This principle was instrumental in shaping our data-focused enterprise architecture transformation in 2023, making data a core pillar alongside loan origination and servicing. Given that the company’s future growth depends on it, the focus on data was essential.
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
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
Databricks recently introduced Databricks Apps, a toolkit designed to simplify AI and data application development. By integrating native development platforms and offering automatic provisioning of serverless compute, the toolkit enables customers to more easily develop and deploy applications. Databricks Apps builds on the existing capabilities of Mosaic AI, which allows users to integrate large language models (LLMs) with their enterprise’s proprietary data. However, the ability to develop interactive AI applications, such as generative AI chatbots, was previously missing. Databricks Apps addresses this gap, allowing developers to build and deploy custom applications entirely within the secure Databricks environment. According to Donald Farmer, founder and principal of TreeHive Strategy, Databricks Apps removes obstacles like the need to set up separate infrastructure for development and deployment, making the process easier and more efficient. The new features allow companies to go beyond implementing AI/ML models and create differentiated applications that leverage their unique data sets. Kevin Petrie, an analyst at BARC U.S., highlighted the significance of Databricks Apps in helping companies develop custom AI applications, which are essential for maintaining a competitive edge. Databricks, founded in 2013, was one of the pioneers of the data lakehouse storage format, and over the last two years, it has expanded its platform to focus on AI and machine learning (ML) capabilities. The company’s $1.3 billion acquisition of MosaicML in June 2023 was a key milestone in building its AI environment. Databricks has since launched DBRX, its own large language model, and introduced further functionalities through product development. Databricks Apps, now available in public preview on AWS and Azure, advances these AI development capabilities, simplifying the process of building applications within a single platform. Developers can use frameworks like Dash, Flask, Gradio, Shiny, and Streamlit, or opt for integrated development environments (IDEs) like Visual Studio Code or PyCharm. The toolkit also provides prebuilt Python templates to accelerate development. Additionally, applications can be deployed and managed directly in Databricks, eliminating the need for external infrastructures. Databricks Apps includes security features such as access control and data lineage through the Unity Catalog. Farmer noted that the support for popular developer frameworks and the automatic provisioning of serverless compute could significantly impact the AI development landscape by reducing the complexity of deploying data architectures. While competitors like AWS, Google Cloud, Microsoft, and Snowflake have also made AI a key focus, Farmer pointed out that Databricks’ integration of AI tools into a unified platform sets it apart. Databricks Apps further enhances this competitive advantage. Despite the added capabilities of Databricks Apps, Petrie cautioned that developing generative AI applications still requires a level of expertise in data, AI, and the business domain. While Databricks aims to make AI more accessible, users will still need substantial knowledge to effectively leverage these tools. Databricks’ vice president of product management, Shanku Niyogi, explained that the new features in Databricks Apps were driven by customer feedback. As enterprise interest in AI grows, customers sought easier ways to develop and deploy internal data applications in a secure environment. Looking ahead, Databricks plans to continue investing in simplifying AI application development, with a focus on enhancing Mosaic AI and expanding its collaborative AI partner ecosystem. Farmer suggested that the company should focus on supporting nontechnical users and emerging AI technologies like multimodal models, which will become increasingly important in the coming years. The introduction of Databricks Apps marks a significant step forward in Databricks’ AI and machine learning strategy, offering users a more streamlined approach to building and deploying AI applications. 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
Salesforce, now one of the most influential companies in tech history, was founded by Marc Benioff after his successful tenure at Oracle. Having witnessed the potential of the internet, Benioff realized it would usher in a new era for delivering enterprise software through the cloud. However, this new approach demanded a complete overhaul of traditional business models, marketing strategies, and software development practices. Salesforce would embody this transformation, evolving over the decades into a platform supporting a diverse array of enterprise applications. Today, Salesforce’s flagship initiative is Agentforce, a pioneering wave of AI agents built on the Salesforce platform. This initiative, which I covered in detail earlier this month, exemplifies Benioff’s forward-thinking vision for AI. He passionately argues that Salesforce is well-positioned to lead in this space, and his insights on AI and its potential are as compelling as they are thought-provoking. Before diving into Agentforce and Benioff’s views on AI, it’s important to explore his early life and the founding of Salesforce. Benioff’s path to becoming the CEO of one of the most powerful software companies in the world was shaped by an early interest in technology. His first foray into software development was creating an app called “How to Juggle,” which he sold for to CLOAD Magazine in Goleta, California. This early success marked the beginning of a long career in tech. Benioff went on to establish an Atari game company called Liberty Software and later wrote assembly code for the Mac as part of an internship. Despite his success as a software developer, he transitioned to a leadership role after realizing his interest in the broader business of technology. He credits his time at Oracle, where he learned the intricacies of sales, marketing, and customer communication, as critical to shaping his vision for Salesforce. Oracle, during Larry Ellison’s prime, was an ideal place to hone these business skills, which Benioff would later apply to revolutionize the enterprise software space. With this foundation, Benioff founded Salesforce, a company that has fundamentally changed the way businesses engage with technology. The company’s success lies not just in its cloud-based software, but in its ongoing evolution to address new challenges, including the AI-powered Agentforce initiative, which stands at the forefront of Salesforce’s future growth. Benioff’s journey, from a young programmer to the CEO of a global tech giant, is a testament to his visionary approach and adaptability in an ever-changing tech landscape. Marc Benioff’s early entrepreneurial instincts were evident even before he fully embraced the title of entrepreneur. From a young age, he knew he wanted to build something of his own, though he hadn’t yet honed the full set of skills needed to succeed in business. He had a knack for technology, excelling as a programmer working in assembly code, but his mentors, including university professors and colleagues at Apple, recognized that being an entrepreneur required more than just technical ability. They encouraged him to develop his sales, marketing, and business acumen. His early exposure to these lessons was pivotal in shaping his future trajectory. Benioff’s transition from a programmer to a business leader began when he sought advice from Bill Jost at Apple, who connected him with Gary Gibson, an Oracle executive. After a memorable and unexpected interview, Benioff joined Oracle, starting with a role answering calls at the company’s 800-number. He quickly moved into sales and marketing, eventually working directly with Larry Ellison, who would become a significant mentor over the next 13 years. Benioff’s success at Oracle set the stage for his eventual departure to start Salesforce in 1999. In the mid-1990s, while at Oracle, Benioff had an epiphany while using a web browser. The internet was transforming how software could be delivered, and Benioff saw the potential for a new business model based on cloud computing. At this time, Oracle was focused on the network computer—a vision Benioff did not fully share. After discussing his ideas with Larry Ellison and taking some time off for reflection and research, Benioff recognized that the world was on the cusp of a major shift in technology. He felt compelled to create something new that would leverage the internet, offering software over the web through a subscription model. During his time off, Benioff traveled to Hawaii, reflecting on the future of the tech industry and contemplating ideas. He also experimented with domain names, purchasing You.com and later gifting it to a friend who turned it into a search and AI company. Benioff returned to Larry Ellison to share his vision of a cloud-based business model and later found inspiration from spiritual gurus during a trip to India, who reminded him that success in business should also be grounded in positive values. Benioff’s experience led to the founding of Salesforce with its revolutionary 1-1-1 Model—dedicating 1% of equity, profit, and employee time to philanthropy. Salesforce also embraced emerging technologies such as social, mobile, and AI, and created a new business model through subscription-based services. This combination of technological innovation and a strong sense of social responsibility has set Salesforce apart, attracting both admiration and emulation from other companies. Benioff’s Career Benioff’s career is a testament to the power of surrounding oneself with great people, continuing to learn, and having a clear vision. His leadership at Salesforce has been guided by values he believes are crucial to the company’s success and its ability to make a positive impact on the world. His insights on the Innovator’s Dilemma, a concept popularized by Clayton Christensen, highlight the challenges companies face as they evolve with changing technologies. While Salesforce’s core principles remain intact, the company has continued to adapt to new technology paradigms, ensuring it remains at the forefront of innovation in the software industry. Marc Benioff (MB) recently shared his excitement about the future of AI and its potential to revolutionize business and technology. He explained that, from a business perspective, this is the most thrilling moment in his career. AI, according to him, will transform how enterprises operate, making them more cost-efficient,
As organizations across industries gather increasing amounts of data from diverse sources, they face the challenge of making that data actionable and deriving real-time insights. With Salesforce Data Cloud and zero copy architecture, organizations can streamline access to data and build dynamic, real-time dashboards that drive value while embedding contextual insights into everyday workflows. A session during Dreamforce 2024 with Joanna McNurlen, Principal Solution Engineer for Data Cloud at Salesforce, discussed how zero copy architecture facilitates the creation of dashboards and workflows that provide near-instant insights, enabling quick decision-making to enhance operational efficiency and competitive advantage. What is zero copy architecture?Traditionally, organizations had to replicate data from one system to another, such as copying CRM data into a data warehouse for analysis. This approach introduces latency, increases storage costs, and often results in inconsistencies between systems. Zero copy architecture eliminates the need for replication and provides a single source of truth for your data. It allows different systems to access data in its original location without duplication across platforms. Instead of using traditional extract, transform, and load (ETL) processes, systems like Salesforce Data Cloud can connect directly with external databases, such as Google Cloud BigQuery, Snowflake, Databricks, or Amazon Redshift, for real-time data access. Zero copy can also facilitate data sharing from within Salesforce to other systems. As Salesforce expands its zero copy partner network, opportunities to easily connect data from various sources will continue to grow. How does zero copy work?Zero copy employs virtual tables that act as blueprints for the data structure, enabling queries to be executed as if the data were local. Changes made in the data warehouse are instantly visible across all connected systems, ensuring users always work with the latest information. While developing dashboards, users can connect directly to the zero copy objects within Data Cloud to create visualizations and reports on top of them. Why is zero copy beneficial?Zero copy allows organizations to analyze data as it is generated, enabling faster responses, smarter decision-making, and enhanced customer experiences. This architecture reduces reliance on data transformation workflows and synchronizations within both Tableau and CRM Analytics, where organizations have historically encountered bottlenecks due to runtimes and platform limits. Various teams can benefit from the following capabilities: Unlocking real-time insights in Salesforce using zero copy architectureZero copy architecture and real-time data are transforming how organizations operate. By eliminating data duplication and providing real-time insights, the use of zero copy in Salesforce Data Cloud empowers organizations to work more efficiently, make informed decisions, and enhance customer experiences. Now is the perfect time to explore how Salesforce Data Cloud and zero copy can elevate your operations. Tectonic, a trusted Salesforce partner, can help you unlock the potential of your data and create new opportunities with the Salesforce platform. Connect with us today to get started. 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
From Science Fiction to Reality: AI’s Game-Changing Role in Service and Sales AI for service and sales has reached a critical tipping point, driving rapid innovation. At Dreamforce in San Francisco, hosted by Salesforce we explored how Salesforce clients are leveraging CRM, Data Cloud, and AI to extract real business value from their Salesforce investments. In previous years, AI features branded under “Einstein” had been met with skepticism. These features, such as lead scoring, next-best-action suggestions for service agents, and cross-sell/upsell recommendations, often required substantial quality data in the CRM and knowledge base to be effective. However, customer data was frequently unreliable, with duplicate records and missing information, and the Salesforce knowledge base was underused. Building self-service capabilities with chatbots was also challenging, requiring accurate predictions of customer queries and well-structured decision trees. This year’s Dreamforce revealed a transformative shift. The advancements in AI, especially for customer service and sales, have become exceptionally powerful. Companies now need to take notice of Salesforce’s capabilities, which have expanded significantly. Agentforce – AI’s New Role in Sales and Service Some standout Salesforce features include: At Dreamforce, we participated in a workshop where they built an AI agent capable of responding to customer cases using product sheets and company knowledge within 90 minutes. This experience demonstrated how accessible AI solutions have become, no longer requiring developers or LLM experts to set up. The key challenge lies in mapping external data sources to a unified data model in Data Cloud, but once achieved, the potential for customer service and sales is immense. How AI and Data Integrate to Transform Service and Sales Businesses can harness the following integrated components to build a comprehensive solution: Real-World Success and AI Implementation OpenTable shared a successful example of building an AI agent for its app in just two months, using a small team of four. This was a marked improvement from the company’s previous chatbot projects, highlighting the efficiency of the latest AI tools. Most CEOs of large enterprises are exploring AI strategies, whether by developing their own LLMs or using pre-existing models. However, many of these efforts are siloed, and engineering costs are high, leading to clunky transitions between AI and human agents. Tectonic is well-positioned to help our clients quickly deploy AI-powered solutions that integrate seamlessly with their existing CRM and ERP systems. By leveraging AI agents to streamline customer interactions, enhance sales opportunities, and provide smooth handoffs to human agents, businesses can significantly improve customer experiences and drive growth. Tectonic is ready to help businesses achieve similar success with AI-driven innovation. 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
The Salesforce hype machine is in full swing, with product announcements like Chatter, Einstein GPT, and Data Cloud, all positioned as revolutionary tools that promise to transform how we work. Is Agentforce Different? However, it’s often difficult to separate fact from fiction in the world of Salesforce. The cloud giant thrives on staying ahead of technological advancements, which means reinventing itself every year with new releases and updates. You could even say three times per year with the major releases. Why Enterprises Need Multiple Salesforce Orgs Over the past decade, Salesforce product launches have been hit or miss—primarily miss. Offerings like IoT Cloud, Work.com, and NFT Cloud have faded into obscurity. This contrasts sharply with Salesforce’s earlier successes, such as Service Cloud, the AppExchange, Force.com, Salesforce Lightning, and Chatter, which defined its first decade in business. One notable exception is Data Cloud. This product has seen significant success and now serves as the cornerstone of Salesforce’s future AI and data strategy. With Salesforce’s growth slowing quarter over quarter, the company must find new avenues to generate substantial revenue. Artificial Intelligence seems to be their best shot at reclaiming a leadership position in the next technological wave. Is Agentforce Different? While Salesforce has been an AI leader for over a decade, the hype surrounding last year’s Dreamforce announcements didn’t deliver the growth the company was hoping for. The Einstein Copilot Studio—comprising Copilot, Prompt Builder, and Model Builder—hasn’t fully lived up to expectations. This can be attributed to a lack of AI readiness among enterprises, the relatively basic capabilities of large language models (LLMs), and the absence of fully developed use cases. In Salesforce’s keynote, it was revealed that over 82 billion flows are launched weekly, compared to just 122,000 prompts executed. While Flow has been around for years, this stat highlights that the use of AI-powered prompts is still far from mainstream—less than one prompt per Salesforce customer per week, on average. When ChatGPT launched at the end of 2022, many predicted the dawn of a new AI era, expecting a swift and dramatic transformation of the workplace. Two years later, it’s clear that AI’s impact has yet to fully materialize, especially when it comes to influencing global productivity and GDP. However, Salesforce’s latest release feels different. While AI Agents may seem new to many, this concept has been discussed in AI circles for decades. Marc Benioff’s recent statements during Dreamforce reflect a shift in strategy, including a direct critique of Microsoft’s Copilot product, signaling the intensifying AI competition. This year’s marketing strategy around Agentforce feels like it could be the transformative shift we’ve been waiting for. While tools like Salesforce Copilot will continue to evolve, agents capable of handling service cases, answering customer questions, and booking sales meetings instantly promise immediate ROI for organizations. Is the Future of Salesforce in the Hands of Agents? Despite the excitement, many questions remain. Are Salesforce customers ready for agents? Can organizations implement this technology effectively? Is Agentforce a real breakthrough or just another overhyped concept? Agentforce may not be vaporware. Reports suggest that its development was influenced by Salesforce’s acquisition of Airkit.AI, a platform that claims to resolve 90% of customer queries. Salesforce has even set up dedicated launchpads at Dreamforce to help customers start building their own agents. Yet concerns remain, especially regarding Salesforce’s complexity, technical debt, and platform sprawl. These issues, highlighted in this year’s Salesforce developer report, cannot be overlooked. Still, it’s hard to ignore Salesforce’s strategic genius. The platform has matured to the point where it offers nearly every functionality an organization could need, though at times the components feel a bit disconnected. For instance: Salesforce is even hinting at usage-based pricing, with a potential $2 charge per conversation—an innovation that could reshape their pricing model. Will Agents Be Salesforce’s Key to Future Growth? With so many unknowns, only time will tell if agents will be the breakthrough Salesforce needs to regain the momentum of its first two decades. Regardless, agents appear to be central to the future of AI. Leading organizations like Copado are also launching their own agents, signaling that this trend will define the next phase of AI innovation. In today’s macroeconomic environment, where companies are overstretched and workforce demands are high, AI’s ability to streamline operations and improve customer service has never been more critical. Whoever cracks customer service AI first could lead the charge in the inevitable AI spending boom. We’re all waiting to see if Salesforce has truly cracked the AI code. But one thing is certain: the race to dominate AI in customer service has begun. And Salsesforce may be at the forefront. 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
Capture Forecasts with Forecast Submissions Track sales forecasts accurately: Submit and review forecasts to keep them up-to-date with the latest estimates and quota comparisons. Available in Lightning Experience for Professional, Developer, Enterprise, and Unlimited editions with Sales Cloud. Forecast SubmissionsHow: Enable Forecast Submissions by navigating to the Forecast Settings page in Setup. Sales reps and managers can then use icons in the forecast summary to submit forecasts or review past submissions. Note that forecast submissions are restricted to forecast types excluding product family or other forecast groups. Expand the Reach of Your Enhanced CMS ContentHow: To publish content from enhanced CMS workspaces to Aura site channels using non-enhanced APIs, ensure the “Use non-enhanced APIs” setting is enabled by default. This setting allows you to continue using non-enhanced CMS Managed Content Resources without disrupting your existing processes. New workspaces created as of Winter ‘25 will be enhanced by default. Monitor Additional User PermissionsHow: Access additional user permission metrics from the Security Center under the User category in Security Overview. Metrics include permissions like Retain Field History, View Real-time Event Monitoring Data, and more. Integrate Knowledge and Unified Knowledge with Data CloudHow: In Data Cloud Setup, select Salesforce CRM and Standard Data Bundles. Choose Service Cloud and install or update the Service data kit. This integration prepares your Knowledge base for future enhancements with Data Cloud. Boost Service Efficiency by Automating Milestone Actions with FlowHow: Open the milestone from an entitlement process, click Add Actions, and select New Flow to add the autolaunched flow. Customize and activate this action in Flow Builder. Additional Enhancements Reference Multiple Picklist Values in Restriction and Scoping Rules Streamlined State and Country Picklist Setup Restore Only the Latest Entity History Records Selective Sandbox Access for Developer Sandboxes Find and Merge Duplicate Prospect Records Retirement of Campaign Member Status Chart Enhance Sales Forecast Accuracy with Manager Judgment Connect Unified Knowledge to More Systems Prioritize Cases by Tracking Milestone Time Renamed and Retired Features Retired: Social Customer Service Starter Pack, Embedded Appointment Management, Embedded Flows, Close Date Predictions, Cadence Builder Classic (1.0), Externally Built Models in Einstein Discovery, Einstein Automated Contacts (Feb 2025), Salesforce Functions (Jan 31, 2025), Embedded Appointment Management (Jun 17, 2025), Legacy Chat (Feb 14, 2026), Salesforce for Outlook (Dec 2027). 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
Salesforce Winter 25 Release notes are here. Salesforce Overall Learn about new features and enhancements that affect your Salesforce experience overall. August 8: Get early access by signing up for a Pre-Release org Admins can sign up for a pre-release Developer Edition environment, which is full of all the Winter ’25 features to explore to your heart’s content. Developer environments are stand-alone environments where you can learn, build, and get comfortable with features and functionality. If you already had a pre-release org for Summer ’24, you can log back into that one. August 14: Review the Release Notes Search the products you use for release updates in the Release Notes section of Salesforce Help. The notes will go live August 14 and we will share the link here. Get help from the community! With each release, there are a number of blogs by community members who break it down. Check out the Release Readiness Trailblazer Community Group where you can continue to get updates, share your favorite features, and ask questions about the upcoming release. August 19: Be Release Ready with Winter ’25 features for Admins Starting on August 19th, we’ll begin publishing blog posts on the Admin Blog to help you Be Release Ready with Winter ’25 features. Get ready to dive into blog posts featuring Winter ’25 user access highlights and more! As blog posts and more release resources become available, we’ll be updating the Be Release Ready page with all the resources and information you need to get started with Winter ’25. August 29 before 5 p.m. PT: Be sure to refresh your Sandbox Once you’ve explored the pre-release org and reviewed the Release Notes for features that are important to you, it’s time to try out features related to your customizations in your sandbox. This is a great time to evaluate how specific features may be useful or impact the way your organization uses Salesforce. During each release, there is a group of sandboxes slated to remain on the non-preview instance (i.e. the current release) while there is another group of sandboxes that will upgrade to the preview instance. Use the Salesforce Sandbox Preview Guide to determine the plan for your sandbox instance(s). Use the tool where you can search by sandbox instance and then specify what you want to do with your sandbox — stay on the non-preview or move to preview. It will then instruct you to refresh your sandbox to get to the desired instance or inform you that there is no action needed because your sandbox is slated for the desired instance. Contact Tectonic today if you need assistance getting Salesforce release ready. 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
Unlock the Full Potential of Your Salesforce Data with Snowflake At Tectonic, we’ve dedicated years to helping businesses maximize their Salesforce investment, driving growth and enhancing customer experiences. Now, we’re expanding those capabilities by integrating with Snowflake.Imagine the power of merging Salesforce data with other sources, gaining deeper insights, and making smarter decisions—without the hassle of complex infrastructure. Snowflake brings this to life with a flexible, scalable solution for unifying your data ecosystem.In this insight, we’ll cover why Snowflake is essential for Salesforce users, how seamlessly it integrates, and why Tectonic is the ideal partner to help you leverage its full potential. Why Snowflake Matters for Salesforce Users Salesforce excels at managing customer relationships, but businesses today need data from multiple sources—e-commerce, marketing platforms, ERP systems, and more. That’s where Snowflake shines. With Snowflake, you can unify these data sources, enrich your Salesforce data, and turn it into actionable insights. Say goodbye to silos and blind spots. Snowflake is easy to set up, scales effortlessly, and integrates seamlessly with Salesforce, making it ideal for enhancing CRM data across various business functions.The Power of Snowflake for Salesforce Users Enterprise-Grade Security & GovernanceSnowflake ensures that your data is secure and compliant. With top-tier security and data governance tools, your customer data remains protected and meets regulatory requirements across platforms, seamlessly integrating with Salesforce. Cross-Cloud Data SharingSnowflake’s Snowgrid feature makes it easy for Salesforce users to share and collaborate on data across clouds. Teams across marketing, sales, and operations can access the same up-to-date information, leading to better collaboration and faster, more informed decisions. Real-Time Data ActivationCombine Snowflake’s data platform with Salesforce Data Cloud to activate insights in real-time, enabling enriched customer experiences through dynamic insights from web interactions, purchase history, and service touchpoints. Tectonic + Snowflake: Elevating Your Salesforce Experience Snowflake offers powerful data capabilities, but effective integration is key to realizing its full potential—and that’s where Tectonic excels. Our expertise in Salesforce, now combined with Snowflake, ensures that businesses can maximize their data strategies. How Tectonic Helps: Strategic Integration Planning: We assess your current data ecosystem and design a seamless integration between Salesforce and Snowflake to unify data without disrupting operations. Custom Data Solutions: From real-time dashboards to data enrichment workflows, we create solutions tailored to your business needs. Ongoing Support and Optimization: Tectonic provides continuous support, adapting your Snowflake integration to meet evolving data needs and business strategies. Real-World Applications Retail: Integrate in-store and e-commerce sales data with Salesforce for real-time customer insights. Healthcare: Unify patient data from wearables, EMRs, and support interactions for a holistic customer care experience. Financial Services: Enhance Salesforce data with third-party risk assessments, enabling quicker, more accurate underwriting. Looking Ahead: The Tectonic Advantage Snowflake opens up new possibilities for Salesforce-powered businesses. Effective integration, however, requires strategic planning and hands-on expertise. Tectonic has a long-standing track record of helping clients get the most out of Salesforce, and now, Snowflake adds an extra dimension to our toolkit. Whether you want to better manage data, unlock insights, or enhance AI initiatives, Tectonic’s combined Salesforce and Snowflake expertise ensures you’ll harness the best of both worlds. Stay tuned as we dive deeper into Snowflake’s features, such as Interoperable Storage, Elastic Compute, and Cortex AI with Arctic, and explore how Tectonic is helping businesses unlock the future of data and AI. Ready to talk about how Snowflake and Salesforce can transform your business? 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
Snowflake announced on Thursday the general availability of Snowpark Container Services, enabling customers to securely deploy and manage models and applications, including generative AI, within Snowflake’s environment. Initially launched in preview in June 2023, Snowpark Container Services is now a fully managed service available in all AWS commercial regions and in public preview in all Azure commercial regions. Containers are a software method used to isolate applications for secure deployment. Snowflake’s new feature allows customers to use containers to manage and deploy any type of model, optimally for generative AI applications, by securely integrating large language models (LLMs) and other generative AI tools with their data, explained Jeff Hollan, Snowflake’s head of applications and developer platform. Mike Leone, an analyst at TechTarget’s Enterprise Strategy Group, noted that Snowpark Container Services’ launch builds on Snowflake’s recent efforts to provide customers with an environment for developing generative AI models and applications. Sridhar Ramaswamy became Snowflake’s CEO in February, succeeding Frank Slootman, who led the company through a record-setting IPO. Under Ramaswamy, Snowflake has aggressively added generative AI capabilities, including launching its own LLM, integrating with Mistral AI, and providing tools for creating AI chatbots. “There has definitely been a concerted effort to enhance Snowflake’s capabilities and presence in the AI and GenAI markets,” Leone said. “Offerings like Snowpark help AI stakeholders like data scientists and developers use the languages they prefer.” As a result, Snowpark Container Services is a significant new feature for Snowflake customers. “It’s a big deal for the Snowflake ecosystem,” Leone said. “By enabling easy deployment and management of containers within the Snowflake platform, it helps customers handle complex workloads and maintain consistency across development and production stages.” Despite the secure environment provided by Snowflake Container Services, it was revealed in May that the login credentials of potentially 160 customers had been stolen and used to access their data. However, Snowflake has stated there is no evidence that the breach resulted from a vulnerability or misconfiguration of the Snowflake platform. Prominent customers affected include AT&T and Ticketmaster, and Snowflake’s investigation is ongoing. New Capabilities Generative AI can transform business by enabling employees to easily work with data to inform decisions and making trained experts more efficient. Generative AI, combined with an enterprise’s proprietary data, allows users to interact with data using natural language, reducing the need for coding and data literacy training. Non-technical workers can query and analyze data, freeing data engineers and scientists from routine tasks. Many data management and analytics vendors are focusing on developing generative AI-powered features. Enterprises are building models and applications trained on their proprietary data to inform business decisions. Among data platform vendors, AWS, Databricks, Google, IBM, Microsoft, and Oracle are providing environments for generative AI tool development. Snowflake, under Slootman, was less aggressive in this area but is now committed to generative AI development, though it still has ground to cover compared to its competitors. “Snowflake has gone as far as creating their own LLM,” Leone said. “But they still have a way to go to catch up to some of their top competitors.” Matt Aslett, an analyst at ISG’s Ventana Research, echoed that Snowflake is catching up to its rivals. The vendor initially focused on traditional data warehouse capabilities but made a significant step forward with the late 2023 launch of Cortex, a platform for developing AI models and applications. Cortex includes access to various LLMs and vector search capabilities, marking substantial progress. The general availability of Snowpark Container Services furthers Snowflake’s effort to foster generative AI development. The feature provides users with on-demand GPUs and CPUs to run any code next to their data. This enables the deployment and management of any type of model or application without moving data out of Snowflake’s platform. “It’s optimized for next-generation data and AI applications by pushing that logic to the data,” Hollan said. “This means customers can now easily and securely deploy everything from source code to homegrown models in Snowflake.” Beyond security, Snowpark Container Services simplifies model management and deployment while reducing associated costs. Snowflake provides a fully integrated managed service, eliminating the need for piecing together various services from different vendors. The service includes a budget control feature to reduce operational costs and provide cost certainty. Snowpark Container Services includes diverse storage options, observability tools like Snowflake Trail, and streamlined DevOps capabilities. It supports deploying LLMs with local volumes, memory, Snowflake stages, and configurable block storage. Integrations with observability specialists like Datadog, Grafana, and Monte Carlo are also included. Aslett noted that the 2020 launch of the Snowpark development environment enabled users to use their preferred coding languages with their data. Snowpark Container Services takes this further by allowing the use of third-party software, including generative AI models and data science libraries. “This potentially reduces complexity and infrastructure resource requirements,” Aslett said. Snowflake spent over a year moving Snowpark Container Services from private preview to general availability, focusing on governance, networking, usability, storage, observability, development operations, scalability, and performance. One customer, Landing AI, used Snowpark Container Services during its preview phases to develop LandingLens, an application for training and deploying computer vision models. “[With Snowflake], we are increasing access to AI for more companies and use cases, especially given the rapid growth of unstructured data in our increasingly digital world,” Landing AI COO Dan Maloney said in a statement Thursday. Future Plans With Snowpark Container Services now available on AWS, Snowflake plans to extend the feature to all cloud platforms. The vendor’s roadmap includes further improvements to Snowpark Container Services with more enterprise-grade tools. “Our team is investing in making it easy for companies ranging from startups to enterprises to build, deliver, distribute, and monetize next-generation AI products across their ecosystems,” Hollan said. Aslett said that making Snowpark Container Services available on Azure and Google Cloud is the logical next step. He noted that the managed service’s release is significant but needs broader availability beyond AWS regions. “The next step will be to bring Snowpark Container Services to general
Salesforce Data Cloud: Debunking Myths and Unveiling Facts If you’ve been active on LinkedIn, attending recent Salesforce events, or even watching a myriad of sporting events, you’ve likely noticed that Salesforce has evolved beyond just CRM. It’s now CRM + DATA + AI. Although Salesforce has always incorporated these elements, with Einstein AI and data being integral to CRM, the latest innovation lies in the Data Cloud. Data Cloud – Facts and Fiction Data Cloud, formerly known as Salesforce Genie, represents Salesforce’s latest evolution, focusing on enabling organizations to scale and grow in an era where data is the new currency. It is the fastest-growing product in Salesforce’s history, pushing new boundaries of innovation by providing better access to data and actionable insights. As Data Cloud rapidly develops, potential clients often have questions about its function and how it can address their challenges. Here are some common myths about Data Cloud and the facts that debunk them. Myth: Data Cloud Requires MuleSoft Fact: While MuleSoft Anypoint Platform can accelerate connecting commonly used data sources, it is not required for Data Cloud. Data Cloud can ingest data from multiple systems and platforms using several out-of-the-box (OOTB) connectors, including SFTPs, Snowflake, AWS, and more. Salesforce designs its solutions to work seamlessly together, but Data Cloud also offers connector options for non-Salesforce products, ensuring flexibility and integration capabilities beyond the Salesforce ecosystem. Myth: Data Cloud Will De-Duplicate Your Data Fact: Harmonizing data in Data Cloud means standardizing your data model rather than de-duplicating it. Data Cloud maps fields to a common data model and performs “Identity Resolution,” using rules to match individuals based on attributes like email, address, device ID, or phone number. This process creates a Unified Individual ID without automatically de-duplicating Salesforce records. Salesforce intentionally does not function as a Master Data Management (MDM) system. Myth: Data Cloud Will Create a Golden Record Fact: Data Cloud does not create a single, updated record synchronized across all systems (a “golden record”). Instead, it retains original source information, identifies matches across systems, and uses this data to facilitate engagements, known as the Data Cloud Key Ring. For instance, it can recognize an individual across different systems and provide personalized experiences without overwriting original data. Myth: You Can’t Ingest Custom Objects from Salesforce Fact: During the data ingestion process, you can select which objects to ingest from your Salesforce CRM Org, including custom objects. The system identifies the API names of the objects and fields from the data source. Ensuring the Data Cloud integration user has access to the necessary information (similar to assigning Permission Sets) allows you to ingest and map custom objects accordingly. Myth: Data Cloud Requires a Data Scientist and Takes a Long Time to Implement Fact: While implementing Data Cloud involves ingesting, mapping data, running identity resolution, and generating insights, it does not necessarily require a data scientist. Skilled Salesforce Admins can often manage data integration from third-party applications. Effective Data Cloud implementation requires thorough planning and preparation, akin to prepping a room before painting. Identifying use cases and understanding data sources in advance can streamline the implementation process. Myth: Data Cloud is Expensive Fact: Data Cloud operates on a consumption-based pricing model. Engaging in strategic conversations with Salesforce Account Executives can help understand the financial implications. Emphasizing the value of a comprehensive data strategy and considering the five V’s of Big Data—Volume, Variety, Veracity, Value, and Velocity—ensures that your data supports meaningful business outcomes and KPIs. In Summary Salesforce Data Cloud represents a significant evolution in managing and leveraging data within your organization. It helps break down data silos, providing actionable insights to drive organizational goals. Despite initial misconceptions, implementing Data Cloud does not require extensive coding skills or a data scientist. Instead, thorough planning and preparation can streamline the process and maximize efficiency. Understanding the value of a comprehensive data strategy is crucial, as data becomes the new currency. Addressing the five V’s of Big Data ensures that your data supports meaningful business outcomes and KPIs. At Tectonic, our team of certified professionals is ready to assist you on this journey. We offer a Salesforce Implementation Solution package to help you get hands-on with the tool and explore its capabilities. Whether you need help understanding your data sources or defining use cases, our data practice can provide the expertise you need. Talk to Tectonic about Data Cloud and discover how our tailored solutions can help you harness the full potential of your 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
Cyber Group UNC3944 Targets SaaS Platforms like Azure, Salesforce, vSphere, AWS, and Google Cloud UNC3944, also known as “0ktapus” and “Scattered Spider,” has shifted its focus to attacking Software-as-a-Service (SaaS) applications, as reported by Google Cloud’s Mandiant threat intelligence team. This hacking group, previously linked to incidents involving companies such as Snowflake and MGM Entertainment, has evolved its strategies to concentrate on data theft and extortion. Cyber Group Targets SaaS Platforms Attack Techniques UNC3944 exploits legitimate third-party tools for remote access and leverages Okta permissions to expand their intrusion capabilities. One notable aspect of their attacks involves creating new virtual machines in VMware vSphere and Microsoft Azure, using administrative permissions linked through SSO applications for further activities. The group uses commonly available utilities to reconfigure virtual machines (VMs), disable security protocols, and download tools such as Mimikatz and ADRecon, which extract and combine various artifacts from Active Directory (AD) and Microsoft Entra ID environments. Evolving Methods Initially, UNC3944 employed a variety of techniques, but over time, their methods have expanded to include ransomware and data theft extortion. Active since at least May 2022, the group has developed resilience mechanisms against virtualization platforms and improved their ability to move laterally by abusing SaaS permissions. The group also uses SMS phishing to reset passwords and bypass multi-factor authentication (MFA). Once inside, they conduct thorough reconnaissance of Microsoft applications like SharePoint to understand remote connection needs. According to Google Cloud’s Mandiant team, UNC3944’s primary activity is now data theft without using ransomware. They employ expert social engineering tactics, using detailed personal information to bypass identity checks and target employees with high-level access. Social Engineering and Threats Attackers often pose as employees, contacting help desks to request MFA resets for setting up new phones. If help desk staff comply, attackers can easily bypass MFA and reset passwords. If social engineering fails, UNC3944 resorts to threats, including doxxing, physical threats, or releasing compromising material to coerce credentials from victims. Once access is gained, they gather information on tools like VPNs, virtual desktops, and remote work utilities to maintain consistent access. Targeting SaaS and Cloud Platforms UNC3944 targets Okta’s single sign-on (SSO) tools, allowing them to create accounts that facilitate access to multiple systems. Their attacks extend to VMware’s vSphere hybrid cloud management tool and Microsoft Azure, where they create virtual machines for malicious purposes. By operating within a trusted IP address range, they complicate detection. Additional targets include SaaS applications like VMware’s vCenter, CyberArk, Salesforce, CrowdStrike, Amazon Web Services (AWS), and Google Cloud. Office 365 is another focus, with attackers using Microsoft’s Delve tool to identify valuable information. To exfiltrate data, they use synchronization utilities such as Airbyte and Fivetran to transfer information to their own cloud storage. The group also targets Active Directory Federation Services (ADFS) to extract certificates and employ Golden SAML attacks for continued access to cloud applications. They leverage Microsoft 365 capabilities like Office Delve for quick reconnaissance and data mining. Recommendations – Cyber Group Targets SaaS Platforms Mandiant advises deploying host-based certificates with MFA for VPN access, implementing stricter conditional access policies, and enhancing monitoring for SaaS applications. Consolidating logs from crucial SaaS applications and monitoring virtual machine setups can help identify potential breaches. Like1 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
What is Zero-ETL? Zero-ETL represents a transformative approach to data integration and analytics by bypassing the traditional ETL (Extract, Transform, Load) pipeline. Unlike conventional ETL processes, which involve extracting data from various sources, transforming it to fit specific formats, and then loading it into a data repository, Zero-ETL eliminates these steps. Instead, it enables direct querying and analysis of data from its original source, facilitating real-time insights without the need for intermediate data storage or extensive preprocessing. This innovative method simplifies data management, reducing latency and operational costs while enhancing the efficiency of data pipelines. As the demand for real-time analytics and the volume of data continue to grow, ZETL offers a more agile and effective solution for modern data needs. Challenges Addressed by Zero-ETL Benefits of ZETL Use Cases for ZETL In Summary ZETL transforms data management by directly querying and leveraging data in its original format, addressing many limitations of traditional ETL processes. It enhances data quality, streamlines analytics, and boosts productivity, making it a compelling choice for modern organizations facing increasing data complexity and volume. Embracing Zero-ETL can lead to more efficient data processes and faster, more actionable insights, positioning businesses for success in a data-driven world. Components of Zero-ETL ZETL involves various components and services tailored to specific analytics needs and resources: Advantages and Disadvantages of ZETL Comparison: Z-ETL vs. Traditional ETL Feature Zero-ETL Traditional ETL Data Virtualization Seamless data duplication through virtualization May face challenges with data virtualization due to discrete stages Data Quality Monitoring Automated approach may lead to quality issues Better monitoring due to discrete ETL stages Data Type Diversity Supports diverse data types with cloud-based data lakes Requires additional engineering for diverse data types Real-Time Deployment Near real-time analysis with minimal latency Batch processing limits real-time capabilities Cost and Maintenance More cost-effective with fewer components More expensive due to higher computational and engineering needs Scale Scales faster and more economically Scaling can be slow and costly Data Movement Minimal or no data movement required Requires data movement to the loading stage Comparison: Zero-ETL vs. Other Data Integration Techniques Top Zero-ETL Tools Conclusion Transitioning to Zero-ETL represents a significant advancement in data engineering. While it offers increased speed, enhanced security, and scalability, it also introduces new challenges, such as the need for updated skills and cloud dependency. Zero-ETL addresses the limitations of traditional ETL and provides a more agile, cost-effective, and efficient solution for modern data needs, reshaping the landscape of data management and analytics. 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
