Snowflake Archives - gettectonic.com - Page 4
Zero-Copy Integrations
27Apr

Zero-Copy Integrations

At the recent Salesforce World Tour NYC event, Salesforce introduced a new global ecosystem of technology and solution providers designed to assist its customers in leveraging third-party data through secure, bidirectional zero-copy integrations with Salesforce Data Cloud. Tyler Carlson, VP of business development and strategic partnerships at Salesforce, highlighted the key challenge faced by many customers: integrating data from various platforms without creating multiple iterations and losing data lineage. Currently, some startups offer “reverse” ETL services, copying data from customers’ data warehouses or platforms back into systems of engagement. However, this approach requires duplicating data, creating storage spaces, and maintaining data synchronization pipelines. To address these challenges, Salesforce introduced the Zero Copy Partner Network, which brings together ISVs and SIs to eliminate custom integrations and complex data pipelines. This network aims to provide businesses with a more efficient, secure, and user-friendly way to connect data to their applications compared to traditional ETL processes. Zero-copy integration allows teams to access data directly from its source, either through queries or virtual access, without the need for data duplication. Salesforce has pioneered zero-copy bidirectional integrations with Data Cloud partners like Amazon Redshift, Databricks, Google Cloud’s BigQuery, and Snowflake. While integrations with BigQuery and Snowflake are generally available, those with Redshift and Databricks are still in pilot but expected to launch later this year. Salesforce is expanding this network to include its ISV ecosystem, enabling them to build on top of zero-copy connectors to offer enrichment datasets and business applications with zero-copy integration. Additionally, the company is extending this capability to its SI ecosystem, ensuring that global SIs are certified and ready to assist customers with distributed zero-copy integration patterns. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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April 27, 2024in Salesforce, Snowflake
Harnessing Zero Copy Integration
21Apr

Harnessing Zero Copy Integration

Streamlining Data Management: Harnessing Zero Copy Integration for Efficient Customer Insights Zero copy integration offers a revolutionary approach to accessing and utilizing customer data without the need for burdensome data transfers. By seamlessly linking disparate databases, this innovative technique eliminates the complexities associated with moving, copying, or reformatting data. Let’s explore how zero copy integration can optimize data management processes and enhance the efficiency of customer data platforms (CDPs) alongside traditional data warehouses. Understanding Zero Copy Integration: Zero copy integration, also known as zero ETL (extract-transform-load), facilitates the sharing of data across multiple databases without physically relocating it. This method enables real-time access to data, significantly reducing costs and mitigating the risks of errors inherent in data movement processes. Unlike traditional data replication methods, zero copy integration ensures that data remains in its original location, eliminating the need for redundant storage and synchronization efforts. Good technology standards can actually bring order to the chaos and allow products from different companies to work together, fostering healthy competition and enhancing productivity. That is precisely what Zero-Copy Integration does. Zero-Copy Integration is a framework that eliminates the need for data copies and integration, making it easier to work with applications, analytics, artificial intelligence, and machine learning. Canada’s Digital Governance Council recently announced that Zero-Copy Integration has been published for open access by the public, setting a new standard for data collaboration. Moreover, Zero-Copy Integration prioritizes data-centricity and active metadata over complex code and solution modularity over monolithic design. It also facilitates data governance through data products and federated stewardship, eliminating the need for centralized teams. In addition to boosting the development and deployment of new technologies, Zero-Copy Integration also addresses the growing number of data privacy regulations. By putting data at the center of the process, Zero-Copy Integration ensures greater collaboration and control, positively affecting industries like healthcare, research, banking, and public services. Benefits of Zero-Data Integration Proponents of zero-copy integration and dataware say the framework will lower data storage costs, improve performance of IT teams, improve privacy and security of data, and drive innovation in systems for public health, social research, open banking and sustainability through innovations in: Comparing Traditional vs. Zero Copy Integration: Aspect Traditional Zero Copy Replication Requires copying data to target Data remains in original location Updates Data accuracy dependent on synchronization Real-time access to data Cost Involves data movement expenses No additional data movement costs Regulatory Compliance Complex governance due to data duplication Simplified compliance with source data only Errors Data movement introduces potential errors Minimized risk of errors with no data movement Maintenance Increases complexity with copying and synchronization Streamlined management with no data relocation Implementing Zero Copy Integration: The implementation of zero copy integration varies depending on the platform and direction of data access. Let’s explore how it works in scenarios involving Salesforce Data Cloud and Snowflake data warehouse: Case Study: Buyers Edge’s Zero Copy Success Story: Buyers Edge, a procurement optimization company, leveraged zero copy integration to unify customer profiles in a CDP while accessing purchase data from their data warehouse. By seamlessly integrating Salesforce Data Cloud with their warehouse, Buyers Edge enhanced predictive modeling capabilities, resulting in tailored sales and marketing strategies. Zero copy integration revolutionizes data management practices by enabling seamless data access and eliminating the need for data duplication or relocation. As businesses navigate the evolving data landscape, leveraging zero copy integration can streamline operations, enhance insights, and empower organizations to unlock the full potential of their data assets. MILESTONES / TIMELINE Second half 2023 – Internationalization February 21, 2023 – Information Session (watch above ☝ or via YouTube) February 15, 2023 – ‘Zero-copy’ applications published by DCA February 08, 2023 – Published by Digital Governance Council (Press release) December 15, 2022 – Approved by Standards Council of Canada June-Nov 2022 – Public consultation period Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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April 21, 2024in Salesforce, Salesforce Data Cloud, Salesforce Einstein, Snowflake
Salesforce in a Mega-Data Deal with Informatica
15Apr

Salesforce in a Mega-Data Deal with Informatica

Since Salesforce announced its acquisition of Slack for $27.7B in late 2020, the cloud software mega-giant has paused its acquisition strategy due to factors like rising interest rates, declining revenues, and a laser focus on profitability. However, recent leaks from The Wall Street Journal and other news publications suggest that Salesforce in a Mega-Data Deal with Informatica, is in advanced talks to acquire Informatica in a deal worth over $11B. Informatica is a significant player in enterprise data management, boasting revenues of over $1.51B and a workforce of over 5,000 employees. They specialize in AI-powered cloud data management, assisting companies in processing and managing large volumes of data from various sources to derive actionable and real-time insights. Salesforce in a Mega-Data Deal with Informatica The synergies between Informatica and Salesforce are many, with both companies focusing on consolidating data from multiple sources to provide comprehensive business insights. This aligns well with Salesforce’s strategic shift towards AI-driven data processing and analysis, aiming to enhance generative and predictive capabilities. While Salesforce’s previous acquisition of MuleSoft in 2018 for $6.5B has proven successful in facilitating API connectivity for real-time integrations, Informatica brings expertise in ETL (Extract-Transform-Load), data quality, and data movement to and from platforms like Snowflake and Databricks. This potential mega-data deal underscores the growing importance of data in the tech industry, especially with the emergence of generative AI and large language models (LLMs) that enable deeper analysis of vast datasets. Salesforce’s recent rebranding of its platform to “Einstein 1” underscores the convergence of AI and data within its product suite. The company’s emphasis on “AI + Data + CRM” reflects its commitment to leveraging data analytics for CRM enhancement, exemplified by the growth of its Data Cloud product. Partnering with industry leaders like Snowflake, Databricks, AWS, and Google, Salesforce aims to offer comprehensive data solutions that integrate seamlessly with existing systems. Informatica’s capabilities in ETL and Master Data Management (MDM) align with this vision, particularly in streamlining data integration and ensuring data quality across disparate systems. In final thoughts, while the Informatica acquisition is still pending finalization, it represents a strategic move by Salesforce to strengthen its position in the AI and data-driven CRM market. As Salesforce continues to evolve its product ecosystem, this acquisition signals its commitment to innovation and leadership in the era of AI-powered data analytics. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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April 15, 2024in Generative AI, Mulesoft, Salesforce, Snowflake
Salesforce Einstein Features
13Apr

Salesforce Einstein Features

Salesforce Einstein Discover the power of the #1 AI for CRM with Einstein. Built into the Salesforce Platform, Einstein uses powerful machine learning and large language models to personalize customer interactions and make employees more productive. With Einstein powering the Customer 360, teams can accelerate time to value, predict outcomes, and automatically generate content within the flow of work. Einstein is for everyone, empowering business users, Salesforce Admins and Developers to embed AI into every experience with low code. Salesforce Einstein Features. Einstein Copilot Sales Actions: Sell faster with an AI assistant in the flow of work.Call Exploration: Ask Einstein to synthesize important call information in seconds. Ask Einstein to identify important takeaways and customer sentiment, so you have the context you need to move deals forward.

 Sales Summaries: Summarize records to identify likelihood the deal will close, the competitors involved, key activities, and more. Forecast Guidance: Ask Einstein to inform your forecast and help you identify which deals need your attention. Close Plan: Generate a customized action plan personalized to your customer and sales process. Increase conversion rates with step-by-step guidance and milestones grounded in CRM data. Salesforce Einstein Features Sales Generative AI features: ° Knowledge Creation: ° Search Answers for Agents and Customers: Einstein Copilot Service Actions: Streamline service operations by drafting Knowledge articles and surfacing answers, grounded in knowledge, to the most commonly asked questions. Summarize support interactions to save agent time and formalize institutional knowledge. Surface generated answers to agents’ & customers’ questions that are grounded in your trusted Knowledge base directly into your search page. Search Answers for Agents is included in the Einstein for Service Add-on SKU and Search Answers for Customers is included in the Einstein 1 Service Edition.
Empower agents to deliver more personalized service and reach resolutions faster with an AI assistant built into the flow of work. You can leverage out-of-the-box actions like summarize conversations or answer questions with Knowledge or you can build custom actions to fit your unique business needs. Service Salesforce Einstein Features This Release Einstein CopilotSell faster with an AI assistant. No data requirements
Included in Einstein 1 Sales Edition. Einstein Copilot: Sales ActionsSell faster with an AI assistant.No data requirements. 
 Call explorer and meeting follow-up requires Einstein Conversation Insights.
Included in Einstein 1 Sales Edition. Generative AIBoost productivity by automating time-consuming tasks.No data requirements. 
 Call summaries and call explorer requires Einstein Conversation Insights.
Included in Einstein 1 Sales Edition. Einstein will use a global model until enough data is available for a local model. For a local model: ≥1,000 lead records created and ≥120 of those converted in the last 6 monthsEinstein Automated Contacts Automatically add new
contacts & events to your CRM≥ 30 business accounts. If you use Person Accounts, >= 50 percent of accounts must be business accounts Einstein Recommended ConnectionsGet insights about your teams network to see who knows your customers and can help out ona deal ≥ 2 users to be connected to Einstein Activity Capture
and Inbox (5 preferred) Einstein Forecasting Easily predict sales forecasts inside
of Salesforce Collaborative Forecasting enabled; use a standard fiscal year; measure forecasts by opportunity revenue; forecast hierarchy must include at least one forecasting enabled user who reports to a forecast manager; opportunities must be in Salesforce ≥ 24 months;Einstein Email Insights Prioritize your inbox with actionable intelligence Einstein Activity Capture enabledEinstein Activity Metiics (Activity 360) Get insight into the activities you enter
manually and automatically from Einstein
Activity Capture Einstein Activity Capture enabled Sales Analytics Get insights into the most common sales KPIs No data requirements. User specific requirements like browser and device apply Einstein Conveisation Insights Gain actionable insights from your sales calls with conversational intelligenceCall or video recordings from Lightning Dialer, Service Cloud Voice, Zoom and other supported CTI audio and video partners.Buyer Assistant Replace web-to-lead forms with real-time conversations. No data requirements – Sales Cloud UE or Sales Engagement. Einstein Opportunity ScoringEinstein Activity CaptuiePrioritize the opportunities most likely to convertAutomatically capture data & add to your CRMEinstein will use a global model until enough data is available for a local model. For a local model: ≥ 200 closed won and ≥ 200 closed lost opportunities in the last 2 years, each with a lifespan of at least 2 days≥ 30 accounts, contacts, or leads; Requires Gmail, Microsoft Exchange 2019, 2016, or 2013 Einstein Relationship Insights Speed prospecting with AI that researches for you. No data requirements. Einstein Next Best Action Deliver optimal recommendations at the point of maximumimpactNo data requirements. User specific requirements like browser and device apply Sales AIGenerate emails, prioritize leads & opportunities most likely to convert, uncover pipeline trends, predict sales forecasts, automate data capture, and more with Einstein for Sales. Generative AIPrompt BuilderEinstein Lead ScoringEinstein Opportunity ScoringEinstein Activity CaptureEinstein Automated ContactsEinstein Recommended ConnectionsEinstein ForecastingEinstein Email InsightsEinstein Activity Metrics (Activity 360)Sales AnalyticsEinstein Conversation InsightsBuyer Assistant Sales AIGenerative AI: 
Feature Why is it so Great? What do I need? Automate common questions and business processes to solve customer requests fasterBoost productivity by auto-generating service replies, summarizing conversations during escalations andtransfers or closed interactions, drafting knowledge articles, and surfacing relevant answers grounded inknowledge for agents’ and customers’ commonly asked questions. Deliver optimal recommendations at the point of maximum impactEliminate the guesswork with AI-powered recommendations for everyoneDecrease time spent on manual data entry for incoming cases and improve case field accuracy and completionAutomate case triage and solve customer requests fasterDecrease time spent selecting field values needed to close a case with chat conversations and improved field accuracySurface the best articles in real time to solve any customer’s questionEliminate time spent typing responses to the most common customer questionsGet insights into contact center operations, understand customers, and deliver enhanced customerexperiencesChat or Messaging channels, minimum of 20 examples for most languagesNo data requirements. User specific requirements like browser and device apply Make sure that your dataset has the minimum records to build a successful recommendation. Recipient Records need a minimum of 100 records,Recommended Item Records need a minimum of 10 records, andPositive Interaction Examples need a minimum of 400

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April 13, 2024in Generative AI, Salesforce Einstein, Snowflake
Data Cloud and Snowflake Bidrectional Data Sharing
01Apr

Data Cloud and Snowflake Bidrectional Data Sharing

Salesforce Data Cloud and Snowflake are excited to announce that bidirectional data sharing between Snowflake, the Data Cloud company, and Salesforce Data Cloud is now generally available. In September, we introduced the ability for organizations to leverage Salesforce data directly in Snowflake via zero-ETL data sharing, enabling unified customer and business data, accelerating decision-making, and streamlining business processes. Today, we’re thrilled to share that customers can now also share Snowflake data into the Salesforce Data Cloud, using the same zero-ETL innovation to reduce friction and quickly surface powerful insights across sales, service, marketing, and commerce applications. Data Cloud and Snowflake Bidrectional Data Sharing. Data Cloud and Snowflake Bidrectional Data Sharing Enterprises generate valuable customer data within Salesforce applications, while increasingly relying on Snowflake as their preferred data platform for storing, modeling, and analyzing their full data estate. This integration between Salesforce and Snowflake minimizes friction, data latency, scale limitations, and data engineering costs associated with using these two leading platforms. The Snowflake Marketplace also offers customers the opportunity to acquire new data sets to enhance or fill gaps in their existing business data, driving innovation. By combining enterprise data and third-party data from Snowflake Marketplace with valuable customer data from Salesforce applications, organizations can unify their data and build powerful AI solutions to surface rich insights, driving superior and differentiated customer experiences. “Zero-ETL data sharing between Salesforce Data Cloud and Snowflake is game-changing. It has opened up new frontiers of data collaboration. We’re excited to see how customers are powering their customer data analytics and developing innovative AI solutions with near real-time data from Salesforce and Snowflake, generating incredible business value. Now that this integration is generally available, this kind of innovation will be broadly accessible,” says Christian Kleinerman, SVP of Product, Snowflake. Power Personalized Experiences with Salesforce and Snowflake Data sharing between Salesforce Data Cloud and Snowflake brings together holistic insights, empowering multiple customer-facing departments within any organization to create a truly robust customer 360. As Snowflake’s Chief Marketing Officer, Denise Persson, often states, a true, enterprise-wide customer 360 is the beating heart of a modern, customer-facing organization. The applicability of this integration spans various industries and unlocks new growth opportunities. For example: The bidirectional integration enables data sharing across business systems, Salesforce clouds, and operational systems, facilitating data set analysis and future action planning. This brings actionable insights and drives actions, unleashing a new level of customer experience and business productivity. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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April 1, 2024in Salesforce Data Cloud, Salesforce Marketing Cloud, Snowflake
Salesforce and Snowflake
13Mar

Is Snowflake a Data Cloud?

Is Snowflake recognized as a data cloud? Certainly, Snowflake stands as an encompassing platform integral to the Data Cloud. Uniquely designed for global connectivity, it empowers businesses to navigate diverse data types and scales, accommodating various workloads and fostering seamless data collaboration. Globally, over 400 million SaaS data sets often remain isolated in cloud storage and on-premise data centers, creating data silos. The Data Cloud, driven by Snowflake, eradicates these silos, facilitating the effortless consolidation, analysis, sharing, and monetization of data. Is Snowflake categorized as a database or cloud? Snowflake exclusively operates on cloud infrastructure. With all components residing in public cloud infrastructures (excluding optional command line clients, drivers, and connectors), Snowflake utilizes virtual compute instances for computing needs and a storage service for persistent data storage. What characterizes a data cloud? A data cloud serves as a unified platform for structured, unstructured, or semi-structured data, simplifying data discovery and reducing complexity. It should be capable of collecting, ingesting, and processing data from various on-premises or cloud-based source systems, consolidating it into a single accessible location. Snowflake’s Data Cloud facilitates organizations in effortlessly unifying and connecting to a single copy of all their data. This results in an ecosystem where businesses connect not only to their individual data but also to each other, seamlessly sharing and consuming data and data services. How does Snowflake’s Data Cloud handle different workloads? Snowflake’s Data Cloud efficiently powers diverse data workloads, including Data Warehousing, Data Lake, Data Engineering, AI and ML, Applications, and Cybersecurity. It operates seamlessly across multiple cloud providers and regions, catering to organizational needs from any location within the organization. What storage type does Snowflake employ? Snowflake utilizes scalable Cloud blob storage for its storage layer, accommodating data, tables, and query results. This storage is designed to scale independently from compute resources, allowing customers to adjust storage and analytics requirements independently. Is Snowflake considered a data warehouse or ETL? Snowflake’s capabilities in data loading, transformation, and storage eliminate the need for additional ETL tools, providing an end-to-end solution. Renowned worldwide, many organizations have adopted Snowflake as their primary Data Warehousing solution due to its distinctive features, scalability, and security. Where is Snowflake’s data stored? Snowflake’s database storage layer resides in a scalable cloud storage service, such as Amazon S3, ensuring data replication, scaling, and availability without requiring customer management. The data is optimized and stored in a columnar format within the storage layer, following user-specified database organization. How does Snowflake’s architecture benefit organizations? Snowflake’s architecture offers near-unlimited storage and real-time computing to an extensive number of concurrent users within the Data Cloud. It enables organizations to execute critical workloads on a fully managed platform, leveraging the cloud’s near-infinite resources. What are the advantages of Snowflake’s single platform? Snowflake’s single platform delivers optimal workload performance, full automation, secure global collaboration, Snowflake capabilities for non-SQL code processing, and optimized storage. It encompasses features such as Elastic Multi-Cluster Compute, Cloud Services, and Snowgrid, providing businesses with a comprehensive and fully managed solution. Why opt for Snowflake over competitors? Snowflake’s main advantage lies in its multi-cloud capability, available on major platforms like Azure, AWS, and GCP. This flexibility benefits companies operating in multi-cloud environments, enabling them to query Snowflake data directly from any of these platforms. About Snowflake Inc. Snowflake Inc., an American cloud computing-based data cloud company located in Bozeman, Montana, was founded in July 2012 and publicly launched in October 2014. Operating as a data-as-a-service provider, Snowflake offers cloud-based data storage and analytics services. Like Related Posts CRM Cloud Salesforce What is a CRM Cloud Salesforce? Salesforce Service Cloud is a customer relationship management (CRM) platform for Salesforce clients to Read more How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more Salesforce 360 Unlock the full potential of your business with Salesforce 360, the world’s leading customer relationship management platform. Imagine having a Read more Salesforce Health Cloud Information Since its inception in 2016, Salesforce Health Cloud has evolved significantly, adapting to the intricacies of the sensitive and dynamic Read more

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March 13, 2024in Salesforce Data Cloud, Snowflake
28Feb

Salesforce Data Cloud Explained

Salesforce Data Cloud: The Essential Guide Unlocking the Power of Unified Customer Data Salesforce Data Cloud revolutionizes how businesses connect and activate customer data by unifying information from multiple sources—including demographic, behavioral, and transactional data (e.g., mobile app engagement, eCommerce purchases, and support cases). But before diving in, it’s crucial to understand what Data Cloud is (and isn’t) to maximize its potential. Here are 10 key facts to guide your implementation. 1. Data Cloud (Free) vs. Paid Editions 💡 Key Insight: Start with the free tier to explore, then upgrade as needs grow. 2. Availability & Regional Restrictions 3. Unified Profiles: The “Golden Record” A unified profile is not a merged record—it’s a dynamic, real-time view combining: Unlike Salesforce duplicate rules, source records remain intact—Data Cloud simply creates a single customer view. ⚠️ Note: Unified profiles consume credits based on processing complexity. 4. Data Cloud ≠ A Data Lake 5. Key Data Modeling Concepts Before ingesting data, understand: 📌 Pro Tip: If you’ve used Marketing Cloud Data Extensions, you already know this! 6. No Activations in Free Tier Activations (sending segments to external platforms) require paid editions: Without activations, your segments remain stuck in Data Cloud. 7. Activations vs. Data Actions Feature Use Case Targets Activations Send segments to external platforms Marketing Cloud, Ads, Salesforce Apps Data Actions Trigger real-time insights Platform Events, Webhooks, MC 8. Have Clear Use Cases Before enabling Data Cloud, define what problem you’re solving:✅ Personalized Marketing (e.g., dynamic ad audiences)✅ AI-Driven Sales Insights (e.g., lead scoring)✅ Unified Service History (e.g., 360° customer view) 🚀 Example: A retailer uses Data Cloud to track online + in-store purchases, enabling hyper-targeted email campaigns. 9. The Learning Curve is Worth It 10. Start Small, Scale Smart Final Thoughts Salesforce Data Cloud is a game-changer for businesses drowning in siloed data. By unifying customer insights and enabling real-time activation, it powers smarter marketing, sales, and service—but only if implemented strategically. Ready to begin?✔ Leverage the free tier for testing.✔ Plan use cases before scaling.✔ Invest in training to maximize value. The future of CRM is connected data—will your business be ready? Content updated March 2025. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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February 28, 2024in Data, Enterprise, Google, Salesforce, Salesforce Data Cloud, Snowflake
Salesforce Data Cloud
05Feb

Salesforce Data Cloud vs Snowflake

A Comprehensive Comparison In the today’s data-driven world, businesses increasingly turn to cloud-based data platforms for managing, analyzing, and deriving insights from their customer data. Among the prominent options available, Salesforce Data Cloud and Snowflake stand out from the crowd. While both platforms offer robust capabilities, they exhibit distinct strengths and weaknesses. This insight looks into the comparison of Salesforce Data Cloud vs Snowflake. Salesforce Data Cloud: Salesforce Data Cloud is a hyperscale customer data platform (CDP) designed to help businesses consolidate all their customer data, including engagement data sourced externally. It establishes a unified view of the customer, empowering businesses to personalize experiences, enhance decision-making, and foster growth. Snowflake: Snowflake is a cloud-based data warehousing platform that facilitates the storage, analysis, and sharing of data for businesses. It encompasses a wide array of features, including an awesome SQL engine, elastic scalability, and compatibility with various data sources. Salesforce Data Cloud vs Snowflake Feature Salesforce Data Cloud Snowflake Focus Customer data General data Strengths Data enrichment, personalization, real-time updates Scalability, analytics, SQL support Weaknesses Less flexible than Snowflake, limited analytics capabilities Not as user-friendly as Salesforce Data Cloud Pricing Based on data volume Based on usage The ideal platform for you will be contingent on your specific needs and requirements. If your primary focus is on managing customer data and enhancing customer engagement, Salesforce Data Cloud proves to be a suitable option. On the other hand, if you require a more versatile data platform capable of handling a broad range of data types and workloads, Snowflake emerges as a better choice. Additional considerations include: Ultimately, the most effective way to determine the right platform for you is to experiment with both and assess which one aligns better with your preferences. Contact Tectonic today to explore Data Cloud and Snowflake for your data needs. Like2 Related Posts Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more 50 Advantages of Salesforce Sales Cloud According to the Salesforce 2017 State of Service report, 85% of executives with service oversight identify customer service as a Read more CRM Cloud Salesforce What is a CRM Cloud Salesforce? Salesforce Service Cloud is a customer relationship management (CRM) platform for Salesforce clients to Read more How Travel Companies Are Using Big Data and Analytics In today’s hyper-competitive business world, travel and hospitality consumers have more choices than ever before. With hundreds of hotel chains Read more

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February 5, 2024in Salesforce Data Cloud, Salesforce Optimization, Snowflake
Einstein 1 Unveiled
19Jan

Salesforce Einstein 1 Platform

Welcome to the dawn of a bold new era in enterprise Artificial Intelligence, where the world demands a platform unlike any other. Enter Salesforce Einstein 1, the pinnacle of innovation, designed to navigate terabytes of disparate data seamlessly, offer freedom in AI model selection, and integrate directly into your workflow—all while upholding customer trust. Einstein 1 Platform serves as the nexus of your data, AI, CRM, development, and security needs, consolidating them into a singular, robust framework. This empowers IT, administrators, and developers alike with an adaptable AI platform, facilitating rapid development of transformative applications and automation. Within Einstein 1 lies an array of artificial intelligence tools, reflecting the modular architecture of the core Salesforce platform. From standardized to custom solutions, Einstein 1 caters to diverse needs. Examples include out-of-the-box AI features like sales email generation in Sales Cloud and service replies in Service Cloud. Since its unveiling as Genie, now Data Cloud, at Dreamforce ‘22, Salesforce has propelled this product to the forefront of its mission. The conversation has shifted from Customer 360 to encompass AI + CRM + Data—a strategic pivot poised for success in 2024 and beyond. But what does this transformation entail? Data Cloud has been a focal point of Salesforce’s narrative, hailed as a groundbreaking CDP innovation. The recent Q4 earnings call unveiled remarkable achievements: Gartner’s endorsement further solidifies Salesforce’s position, elevating it as a leader in the Magic Quadrant, surpassing rivals like Twilio and Adobe. This achievement is remarkable given Salesforce’s relatively recent CDP certification from the CDP Institute. However, understanding the significance of Data Cloud amidst a sea of CRM solutions may elude many. Yet, the resurgence of data underscores its importance. CRMs, while pivotal for sales, support, and marketing functions, fall short in managing the breadth of modern data collection, storage, and dissemination. Enter the CDP—a dedicated platform for aggregating customer data across every touchpoint throughout the customer lifecycle. Unlike CRMs, which traditionally served as the single source of truth, CDPs offer a comprehensive view, ingesting data from myriad sources. Two key attributes distinguish CDPs and Data Cloud: Yet, amidst this data revolution, the role of artificial intelligence cannot be understated. Einstein 1, formerly known as Salesforce Customer 360, epitomizes this synergy between AI, CRM, and Data. Einstein 1 boasts a diverse array of AI tools, from pre-built features to customizable solutions housed within the Einstein 1 Studio. This enables users to craft tailored AI functionality, seamlessly integrated with Salesforce’s ecosystem. Moreover, Salesforce’s agnostic approach to LLMs empowers users to harness the full potential of AI. Whether leveraging Salesforce’s proprietary LLM or integrating third-party or proprietary models, Einstein 1 ensures flexibility and innovation. As AI continues to permeate every facet of business and personal life, Salesforce’s strategic focus on Data Cloud and Einstein could propel the company to unprecedented growth. In the ever-evolving landscape of enterprise AI, Salesforce remains at the forefront, poised to redefine the future of customer engagement and business intelligence. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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January 19, 2024in AI Tools, Artificial Intelligence, Data, Einstein 1 Platform, Enterprise, Google, Salesforce, Salesforce Einstein, Snowflake
Salesforce Data Cloud Explained
11Jan

The Complete Guide to Unified Customer Data

Salesforce Data Cloud: The Complete Guide to Unified Customer Data Transforming Business with Intelligent Data Management In today’s hyper-connected digital economy, 83% of customers expect personalized experiences—yet most companies struggle with data trapped in silos across 900+ applications. Salesforce Data Cloud (formerly Customer 360) solves this challenge by creating a single, real-time customer profile that powers: ✅ Hyper-personalized engagement across sales, service, and marketing✅ AI-driven decision making with unified analytics✅ Automated workflows that respond to customer needs instantly *”After implementing Data Cloud, Formula 1 achieved 88% fan satisfaction by delivering real-time personalized experiences based on location, preferences, and favorite drivers.”* Core Capabilities: Beyond Traditional CDPs Feature Legacy CDPs Salesforce Data Cloud Scope Marketing-only Enterprise-wide (Sales, Service, IT, Analytics) Data Types Structured customer data Structured + unstructured (PDFs, emails, IoT) AI Integration Basic segmentation Einstein AI predictions + generative AI Activation Campaign execution Cross-department automations Ecosystem Standalone Native Salesforce app integration Key Differentiators: 12 Transformative Features Explained Proven Business Outcomes For Customers: For Teams: For Leadership: Implementation Considerations Strengths✔ Gartner’s #1 Ranked CDP for 3 consecutive years✔ 98% implementation success rate✔ .2 ROI for every spent (Forrester TEI Study) Challenges⚠ Requires Salesforce ecosystem commitment⚠ Steep learning curve for advanced AI features⚠ Consumption-based pricing needs careful planning The Future of Data Cloud Salesforce’s 2024 roadmap includes:🔹 Autonomous Segmentation – AI that suggests customer cohorts🔹 GenAI Data Mapping – Natural language commands to transform data🔹 Blockchain Verification – Tamper-proof audit trails for regulated industries Getting Started “Companies using Data Cloud see payback in 7 months on average.” – IDC Research Ready to unlock your data potential? [Talk to our experts] about a tailored adoption path. #CustomerDataPlatform #CDP #Salesforce #CRM #ArtificialIntelligence Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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January 11, 2024in Data, Enterprise, Generative AI, Mulesoft, Salesforce, Salesforce Data Cloud, Snowflake
Marketing Cloud Cloudpages
20Dec

Salesforce Marketing Cloud for a Healthcare Provider

Personalized Care & Communication: Salesforce Marketing Cloud for a Healthcare Provider As the elderly become more tech-savvy, they expect modern, efficient ways to communicate with healthcare providers. A regional Medicare provider in the Northeastern United States faced challenges with their outdated communication systems. Relying solely on paper mail, postcards, and phone calls, the organization struggled to connect with members, lacked visibility into the success of their campaigns, and experienced early turnover due to minimal engagement. These inefficiencies strained the customer experience and made it clear that modernization was overdue. To address these challenges, we implemented Salesforce Marketing Cloud to engage customers through email and SMS. Goals for the Project: Tectonic’s Role in the Transformation Tectonic designed and implemented a Salesforce Marketing Cloud solution that transformed how the provider communicated with its members. The solution enabled multi-channel, multi-language communications integrated with Salesforce Health Cloud via the Marketing Cloud Connector and additional systems like MuleSoft and Snowflake. To enhance SMS capabilities, the organization also integrated with Five9. Early collaboration with the provider’s Salesforce Health Cloud team enabled Tectonic to address outdated customer data issues, create safeguards for inaccurate information, and plan future strategies for seamless customer data collection. A custom preference center was also developed and translated into multiple languages. Overcoming Data Challenges Accurate customer data was a significant obstacle—only 60% of records included valid email or mobile phone numbers, with an even smaller percentage having both. Tectonic conducted multiple working sessions to develop strategic efficiencies and establish a foundational process for gathering and cleansing member contact information. Customized journeys were created to ensure messaging aligned with available communication channels. For example: Key Outcomes Tectonic’s efforts allowed the healthcare provider to modernize their communications, better analyze engagement data, and improve member interactions. The results exceeded expectations: Impact Across Departments This project not only improved member communication but also empowered internal departments—including Marketing, Customer Experience, Sales, and Retention—with easy-to-understand metrics. It laid the foundation for future campaigns, enhanced data accuracy, and fostered stronger member relationships. By leveraging Salesforce Marketing Cloud and Tectonic’s expertise, the healthcare provider transformed its operations to deliver personalized, timely communication and ensure lifelong member satisfaction. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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December 20, 2023in Data, Mulesoft, Salesforce, Salesforce Health Cloud, Salesforce Marketing Cloud, Snowflake
Data Cloud Credits
12Dec

Data Cloud Credits

Credits are the currency of usage in Salesforce Data Cloud, where every action performed consumes credits. The consumption rate varies based on the complexity and compute cost of the action, reflecting different platform features. Data Cloud Pricing Model The pricing model for Data Cloud consists of three primary components: Data Service Credits Each platform action incurs a specific compute cost. For instance, processes like connecting, ingesting, transforming, and harmonizing data all consume ‘data service credits’. These credits are further divided into categories such as connect, harmonize, and activate, each encompassing multiple services with differing consumption rates. Segment and Activation Credits Apart from data service credits, ‘segment and activation credits’ are consumed based on the number of rows processed when publishing and activating segments. Monitoring Consumption Currently, Data Cloud users must request a consumption report from their Salesforce Account Executive to review credit and storage usage. However, the new Digital Wallet feature in the Summer ’24 Release will provide users with real-time monitoring capabilities. This includes tracking credit and storage consumption trends by usage type directly within the platform. Considerations and Best Practices To optimize credit consumption and ensure efficient use of resources, consider the following best practices: Final Thoughts Credits are integral to Data Cloud’s pricing structure, reflecting usage across various platform activities. Proactive monitoring through the Digital Wallet feature enables users to manage credits effectively, ensuring optimal resource allocation and cost efficiency. Content updated June 2024. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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December 12, 2023in Salesforce Data Cloud, Snowflake
Account Planning With Salesforce
25Sep

CRM Analytics Limits

When using CRM Analytics, keep these limits in mind. API Call Limits These limits apply to all supported editions. API Call Limit Maximum concurrent CRM Analytics API calls per org 100 Maximum CRM Analytics API calls per user per hour 10,000 Dataset Row Storage Allocations per License In Salesforce org, your total row storage limit for all registered datasets combined depends on your license combination. Each license allocates a different number of rows. Baseline Row Allocation Allocated Rows CRM Analytics Plus 10 billion CRM Analytics Growth 100 million Sales Analytics 25 million Service Analytics 25 million Event Monitoring Analytics 50 million B2B Marketing Analytics 25 million CRM Analytics for Financial Services Cloud 25 million CRM Analytics for Health Cloud 25 million Extra Data Rows license 100 million Your total row storage limit is a combination of your active licenses. For example: Because the CRM Analytics Plus license includes the Sales Analytics and Service Analytics licenses, your total row allocation remains 10 billion. Similarly, the CRM Analytics Growth license includes the Sales Analytics and the Service Analytics licenses, so your total row allocation remains 100 million. However, if you obtain another Sales Analytics or Services Analytics license, your row limit increases by 25 million for each added license. Dataset Row Limits Each dataset supports up to 2 billion rows. If your Salesforce org has less than 2 billion allocated rows, each dataset supports up to your org’s allocated rows. Dataset Field Limits Value Limit Maximum number of fields in a dataset 5,000 (including up to 1,000 date fields) Maximum number of decimal places for each value in a numeric field in a dataset (overflow limit) 17 decimal placesWhen a value exceeds the maximum number of decimal places, it overflows. Both 100,000,000,000,000,000 and 10,000,000,000,000,000.0 overflow because they use more than 17 decimal places. A number also overflows if it’s greater (or less) than the maximum (or minimum) supported value. 36,028,797,018,963,968 overflows because its value is greater than 36,028,797,018,963,967. -36,028,797,018,963,968 overflows because it’s less than -36,028,797,018,963,967.When a number overflows, the resulting behavior in CRM Analytics is unpredictable. Sometimes CRM Analytics throws an error. Sometimes it replaces a numeric value with a null value. And sometimes mathematical calculations, such as sums or averages, return incorrect results. Occasionally, CRM Analytics handles numbers up to 19 digits without overflowing because they are within the maximum value for a 64-bit signed integer (263 – 1). But numbers of these lengths aren’t guaranteed to process.As a best practice, stick with numbers that are 17 decimal places or fewer. If numbers that would overflow are necessary, setting lower precision and scale on the dataset containing the large numbers sometimes prevents overflow. If your org hasn’t enabled the handling of numeric values, the maximum number of decimal places for each value in a numeric field in a dataset is 16. All orgs created after Spring ’17 have Null Measure Handling enabled. Maximum value for each numeric field in a dataset, including decimal places 36,028,797,018,963,967For example, if three decimal places are used, the maximum value is 36,028,797,018,963.967 Minimum value for each numeric field in a dataset, including decimal places -36,028,797,018,963,968For example, if five decimal places are used, the minimum value is -36,028,797,018,9.63968 Maximum number of characters in a field 32,000 Data Sync Limits If you extract more than 100 objects in your dataflows, contact Salesforce Customer Support before you enable data sync. Value Limit Maximum number of concurrent data sync runs 3 Maximum number of objects that can be enabled for data sync, including local and remote objects 100 Maximum amount of time each data sync job can run for local objects 24 hours Maximum amount of time each data sync job can run for remote objects 12 hours Data sync limits for each job:Marketo Connector (Beta)NetSuite ConnectorZendesk Connector Up to 100,000 rows or 500 MB per object, whichever limit is reached first Data sync limits for each job:Amazon Athena ConnectorAWS RDS Oracle ConnectorDatabricks ConnectorGoogle Analytics ConnectorGoogle Analytics Core Reporting V4 ConnectorOracle Eloqua ConnectorSAP HANA Cloud ConnectorSAP HANA Connector Up to 10 million rows or 5 GB per object, whichever limit is reached first Data sync limits for each job*:AWS RDS Aurora MySQL ConnectorAWS RDS Aurora PostgresSQL ConnectorAWS RDS MariaDB ConnectorAWS RDS MySQL ConnectorAWS RDS PostgreSQL ConnectorAWS RDS SQL Server ConnectorGoogle Cloud Spanner ConnectorMicrosoft Azure Synapse Analytics ConnectorMicrosoft Dynamics CRM ConnectorSalesforce External ConnectorSalesforce Contacts Connector for Marketing Cloud EngagementSalesforce OAuth 2.0 Connector for Marketing Cloud Engagement Up to 20 million rows or 10 GB per object, whichever limit is reached first Data sync limits for each job*:Amazon Redshift ConnectorAmazon S3 ConnectorCustomer 360 Global Profile Data Connector (Beta)Google BigQuery for Legacy SQL ConnectorGoogle BigQuery Standard SQL ConnectorHeroku Postgres ConnectorMicrosoft Azure SQL Database ConnectorSnowflake Input Connector Up to 100 million rows or 50 GB per object, whichever limit is reached first *When using these connectors, Salesforce Government Cloud org data is protected in transit with advanced encryption and can sync up to 10 million rows or 5 GB for each connected object, whichever limit is reached first. Note When using a Salesforce local input connection, CRM Analytics bulk API usage doesn’t count towards Salesforce bulk API limits. Use of the external Salesforce connection and output connection impacts your limits. The dataflow submits a separate bulk API call to extract data from each Salesforce object. The dataflow uses a batch size of 100,000–250,000, depending on whether the dataflow or the bulk API chunks the data. As a result, to extract 1 million rows from an object, the dataflow creates 4–10 batches. Recipe and Dataflow Limits Important In Winter ‘24, recipe runs over 2 minutes are counted against the limit. Previously, the recipe run counts weren’t correct. For more information, see Known Issue – Recipe runs are not counting towards the daily maximum run limit. Value Limit Maximum amount of time each recipe or dataflow can run 48 hours Maximum number of recipes 1,000 Maximum number of dataflows definitions (with data sync enabled) 100 Maximum number of dataflow and recipe runs in a rolling

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September 25, 2023in Data, Google, Google Analytics, Salesforce, Salesforce Government Cloud, Snowflake
Crucial Role of Data and Integration in AI at Dreamforce
13Sep

The Crucial Role of Data and Integration in AI at Dreamforce

Understanding The Crucial Role of Data and Integration in AI at Dreamforce At this year’s Dreamforce, AI is the star of the show, but two essential supporting actors are data and integration. Enterprises are increasingly recognizing the importance of unifying their diverse data sources for effective analysis and swift action, and the race to harness AI makes this integration even more critical. Integration is key not only for merging data but also for automating end-to-end processes, enabling organizations to move faster and deliver better outcomes to customers. Crucial Role of Data and Integration in AI at Dreamforce. It’s no surprise that MuleSoft, acquired by Salesforce five years ago, is now a major contributor to Salesforce’s growth. Brian Millham, President and COO at Salesforce, highlighted this during the company’s recent Q2 earnings call: “In Q2, nearly half of our greater than $1 million deals included MuleSoft. As customers integrate data from all sources to drive efficiency, growth, and insights, MuleSoft has become mission-critical and was included in half of our top 10 deals.” Breaking Down Silos Param Kahlon, EVP and General Manager for Automation and Integration at Salesforce, recently discussed the investments customers are making in data and integration. He emphasized the importance of breaking down operational silos: “We are in the business of breaking silos across systems to ensure that data can travel seamlessly through multiple systems and people for processes like order-to-cash or procure-to-pay. Our technology connects these dots.” The surge in AI interest has increased the urgency to act, as Kahlon explained: “Creating data repositories for AI algorithms requires real-time data across silos, driving significant demand for our integration solutions.” Consolidating Data Enterprises have long struggled with data consolidation due to monolithic application stacks with separate data stores. This has been a challenge even within Salesforce’s own products. Last year, Salesforce introduced a Customer Data Platform (CDP) called Data Cloud, which includes a real-time data layer named Genie. Kahlon elaborated on its significance: “Data Cloud’s strength lies in its understanding and storage of Salesforce metadata. This native integration allows for real-time actions within Salesforce, enhancing the ability to aggregate, reason over, and act on data.” For example, when a customer contacts a bank, Data Cloud can compile their ATM usage, website interactions, and recent support cases, providing the agent with a comprehensive view to better assist the customer. Leveraging Metadata for AI Salesforce’s metadata layer, which has been fine-tuned over two decades, gives it a distinct advantage. Kahlon noted: “This metadata-based architecture allows us to create meaningful AI algorithms that are natively consumed within Salesforce, enabling visualization and action based on real-time data.” This is crucial for training the underlying Large Language Model (LLM) accurately, ensuring generated content is contextually grounded and trustworthy. Kahlon emphasized: “The trust layer is essential. We need to ensure no hallucination or toxicity in the LLM’s responses, and that communications align with our company’s values.” Real-Time Data and API Management Data Cloud’s ability to connect to other data sources like Snowflake without duplicating data is a significant benefit. Kahlon commented: “Duplicating data is not desirable. Customers need real-time access to the actual source of truth.” On the integration front, APIs have simplified connecting applications and data sources. However, managing API sprawl is crucial. Kahlon explained: “Standardizing API use and publishing them in a centralized portal is essential for reusability and consistency. Low-code platforms and connectors are becoming increasingly relevant, enabling business users to access data without relying on IT.” Automation and AI The demand for automation is growing, and low-code tools are vital. Instead of integration experts being overwhelmed, organizations should establish Centers for Excellence to focus on creating reusable connectors and automations. Kahlon added: “Companies need low-code tools to involve more business users in the transformation journey without slowing down due to legacy applications.” In the future, AI may further ease the workload on integration specialists. MuleSoft recently introduced an API Experience Hub to make APIs discoverable, and AI might eventually help monitor execution logs and manage APIs more effectively. Kahlon concluded: “AI could help developers find and use APIs efficiently, enhancing security and governance while simplifying access to data across the organization.” Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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September 13, 2023in Mulesoft, Salesforce, Salesforce Einstein, Snowflake

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