace event-driven agents will: The Bottom Line AI agents are the next evolution of enterprise software—but without EDA, they’ll hit a wall. Companies that invest in event-driven infrastructure today will lead the next wave of AI innovation. The rest? They’ll struggle to keep up. Ready to future-proof your AI strategy? AI Agents Are the Future of Enterprise. The time to build for agents is now. Contact Tectonic today. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

Read More
March 5, 2025in Agentforce Platform, Data, Enterprise, Generative AI, Salesforce

Data Cloud Billable Usage

Data Cloud Billable Usage Overview Usage of certain Data Cloud features impacts credit consumption. To track usage, access your Digital Wallet within your Salesforce org. For specific billing details, refer to your contract or contact your Account Executive. Important Notes ⚠️ Customer Data Platform (CDP) Licensing – If your Data Cloud org operates under a CDP license, refer to Customer Data Platform Billable Usage Calculations instead.⚠️ Sandbox Usage – Data Cloud sandbox consumption affects credits, with usage tracked separately on Data Cloud sandbox cards. Understanding Usage Calculations Credit consumption is based on the number of units used multiplied by the multiplier on the rate card for that usage type. Consumption is categorized as follows: 1. Data Service Usage Service usage is measured by records processed, queried, or analyzed. Billing Category Description Batch Data Pipeline Based on the volume of batch data processed via Data Cloud data streams. Batch Data Transforms Measured by the higher of rows read vs. rows written. Incremental transforms only count changed rows after the first run. Batch Profile Unification Based on source profiles processed by an identity resolution ruleset. After the first run, only new/modified profiles are counted. Batch Calculated Insights Based on the number of records in underlying objects used to generate Calculated Insights. Data Queries Based on records processed, which depends on query structure and total records in the queried objects. Unstructured Data Processed Measured by the amount of unstructured data (PDFs, audio/video files) processed. Streaming Data Pipeline Based on records ingested through real-time data streams (web, mobile, streaming ingestion API). Streaming Data Transforms Measured by the number of records processed in real-time transformations. Streaming Calculated Insights Usage is based on the number of records processed in streaming insights calculations. Streaming Actions (including lookups) Measured by the number of records processed in data lookups and enrichments. Inferences Based on predictive AI model usage, including one prediction, prescriptions, and top predictors. Applies to internal (Einstein AI) and external (BYOM) models. Data Share Rows Shared (Data Out) Based on the new/changed records processed for data sharing. Data Federation or Sharing Rows Accessed Based on records returned from external data sources. Only cross-region/cross-cloud queries consume credits. Sub-second Real-Time Events & API Based on profile events, engagement events, and API calls in real-time processing. Private Connect Data Processed Measured by GB of data transferred via private network routes. 🔹 Retired Billing Categories: Accelerated Data Queries and Real-Time Profile API (no longer billed after August 16, 2024). 2. Data Storage Allocation Storage usage applies to Data Cloud, Data Cloud for Marketing, and Data Cloud for Tableau. Billing Category Description Storage Beyond Allocation Measured by data storage exceeding your allocated limit. 3. Data Spaces Billing Category Description Data Spaces Usage is based on the number of data spaces beyond the default allocation. 4. Segmentation & Activation Usage applies to Data Cloud for Marketing customers and is based on records processed, queried, or activated. Billing Category Description Segmentation Based on the number of records processed for segmentation. Batch Activations Measured by records processed for batch activations. Activate DMO – Streaming Based on new/updated records in the Data Model Object (DMO) during an activation. If a data graph is used, the count is doubled. 5. Ad Audiences Service Usage Usage is calculated based on the number of ad audience targets created. Billing Category Description Ad Audiences Measured by the number of ad audience targets generated. 6. Data Cloud Real-Time Profile Real-time service usage is based on the number of records associated with real-time data graphs. Billing Category Description Sub-second Real-Time Profiles & Entities Based on the unique real-time data graph records appearing in the cache during the billing month. Each unique record is counted only once, even if it appears multiple times. 📌 Example: If a real-time data graph contains 10M cached records on day one, and 1M new records are added daily for 30 days, the total count would be 40M records. 7. Customer Data Platform (CDP) Billing Previously named Customer Data Platform orgs are billed based on contracted entitlements. Understanding these calculations can help optimize data management and cost efficiency. Track & Manage Your Usage 🔹 Digital Wallet – Monitor Data Cloud consumption across all categories.🔹 Feature & Usage Documentation – Review guidelines before activating features to optimize cost.🔹 Account Executive Consultation – Contact your AE to understand credit consumption and scalability options. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

Read More
February 26, 2025in Data, Salesforce, Salesforce Data Cloud

Autonomy, Architecture, and Action

Redefining AI Agents: Autonomy, Architecture, and Action AI agents are reshaping how technology interacts with us and executes tasks. Their mission? To reason, plan, and act independently—following instructions, making autonomous decisions, and completing actions, often without user involvement. These agents adapt to new information, adjust in real time, and pursue their objectives autonomously. This evolution in agentic AI is revolutionizing how goals are accomplished, ushering in a future of semi-autonomous technology. At their foundation, AI agents rely on one or more large language models (LLMs). However, designing agents is far more intricate than building chatbots or generative assistants. While traditional AI applications often depend on user-driven inputs—such as prompt engineering or active supervision—agents operate autonomously. Core Principles of Agentic AI Architectures To enable autonomous functionality, agentic AI systems must incorporate: Essential Infrastructure for AI Agents Building and deploying agentic AI systems requires robust software infrastructure that supports: Agent Development Made Easier with Langflow and Astra DB Langflow simplifies the development of agentic applications with its visual IDE. It integrates with Astra DB, which combines vector and graph capabilities for ultra-low latency data access. This synergy accelerates development by enabling: Transforming Autonomy into Action Agentic AI is fundamentally changing how tasks are executed by empowering systems to act autonomously. By leveraging platforms like Astra DB and Langflow, organizations can simplify agent design and deploy scalable, effective AI applications. Start building the next generation of AI-powered autonomy today. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

Read More
January 11, 2025in Agentic AI, Data, Enterprise, Technology

Data Cloud Permissions

Data Cloud Standard Permission Sets Permission sets determine the level of access and visibility Salesforce users have to Data Cloud features and data. Salesforce provides six standard permission sets for Data Cloud, which administrators assign to users. While it is possible to create custom permission sets, it is generally recommended to use the standard ones, as they are updated automatically with each release. However, these can be combined with other Salesforce permission sets to expand user access as needed. Identifying Your Data Cloud License To check your org’s Data Cloud license: Important: For details on the transition from legacy standard permission sets to enhanced standard permission sets, see Data Cloud Permission Set Naming Changes During the Enhanced Security Migration Period. System Administrator Access System Administrators can manage and assign users within Setup and access Data Cloud Setup. They must have either the System Administrator user profile or permissions that grant access to Salesforce Setup. Note: A Data Cloud standard permission set is not required to access Data Cloud Setup. Data Cloud Standard Permission Sets General Data Cloud Permission Sets These permission sets can be assigned to users and combined with other Salesforce permission sets: Marketing-Specific Permission Sets Organizations with the Segmentation and Activation Add-On License, commonly used with Marketing Cloud Engagement and third-party applications, have additional marketing-specific permission sets: For details on the Segmentation and Activation Add-On License, contact your account executive. Feature Access by Permission Set The following table outlines the access levels for each permission set: | Feature | System Admin | Data Cloud Admin | Data Cloud User | Data Cloud Marketing Admin | Data Cloud Data Aware Specialist | Data Cloud Marketing Manager | Data Cloud Marketing Specialist | |—|—|—|—|—|—|—|| Data Cloud Setup | Full Access | | | | | || Data Space Management | Full Access | | | | | || Data Additions to a Data Space | | Full Access | | Full Access | Full Access | || Data Streams | | Full Access | View Only | Full Access | Full Access | View Only | View Only || Data Shares | | Full Access | View Only | View Only | Full Access | View Only | View Only || Data Lake Objects | | Full Access | View Only | Full Access | Full Access | View Only | View Only || Data Transforms | | Full Access | View Only | Full Access | Full Access | View Only | View Only || Data Model | | Full Access | View Only | Full Access | Full Access | View Only | View Only || Identity Resolution | | Full Access | View Only | Full Access | Full Access | View Only | View Only || Calculated Insights | | Full Access | View Only | Full Access | Full Access | View Only | View Only || Segments | | | Full Access | View Only | Full Access | Full Access | || Activation & Targets | | | Full Access | View Only | Full Access | View Only | || Communication Capping Setup | Full Access | | | | | || Search Index Configurations | | Full Access | View Only | Full Access | Full Access | View Only | View Only | Best Practice: Use Standard Permission Sets It is strongly recommended to assign standard permission sets rather than creating custom ones. Standard permission sets are automatically updated with each release, ensuring users have access to the latest features. Custom permission sets may not include new functionality, potentially limiting access to new capabilities. Customer Data Platform Standard Permission Sets Salesforce provides four standard permission sets for Customer Data Platform (CDP) licensed orgs. These define access to Data Cloud features under the CDP contract. For details, refer to Customer Data Platform Standard Permission Sets in Data Cloud documentation. Data Cloud Permission Set Changes During the Enhanced Security Migration Starting November 2023, Salesforce introduced enhanced security measures, renaming existing Data Cloud standard permission sets and creating new permission sets with similar names in all Data Cloud orgs. Refer to the Data Cloud Permission Set Naming Changes documentation for details. Creating Custom Permission Sets in Data Cloud If a custom permission set is required, it is best to clone an existing standard permission set rather than creating one from scratch. Some Data Cloud features may not be accessible if a permission set is built manually. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

Read More
January 4, 2025in Data, Salesforce, Salesforce Data Cloud
AI is revolutionizing BI by transforming it from a retrospective tool into a proactive, real-time decision-making engine.
30Dec

AI in Business Intelligence

AI in Business Intelligence: Applications, Benefits, and Challenges AI is rapidly transforming business intelligence (BI) by enhancing analytics capabilities and streamlining processes. This shift is reshaping how organizations leverage data for decision-making. Here’s an in-depth look at how AI complements BI, its advantages, and the challenges it introduces. The Evolution of Business Intelligence with AI BI has traditionally focused on aggregating historical and current data to provide insights into business operations—a process known as descriptive analytics. However, many decision-makers seek more: insights into future trends (predictive analytics) and actionable recommendations (prescriptive analytics). AI bridges this gap. With advanced tools like natural language processing (NLP) and machine learning (ML), AI enables businesses to move beyond static dashboards to dynamic, real-time insights. It also simplifies complex analytics, making data more accessible to business users and fostering more informed, proactive decision-making. Key Benefits of AI in Business Intelligence AI brings significant benefits to BI, including: Real-World Applications of AI in BI AI’s integration into BI goes beyond internal efficiency, delivering external value by enhancing customer experiences and driving business growth. Notable applications include: Challenges of AI in Business Intelligence Despite its potential, integrating AI into BI comes with challenges: Best Practices for AI-Driven BI To successfully integrate AI with BI, organizations should: Future Trends in AI and BI AI is expected to augment rather than replace BI, enhancing its capabilities while keeping human expertise central. Emerging trends include: Conclusion AI is revolutionizing BI by transforming it from a retrospective tool into a proactive, real-time decision-making engine. While challenges remain, thoughtful implementation and adherence to best practices can help organizations unlock AI’s full potential in BI. By integrating AI into existing BI workflows, businesses can drive innovation, improve decision-making, and create more agile and data-driven operations. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
December 30, 2024in AI Tools, Data, Generative AI
Mulesoft
04Nov

MuleSoft Empowering AI Agents

Empowering AI Agents with Real-Time Data: MuleSoft’s Full Lifecycle AsyncAPI Support MuleSoft has officially launched full lifecycle AsyncAPI support, providing organizations with the tools to connect real-time data to AI agents via event-driven architectures (EDAs). This integration empowers businesses to deploy AI agents that can autonomously act on dynamic, real-time events across various operations. MuleSoft Empowering AI Agents. AI Agents in Action with AsyncAPI The integration of Agentforce, Salesforce’s AI agent suite, with AsyncAPI takes automation to a new level. By utilizing real-time data streams, businesses can create AI agents capable of immediate, autonomous decision-making. Why AsyncAPI Matters Event-driven architectures are critical for real-time data processing, yet 43% of IT leaders struggle to integrate existing systems with their EDAs. AsyncAPI provides a scalable, standardized way to connect applications and AI agents, overcoming these challenges. Key Features of MuleSoft’s AsyncAPI Support Why It’s a Game-Changer for AI Agents AsyncAPI integration enables AI agents to function asynchronously within EDAs, meaning they can process tasks without waiting for updates. For example: Driving Innovation Across Industries Organizations in sectors like retail, IT, and financial services can leverage these capabilities: Expert Insights Andrew Comstock, VP of Product, Integration at Salesforce:“AI is reshaping how we think about modern architectures, but connectivity remains foundational. By supporting AsyncAPI, we’re empowering businesses to build event-driven, autonomous systems on a flexible and robust platform.” Maksim Kogan, Solution Architect, OBI Group Holding:“Integrating AsyncAPI into Anypoint Platform simplifies the developer experience and increases resilience, enabling real-time services that directly enhance customer satisfaction.” Availability MuleSoft’s full lifecycle AsyncAPI support is now available via the Anypoint Platform, with compatibility for Kafka, Solace, Anypoint MQ, and Salesforce Platform Events. Tools like Anypoint Code Builder and Anypoint Exchange further streamline the development process. MuleSoft Empowering AI Agents With full AsyncAPI support, MuleSoft unlocks the potential for AI agents to operate seamlessly within real-time event-driven systems. From improving customer experiences to enhancing operational efficiency, this innovation positions businesses to thrive in today’s fast-paced digital landscape. Learn more about empowering your AI agents with MuleSoft’s AsyncAPI capabilities today. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

Read More
November 4, 2024in Data, Mulesoft, Salesforce
Amazon DynamoDB to Salesforce Data Cloud
18Oct

Amazon DynamoDB to Salesforce Data Cloud

Ingesting Data from Amazon DynamoDB to Salesforce Data Cloud Salesforce Data Cloud serves as your organization’s digital command center, enabling real-time ingestion, unification, and activation of data from any source. By transforming scattered customer information into actionable insights, it empowers businesses to operate with unparalleled efficiency. Integrating Amazon DynamoDB with Salesforce Data Cloud exemplifies the platform’s capacity to unify and activate enterprise data seamlessly. Follow this step-by-step guide to ingest data from Amazon DynamoDB into Salesforce Data Cloud. Prerequisites Part 1: Amazon DynamoDB Setup 1. AWS Account Setup 2. Create a DynamoDB Table 3. Populate the Table with Data 4. Security Credentials Part 2: Salesforce Data Cloud Configuration 1. Creating the Data Connection 2. Configuring Data Streams Create a New Data Stream Configure the Data Model 3. Data Modeling and Mapping Custom Object Creation Conclusion After completing the setup: This integration underscores Salesforce Data Cloud’s role as a centralized hub, capable of harmonizing diverse data sources, ensuring real-time synchronization, and enabling actionable insights. By connecting Amazon DynamoDB, businesses can unlock the full potential of their data, driving better decision-making and customer experiences. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

Read More
October 18, 2024in Data, Enterprise, Salesforce, Salesforce Data Cloud
Third Wave of AI at Salesforce
16Oct

Third Wave of AI at Salesforce

The Third Wave of AI at Salesforce: How Agentforce is Transforming the Landscape At Dreamforce 2024, Salesforce unveiled several exciting innovations, with Agentforce taking center stage. This insight explores the key changes and enhancements designed to improve efficiency and elevate customer interactions. Introducing Agentforce Agentforce is a customizable AI agent builder that empowers organizations to create and manage autonomous agents for various business tasks. But what exactly is an agent? An agent is akin to a chatbot but goes beyond traditional capabilities. While typical chatbots are restricted to scripted responses and predefined questions, Agentforce agents leverage large language models (LLMs) and generative AI to comprehend customer inquiries contextually. This enables them to make independent decisions, whether processing requests or resolving issues using real-time data from your company’s customer relationship management (CRM) system. The Role of Atlas At the heart of Agentforce’s functionality lies the Atlas reasoning engine, which acts as the operational brain. Unlike standard assistive tools, Atlas is an agentic system with the autonomy to act on behalf of the user. Atlas formulates a plan based on necessary actions and can adjust that plan based on evaluations or new information. When it’s time to engage, Atlas knows which business processes to activate and connects with customers or employees via their preferred channels. This sophisticated approach allows Agentforce to significantly enhance operational efficiency. By automating routine inquiries, it frees up your team to focus on more complex tasks, delivering a smoother experience for both staff and customers. Speed to Value One of Agentforce’s standout features is its emphasis on rapid implementation. Many AI projects can be resource-intensive and take months or even years to launch. However, Agentforce enables quick deployment by leveraging existing Salesforce infrastructure, allowing organizations to implement solutions rapidly and with greater control. Salesforce also offers pre-built Agentforce agents tailored to specific business needs—such as Service Agent, Sales Development Representative Agent, Sales Coach, Personal Shopper Agent, and Campaign Agent—all customizable with the Agent Builder. Agentforce for Service and Sales will be generally available starting October 25, 2024, with certain elements of the Atlas Reasoning Engine rolling out in February 2025. Pricing begins at $2 per conversation, with volume discounts available. Transforming Customer Insights with Data Cloud and Marketing Cloud Dreamforce also highlighted enhancements to Data Cloud, Salesforce’s backbone for all cloud products. The platform now supports processing unstructured data, which constitutes up to 90% of company data often overlooked by traditional reporting systems. With new capabilities for analyzing various unstructured formats—like video, audio, sales demos, customer service calls, and voicemails—businesses can derive valuable insights and make informed decisions across Customer 360. Furthermore, Data Cloud One enables organizations to connect siloed Salesforce instances effortlessly, promoting seamless data sharing through a no-code, point-and-click setup. The newly announced Marketing Cloud Advanced edition serves as the “big sister” to Marketing Cloud Growth, equipping larger marketing teams with enhanced features like Path Experiment, which tests different content strategies across channels, and Einstein Engagement Scoring for deeper insights into customer behavior. Together, these enhancements empower companies to engage customers more meaningfully and measurably across all touchpoints. Empowering the Workforce Through Education Salesforce is committed to making AI accessible for all. They recently announced free instructor-led courses and AI certifications available through 2025, aimed at equipping the Salesforce community with essential AI and data management skills. To support this initiative, Salesforce is establishing AI centers in major cities, starting with London, to provide hands-on training and resources, fostering AI expertise. They also launched a global Agentforce World Tour to promote understanding and adoption of the new capabilities introduced at Dreamforce, featuring repackaged sessions from the conference and opportunities for specialists to answer questions. The Bottom Line What does this mean for businesses? With the rollout of Agentforce, along with enhancements to Data Cloud and Marketing Cloud, organizations can operate more efficiently and connect with customers in more meaningful ways. Coupled with a focus on education through free courses and global outreach, getting on board has never been easier. If you’d like to discuss how we can help your business maximize its potential with Salesforce through data and AI, connect with us and schedule a meeting with our team. Legacy systems can create significant gaps between operations and employee needs, slowing lead processes and resulting in siloed, out-of-sync data that hampers business efficiency. Responding to inquiries within five minutes offers a 75% chance of converting leads into customers, emphasizing the need for rapid, effective marketing responses. Salesforce aims to help customers strengthen relationships, enhance productivity, and boost margins through its premier AI CRM for sales, service, marketing, and commerce, while also achieving these goals internally. Recognizing the complexity of its decade-old processes, including lead assignment across three systems and 2 million lines of custom code, Salesforce took on the role of “customer zero,” leveraging Data Cloud to create a unified view of customers known as the “Customer 360 Truth Profile.” This consolidation of disparate data laid the groundwork for enterprise-wide AI and automation, improving marketing automation and reducing lead time by 98%. As Michael Andrew, SVP of Marketing Decision Science at Salesforce, noted, this initiative enabled the company to provide high-quality leads to its sales team with enriched data and AI scoring while accelerating time to market and enhancing data quality. Embracing Customer Zero “Almost exactly a year ago, we set out with a beginner’s mind to transform our lead automation process with a solution that would send the best leads to the right sales teams within minutes of capturing their data and support us for the next decade,” said Andrew. The initial success metric was “speed to lead,” aiming to reduce the handoff time from 20 minutes to less than one minute. The focus was also on integrating customer and lead data to develop a more comprehensive 360-degree profile for each prospect, enhancing lead assignment and sales rep productivity. Another objective was to boost business agility by cutting the average time to implement assignment changes from four weeks to mere days. Accelerating Success with

Read More
October 16, 2024in Data, Enterprise, Generative AI, Google, Google Analytics, Marketing Automation, Salesforce
Data Cloud Model
08Oct

Data Cloud Model

While many Salesforce products like Sales Cloud, Service Cloud, Education Cloud, Health Cloud, and various industry clouds are built on a common ‘core’ platform that shares the same datastore (an Oracle relational database), Salesforce Data Cloud operates on (Data Cloud Model) a distinct architecture and technology stack. This insight explores the data models and related concepts that underpin the platform. Data Cloud Architecture and Storage Starting with the storage layer, Salesforce Data Cloud integrates multiple services: This combination enables Data Cloud to provide a petabyte-scale data store, overcoming the scalability and performance constraints of traditional relational databases. The physical architecture of Data Cloud is represented as a set of data objects, which form the foundation for how data is ingested, harmonized, and activated within the platform. Key Data Cloud Objects Data Source A Data Source is the initial data layer in Data Cloud, representing an external platform or system from which your data originates. Data Sources can include: Data Stream A Data Stream is an entity extracted from a Data Source, such as ‘Orders’ from Commerce Cloud, ‘Contacts’ from Sales Cloud, or ‘Subscribers’ from Marketing Cloud. Once a Data Source is connected to Data Cloud, Data Streams provide access to these entities and require assignment to a category: Profile, Engagement, or Other. A single Data Source can contain multiple Data Streams. Data Source Object (DSO) Data Streams are ingested into Data Source Objects (DSOs), which serve as temporary staging stores containing data in its raw, native format (e.g., CSV files). Minor transformations can be applied to fields during data ingestion. Data Lake Object (DLO) The next step in the data flow is the Data Lake Object (DLO). DLOs are typed, schema-based, materialized views that reside in the data lake (Amazon S3) and are generally stored as Apache Parquet files. This format, along with Apache Iceberg’s abstraction layer, supports efficient data storage and retrieval. DLOs are the first objects available for inspection and preparation of data, including field mapping and additional transformations. Data Model Object (DMO) Unlike DSOs and DLOs, which use a physical data store, Data Model Objects (DMOs) provide a virtual, non-materialized view into the data lake. Queries against a DMO return results based on the current data snapshot in DLOs, without storing them. DMOs can aggregate attributes from different Data Streams, Calculated Insights, and other sources. DMOs inherit their category from the first DLO mapped to them, and only DLOs with the same category can be mapped to the DMO. Like Salesforce objects, DMOs offer a canonical data model with pre-defined attributes (standard objects), and you can also create custom DMOs (custom objects). DMOs can have one-to-one or many-to-one relationships with other DMOs. Currently, there are 89 standard DMOs in Data Cloud, and this number is growing to support more use cases. DMOs are organized into various subject areas, including: Data Spaces Data Spaces offer logical partitions, allowing data to be segregated between different brands, regions, or departments, without the need for multiple Data Cloud instances. They also align with a Software Development Lifecycle (SDLC), enabling you to stage and test Data Objects in separate environments without affecting production data. Data Sources, Data Streams, and DLOs can be shared across Data Spaces, while DMOs and other platform features are isolated based on user permissions. Final Thoughts With the drop in storage costs and advancements in technology, companies now have vast datasets at their disposal. Every customer interaction, whether it’s a purchase, an email open, or a web page view, can be captured and stored. When properly organized, this data allows businesses to understand customers better, predict their needs, personalize interactions, and much more. However, one of Salesforce’s challenges is that its original platform, built over 20 years ago, was designed for a different era and struggles with handling voluminous, “big” data. Salesforce Data Cloud addresses these limitations with a modern architecture that overcomes the constraints of relational databases. Data Cloud is poised to become the backbone of the Salesforce platform, supporting the data needs of customers and products for the next two decades. If you’re interested in learning how to implement and integrate Salesforce Data Cloud in your organization, following best practices, reach out today. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

Read More
October 8, 2024in Data, Google, Salesforce, Salesforce Data Cloud, Snowflake, Technology, Unified Knowledge
Salesforce LlamaRank
07Oct

Salesforce LlamaRank

Document ranking remains a critical challenge in information retrieval and natural language processing. Effective document retrieval and ranking are crucial for enhancing the performance of search engines, question-answering systems, and Retrieval-Augmented Generation (RAG) systems. Traditional ranking models often struggle to balance result precision with computational efficiency, especially when dealing with large datasets and diverse query types. This challenge underscores the growing need for advanced models that can provide accurate, contextually relevant results in real-time from continuous data streams and increasingly complex queries. Salesforce AI Research has introduced a cutting-edge reranker named LlamaRank, designed to significantly enhance document ranking and code search tasks across various datasets. Built on the Llama3-8B-Instruct architecture, LlamaRank integrates advanced linear and calibrated scoring mechanisms, achieving both speed and interpretability. The Salesforce AI Research team developed LlamaRank as a specialized tool for document relevancy ranking. Enhanced by iterative feedback from their dedicated RLHF data annotation team, LlamaRank outperforms many leading APIs in general document ranking and sets a new standard for code search performance. The model’s training data includes high-quality synthesized data from Llama3-70B and Llama3-405B, along with human-labeled annotations, covering a broad range of domains from topic-based search and document QA to code QA. In RAG systems, LlamaRank plays a crucial role. Initially, a query is processed using a less precise but cost-effective method, such as semantic search with embeddings, to generate a list of potential documents. The reranker then refines this list to identify the most relevant documents, ensuring that the language model is fine-tuned with only the most pertinent information, thereby improving accuracy and coherence in the output responses. LlamaRank’s architecture, based on Llama3-8B-Instruct, leverages a diverse training corpus of synthetic and human-labeled data. This extensive dataset enables LlamaRank to excel in various tasks, from general document retrieval to specialized code searches. The model underwent multiple feedback cycles from Salesforce’s data annotation team to achieve optimal accuracy and relevance in its scoring predictions. During inference, LlamaRank predicts token probabilities and calculates a numeric relevance score, facilitating efficient reranking. Demonstrated on several public datasets, LlamaRank has shown impressive performance. For instance, on the SQuAD dataset for question answering, LlamaRank achieved a hit rate of 99.3%. It posted a hit rate of 92.0% on the TriviaQA dataset. In code search benchmarks, LlamaRank recorded a hit rate of 81.8% on the Neural Code Search dataset and 98.6% on the TrailheadQA dataset. These results highlight LlamaRank’s versatility and efficiency across various document types and query scenarios. LlamaRank’s technical specifications further emphasize its advantages. Supporting up to 8,000 tokens per document, it significantly outperforms competitors like Cohere’s reranker. It delivers low-latency performance, ranking 64 documents in under 200 ms with a single H100 GPU, compared to approximately 3.13 seconds on Cohere’s serverless API. Additionally, LlamaRank features linear scoring calibration, offering clear and interpretable relevance scores. While LlamaRank’s size of 8 billion parameters contributes to its high performance, it is approaching the upper limits of reranking model size. Future research may focus on optimizing model size to balance quality and efficiency. Overall, LlamaRank from Salesforce AI Research marks a significant advancement in reranking technology, promising to greatly enhance RAG systems’ effectiveness across a wide range of applications. With its powerful performance, efficiency, and clear scoring, LlamaRank represents a major step forward in document retrieval and search accuracy. The community eagerly anticipates its broader adoption and further development. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

Read More
October 7, 2024in Data, Salesforce, Technology
05Oct

Agentforce Autonomous Agents

Agentforce: Transforming Business Operations with Autonomous Agents Agentforce empowers organizations to create and manage autonomous agents that streamline tasks across various business departments. These include Sales Agents, Service Agents, Marketing Agents, Commerce Agents, and Platform Agents—truly delivering on the vision of “an Agentforce in every app.” But how does Agentforce work, and what are the building blocks for configuring these agents? Salesforce emphasizes that Agentforce is built with clicks, not code, making it highly accessible to users. This claim was validated by many attendees at the ‘Agentforce Launchpad’ during Dreamforce, who noted that the tool is as declarative and user-friendly as Salesforce promised. The Building Blocks of Agentforce 1. Agent Builder The journey begins with the Agent Builder within Agentforce Studio. This configuration tool allows users to define their agent’s attributes, such as the avatar, name, and description, using natural language inputs—essentially describing the agent in conversational terms. Salesforce describes it as: “If you can dream it, Agentforce can do it.” The Agent Builder interface comprises: Salesforce also provides out-of-the-box agents, such as Sales Agents, which can be enabled via guided setup. 2. Agent Topics Topics are the foundational building blocks that determine an agent’s scope of work. For example, a topic like “Order Management” grants the agent access to data such as order histories and product specifications. In the Dreamforce keynote, Saks’ service agent demonstrated the importance of topics by resolving customer queries tied to its assigned topics. However, queries outside the defined topics were flagged as “guardrails,” ensuring the agent stayed within its designated scope. 3. Topic Actions Actions, tied to topics, define what an agent can do. These actions are often flows, such as querying a CRM database or triggering automated processes. Users can assign existing actions or create new ones by referencing Apex, Flow, prompts, or MuleSoft APIs. For example, integrating external data sources requires defining a new Agentforce action tied to a MuleSoft API. This allows the agent to query data just as human users would. Testing Agents with the Atlas Reasoning Engine Agentforce’s Atlas Reasoning Engine powers agents with advanced capabilities. Users can test agents within the Agent Builder interface, following the reasoning process step-by-step: Once configured, agents are ready to operate across their assigned communication channels (e.g., email, WhatsApp, voice). Omni Supervisor: Real-Time Agent Monitoring Omni Supervisor, originally a Service Cloud feature, now extends to monitoring agents. It provides insights into overall trends, allows real-time oversight of interactions, and even enables listening to recent conversations. The Role of Data Cloud in Agentforce Data powers Agentforce, enabling agents to provide highly contextual responses. The Data Cloud processes both structured data (e.g., Salesforce records) and unstructured data (e.g., emails, voice memos) using its Vector Database for advanced processing. 1. Retrieval Augmented Generation (RAG) Salesforce employs RAG to enhance the accuracy of agent responses. RAG integrates the Atlas Reasoning Engine with Data Cloud, creating a feedback loop. Data Cloud enriches user prompts by retrieving relevant data, making agent responses more contextual and informed. 2. New Data Streams To enhance Agentforce capabilities, data can be ingested into the platform in three ways: For instance, connecting an order management system like Snowflake is streamlined via Salesforce’s prebuilt connectors. 3. Data Graphs Data Graphs visualize relationships between Data Model Objects (DMOs), enabling users to ensure all necessary data is available for optimal agent performance. Real-time Data Graphs enhance identity resolution, segmentation, and action execution for seamless data flow. Inside Prompt Builder Prompt Builder allows users to create or refine prompts that power Agentforce actions. Low-code tools guide users through the process, offering features such as previewing results and assessing feedback toxicity ratings. Search Index in RAG The Search Index is a critical component of RAG. It retrieves relevant data from Data Cloud to enhance agent reasoning. Search parameters can be configured in three ways: Tectonic’s Thoughts Agentforce, powered by Data Cloud and advanced AI tools like the Atlas Reasoning Engine, represents a new era of automation and efficiency for businesses. Whether through Sales, Service, or Marketing Agents, organizations can leverage this technology to streamline operations, personalize customer experiences, and achieve better outcomes. With over 5,200 customers implementing Agentforce in their sandboxes within the first two days of Dreamforce, the platform is already proving its transformative potential. By 2025 over a billion agents had been created! Agentforce isn’t just about improving efficiency; it’s about redefining what’s possible for business operations. Content updated January 2025. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

Read More
October 5, 2024in AI Tools, Data, Mulesoft, Salesforce, Snowflake, Technology
What Makes a True AI Agent
03Oct

What Makes a True AI Agent

What Makes a True AI Agent? Rethinking the Pursuit of Autonomy Unpacking the Core Traits of AI Agents — And Why Foundations Matter More Than Buzzwords The tech industry is enamored with AI agents. From sales bots to autonomous systems, companies like Salesforce and HubSpot claim to offer groundbreaking AI agents. Yet, I’ve yet to encounter a truly autonomous, agentic experience built from LLMs. The market is awash with what I call “botshit,” and if the best Salesforce can do is improve slightly over a mediocre chatbot, that’s underwhelming. What Makes a True AI Agent? But here’s the critical question everyone is missing: even if we could build fully autonomous AI agents, how often would they be the best solution for users? To explore this, let’s consider travel planning through the lens of agents and assistants. This use case helps clarify what each trait of agentic behavior brings to the table and offers a framework for evaluating AI products beyond the hype. By the end of this piece, you’ll be able to decide whether AI autonomy is a worthwhile investment or a costly distraction. The Spectrum of Agentic Behavior: A Practical Framework There’s no consensus on what truly defines an AI “agent.” Instead of relying on a binary classification, I suggest adopting a spectrum framework with six key attributes from AI research. This approach is more useful in today’s landscape because: Using the example of a travel “agent,” we’ll explore how different implementations fall on this spectrum. Most real-world applications land somewhere between “basic” and “advanced” tiers across the six traits. This framework will help you make informed decisions about AI integration and communicate more effectively with both technical teams and end users. By the end, you’ll be equipped to: What Makes a True AI Agent The Building Blocks of Agentic Behavior 1. Perception The ability to sense and interpret its environment or relevant data streams. An agent with advanced perception could, for instance, notice your preference for destinations with excellent public transit and factor that into future recommendations. 2. Interactivity The ability to engage with its environment, users, and external systems. LLMs like ChatGPT have set a high bar for interactivity. However, most customer support bots struggle because they need to integrate company-specific data and backend systems, prioritizing accuracy over creativity. 3. Persistence The ability to store, maintain, and update long-term memories about users and interactions. True persistence requires systems that not only store data but also evolve with each interaction, much like how a human travel agent remembers your favorite seat on a plane. 4. Reactivity The ability to respond to changes in its environment in real time. For example, a reactive system could suggest alternative travel dates if hotel prices surge due to a local event. 5. Proactivity The ability to anticipate needs and offer relevant suggestions unprompted. True proactivity requires robust perception, persistence, and reactivity to offer timely, context-aware suggestions. 6. Autonomy The ability to operate independently and make decisions within defined parameters. Autonomy varies by the level of resource control, impact scope, and operational boundaries. For example: The more complex the task and the greater the impact of a mistake, the more safeguards and precision the system needs. Proactive Autonomy: A Future Frontier The next step is proactive autonomy — the ability to modify goals or parameters to achieve overarching objectives. While theoretically possible, this introduces new risks and complexities, bringing us closer to the scenarios seen in sci-fi, where AI systems operate beyond human control. Most companies are nowhere near this level, and prioritizing foundation work like perception and persistence is far more practical for today’s needs. Agents vs. Assistants: A Useful Distinction An AI agent demonstrates at least five of the six attributes and exhibits autonomy within its domain. An AI assistant excels in perception, interactivity, and persistence but lacks autonomy or proactivity. It primarily responds to human requests and relies on human oversight for decisions. While many AI systems today are labeled “agents,” most function more like assistants. A Roomba, for example, is closer to an agent, autonomously navigating and adapting within a predefined space. On the other hand, tools like GitHub Copilot serve as powerful assistants, enhancing user capabilities without making independent decisions. Foundations Before Flash: The Role of Data Despite all the AI buzz, few companies today have the data foundations to support meaningful agentic behavior. For instance, most customer interactions rely on nuanced, unwritten information that is hard to automate. Missing perception foundations and inadequate testing lead to the “botshit” plaguing the industry. The key is to focus on building strong foundations in perception, interactivity, and persistence before tackling full autonomy. Start with the Problem: Why User-Centric AI Wins Before chasing the dream of autonomous agents, companies should start by asking what users actually need. Many organizations would benefit more from developing reliable assistants rather than fully autonomous systems. Real user problems, like those solved by Waymo and Roomba, offer clear paths to valuable AI solutions. The Path Forward: Align Data, Systems, and User Needs When deciding where to invest in AI: By focusing on foundational pillars, companies can build AI systems that solve immediate problems, laying the groundwork for more advanced capabilities in the future. Whether you’re developing agents, assistants, or indispensable tools, aligning solutions with real user needs is the key to meaningful progress. Contact Tectonic for assistance answering the question What Makes a True AI Agent work for my business? Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are

Read More
October 3, 2024in Data, Salesforce
Cross Cloud Zero-Copy Data
15Sep

Cross Cloud Zero-Copy Data

Simplifying Secure Data Access Across Clouds In today’s data-driven world, secure and prompt access to information is crucial. However, with critical analytics data spread across various cloud vendors, achieving this expediency can be challenging. Cross-cloud zero-copy data sharing doesn’t have to be complex. By leveraging your Autonomous Database, you can swiftly establish secure data sharing with your Salesforce CRM Data Stream in just seconds. This guide will walk you through the straightforward process of connecting your Salesforce CRM data to your Autonomous Database using the Salesforce CRM data connector type. Requirements for Salesforce Integration To connect Salesforce CRM data with your Autonomous Database, you’ll need the following: 1. Confirm Data Stream Configuration On the Data Streams Dashboard, verify the Data Stream Name, Data Connector Type, and Data Stream Status. 2. Set Up Your Autonomous Database Create Your Credentials: sqlCopy codeBEGIN DBMS_CLOUD.CREATE_CREDENTIAL( credential_name => ‘<your credential name>’, username => ‘<your salesforce log-in id>’, password => ‘<your salesforce password>’); END; / Create Your Database Link: sqlCopy codeBEGIN DBMS_CLOUD_ADMIN.CREATE_DATABASE_LINK( db_link_name => ‘<your database link name>’, hostname => ‘<your host>.my.salesforce.com’, port => ‘19937’, service_name => ‘salesforce’, ssl_server_cert_dn => NULL, credential_name => ‘<your credential name>’, gateway_params => JSON_OBJECT( ‘db_type’ value ‘salesforce’, ‘security_token’ value ‘<your security token>’)); END; / 3. Check Connectivity Details The HETEROGENEOUS_CONNECTIVITY_INFO view provides information on credential and database link requirements for external databases. For example: sqlCopy codeSELECT database_type, required_port, sample_usage FROM heterogeneous_connectivity_info WHERE database_type = ‘salesforce’; 4. Demonstration: Connecting to Salesforce Data Follow these steps to connect to your Salesforce CRM organization using the Salesforce Data Cloud Sales synthetic data in the Account_Home Data Stream: 5. Set Up Connectivity Using DBMS_CLOUD.CREATE_CREDENTIAL, create the necessary credentials to connect to Salesforce. Then, use DBMS_CLOUD_ADMIN.CREATE_DATABASE_LINK to establish the database link. Once configured, execute the SELECT statement against the ACCOUNT data to verify successful connection. 6. Utilize Zero-Copy Data Sharing With zero-copy data access to the Salesforce CRM Data Lake ACCOUNT object, you can: Conclusion As demonstrated, secure and efficient cross-cloud zero-copy data access can be straightforward. By following these simple steps, you can bypass cumbersome ETL operations and gain immediate, secure access to your Salesforce CRM data. This approach eliminates the overhead of complex data pipelines and provides you with real-time access to critical business data. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

Read More
September 15, 2024in Salesforce Data Cloud
  • 1
  • 2

Subscribe to our mailing list. Join our mail list to receive our newsletter

We'll keep you up to date with our latest news, insights, free resources, and much more.
Don't worry, we won't spam you.
gettectonic.com