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Salesforce Flow Tests

Salesforce Flow Tests: What Are the Limitations? Salesforce Flow Tests are essential for ensuring automation reliability, but they aren’t without their constraints. Recognizing these limitations is key to refining your automation strategy and avoiding potential roadblocks. Here’s an overview of common challenges, along with insights into how you can navigate them to maximize the effectiveness of your testing processes. The Role of Flow Tests in Automation Automated processes in Salesforce are powerful, but they don’t optimize themselves. Proper setup and rigorous testing are essential to ensure that your automations run smoothly. While Salesforce Flow Tests help verify functionality, they have inherent limitations that, if misunderstood, could lead to inefficiencies or rework. By understanding these boundaries, you can make informed decisions to strengthen your overall approach to testing and automation. Key Limitations of Salesforce Flow Tests Final Thoughts Mastering Salesforce Flow Tests means leveraging their strengths while acknowledging their constraints. Optimized automations require careful planning, robust testing, and a clear understanding of the tools’ boundaries. Have questions about improving your Salesforce Flows or testing strategy? Let’s chat and explore ways to fine-tune your automations! 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Collaborative Business Intelligence

Collaborative Business Intelligence

Collaborative BI combines BI tools with collaboration platforms, enabling users to connect data insights directly within their existing workflows. This integration enhances decision-making by reducing misunderstandings and fostering teamwork through real-time or asynchronous discussions about data. In traditional BI, data analysis was handled by data scientists and statisticians who translated insights for business users. However, the rise of self-service BI tools has democratized data access, allowing users of varying technical skills to create and share visualizations. Collaborative BI takes this a step further by embedding BI functions into collaboration platforms like Slack and Microsoft Teams. This setup allows users to ask questions, clarify context, and share reports within the same applications they already use, enhancing data-driven decisions across the organization. One real-life time saver in my experience is being able as a marketer to dig in to our BI and generate lists myself, without depending upon a team of data scientists. Benefits of Collaborative BI Leading Collaborative BI Platforms Several vendors offer collaborative BI solutions, each with unique integrations for communication and data sharing: Collaborative BI bridges data analysis with organizational collaboration, creating an agile environment for informed decision-making and effective knowledge sharing across all levels. 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Mulesoft

MuleSoft Empowering AI Agents

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

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Collaborative Business Intelligence

Collaborative Business Intelligence

Collaborative Business Intelligence: Connecting Data and Teams In today’s data-driven world, the ability to interact with business intelligence (BI) tools is essential for making informed decisions. Collaborative business intelligence (BI), also known as social BI, allows users to engage with their organization’s data and communicate with data experts through the same platforms where they already collaborate. While self-service BI empowers users to generate insights, understanding the data’s context is critical to avoid misunderstandings that can derail decision-making. Collaborative BI integrates BI tools with collaboration platforms to bridge the gap between data analysis and communication, reducing the risks of misinterpretation. Traditional Business Intelligence Traditional BI involves the use of technology to analyze data and present insights clearly. Before BI platforms became widespread, data scientists and statisticians handled data analysis, making it challenging for non-technical professionals to digest the insights. BI evolved to automate visualizations, such as charts and dashboards, making data more accessible to business users. Previously, BI reports were typically available only to high-level executives. However, modern self-service BI tools democratize access, enabling more users—regardless of technical expertise—to create reports and visualize data, fostering better decision-making across the organization. The Emergence of Collaborative BI Collaborative BI is a growing trend, combining BI applications with collaboration tools. This approach allows users to work together synchronously or asynchronously within a shared platform, making it easier to discuss data reports in real time or leave comments for others to review. Whether it’s through Slack, Microsoft Teams, or social media apps, users can receive and discuss BI insights within their usual communication channels. This seamless integration of BI and collaboration tools offers a competitive edge, simplifying the process of sharing knowledge and clarifying data without switching between applications. Key Benefits of Collaborative Business Intelligence Leading Collaborative BI Platforms Here’s a look at some of the top collaborative BI platforms driving innovation in the market: Conclusion Collaborative BI empowers organizations by improving decision-making, democratizing data access, optimizing data quality, and ensuring data security. By integrating BI tools with collaboration platforms, businesses can streamline their operations, foster a culture of data-driven decision-making, and enhance overall efficiency. Choosing the right platform is key to maximizing the benefits of collaborative BI. 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Batch Job Behavior

Batch Job Behavior

By automating specific actions  that you’d normally have to manually initiate, batch jobs make processing large amount of data less tedious and time consuming. If you’ve ever noticed data from batch jobs processes ‘out of order,’ we’ll go over why that’s the case.  Inconsistent Batch Job Behavior Resolution Inconsistent behavior of batches is because batch Apex is an asynchronous process with no SLA, and many customers are sharing the resources, causing it to be slow.  Being an asynchronous process, the system will process the batches only when the system resources are available. There’s no way to prioritize a process, and we don’t provide a SLA for the execution.  Asynchronous Apex In a nutshell, asynchronous Apex is used to run processes in a separate thread, at a later time. An asynchronous process is a process or function that executes a task “in the background” without the user having to wait for the task to finish. You’ll typically use Asynchronous Apex for callouts to external systems, operations that require higher limits, and code that needs to run at a certain time. The key benefits of asynchronous processing include: User efficiency Let’s say you have a process that makes many calculations on a custom object whenever an Opportunity is created. The time needed to execute these calculations could range from a minor annoyance to a productivity blocker for the user. Since these calculations don’t affect what the user is currently doing, making them wait for a long running process is not an efficient use of their time. With asynchronous processing the user can get on with their work, the processing can be done in the background and the user can see the results at their convenience. Scalability By allowing some features of the platform to execute when resources become available at some point in the future, resources can be managed and scaled quickly. This allows the platform to handle more jobs using parallel processing. Higher Limits Asynchronous processes are started in a new thread, with higher governor and execution limits. And to be honest, doesn’t everyone want higher governor and execution limits? Asynchronous Apex comes in a number of different flavors. We’ll get into more detail for each one shortly, but here’s a high level overview. Type Overview Common Scenarios Future Methods Run in their own thread, and do not start until resources are available. Web service callout. Batch Apex Run large jobs that would exceed normal processing limits. Data cleansing or archiving of records. Queueable Apex Similar to future methods, but provide additional job chaining and allow more complex data types to be used. Performing sequential processing operations with external Web services. Scheduled Apex Schedule Apex to run at a specified time. Daily or weekly tasks. It’s also worth noting that these different types of asynchronous operations are not mutually exclusive. For instance, a common pattern is to kick off a Batch Apex job from a Scheduled Apex job. Increased Governor and Execution Limits One of the main benefits of running asynchronous Apex is higher governor and execution limits. For example, the number of SOQL queries is doubled from 100 to 200 queries when using asynchronous calls. The total heap size and maximum CPU time are similarly larger for asynchronous calls. Not only do you get higher limits with async, but also those governor limits are independent of the limits in the synchronous request that queued the async request initially. That’s a mouthful, but essentially, you have two separate Apex invocations, and more than double the processing capability. This comes in handy for instances when you want to do as much processing as you can in the current transaction but when you start to get close to governor limits, continue asynchronously. How Asynchronous Processing Works Asynchronous processing, in a multitenant environment, presents some challenges: Ensure fairness of processing Make sure every customer gets a fair share of processing resources. Ensure fault tolerance Make sure no asynchronous requests are lost due to equipment or software failures. The platform uses a queue-based asynchronous processing framework. This framework is used to manage asynchronous requests for multiple organizations within each instance. The request lifecycle is made up of three parts: Enqueue The request gets put into the queue. This could be an Apex batch request, future Apex request or one of many others. The platform will enqueue requests along with the appropriate data to process that request. Persistence The enqueued request is persisted. Requests are stored in persistent storage for failure recovery and to provide transactional capabilities. Dequeue The enqueued request is removed from the queue and processed. If the processing fails, transaction control ensures that requests are not lost. Each request is processed by a handler. The handler is the code that performs functions for a specific request type. Handlers are executed by a finite number of worker threads on each of the application servers that make up an instance. The threads request work from the queuing framework and when received, start a specific handler to do the work. Resource Conservation Asynchronous processing has lower priority than real-time interaction via the browser and API. To ensure there are sufficient resources to handle an increase in computing resources, the queuing framework monitors system resources such as server memory and CPU usage and reduce asynchronous processing when thresholds are exceeded. This is a fancy way of saying that the multitenant system protects itself. If an org tries to “gobble up” more than its share of resources, asynchronous processing is suspended until a normal threshold is reached. The long and short of it is that there’s no guarantee on processing time, but it’ll all work out in the end. 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

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Sensitive Information De-identification

Sensitive Information De-identification

Using Google Cloud Data Loss Prevention with Salesforce for Sensitive Data Handling This insight discusses the transition from detecting and classifying sensitive data to preventing data loss using Google Cloud Data Loss Prevention (DLP). Sensitive Information De-identification for Salesforce is used as the data source to demonstrate how personal, health, credential, and financial information can be de-identified in unstructured data in near real-time. Overview of Google Cloud DLP Google Cloud DLP is a fully managed service designed to help discover, classify, and protect sensitive data. It easily transitions from detection to prevention by offering services that mask sensitive information and measure re-identification risk. Objective The goal was to demonstrate the ability to redact sensitive information in unstructured data at scale. Specifically, it aimed to determine whether sensitive data, such as credit card numbers, tax file numbers, and health care numbers, entered into Salesforce communications (Emails, Files, and Chatter) could be detected and redacted. Constraints Tested De-identifying Data with Google Cloud DLP API Instead of detailing the setup, this section focuses on the key areas of design. Google Design Decisions Supporting Disparate Data Sources with Multiple Integration Patterns and Redundant Design Salesforce Data Source De-identification targets include email addresses, Australian Medicare card numbers, GCP API keys, passwords, and credit card numbers. Credit card numbers are masked with asterisks, while other sensitive data is replaced with information types for readability (e.g., [email protected] becomes [redacted-email-address]). Sample Requests to Google De-identification Service JSON Structure to De-identify Text Using Google Cloud DLP API jsonCopy code{ // JSON structure } JSON Structure to De-identify Images Using Google Cloud DLP API jsonCopy code{ // JSON structure } Salesforce Design Decisions Redundancy and Batch Processing A scheduled batch job allows for recovery by polling unprocessed records. To handle large data volumes (e.g., 360,000 records over 5 days), the Salesforce BULK API is used to process queries and updates in large batch sizes, reducing the number of API calls. Sensitive Information De-identification Google Cloud Data Loss Prevention allows detecting and protecting assets with sensitive information, supporting a wide range of use cases across an enterprise. Proven Capabilities: Considerations and Lessons Learned Enhanced Email: Redacting tasks and EmailMessage records, handling read-only EmailMessage records by deleting and recreating them. Files: The architecture assumes files with sensitive data can be deleted and replaced with redacted versions. Audit Fields: Ensure setting CreatedDate and LastModifiedDate fields using original record dates. Field History Tracking: Avoid tracking fields intended for de-identification, tracking shadow fields instead. Image De-identification: Limited to JPEG, BMP, and PNG formats, with DOCX and PDF not yet supported. 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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