Summary A Netherlands-based non-profit sustainability certification organisation reduced manual certification configuration time from hours to mere seconds using Microsoft Power Apps, implemented by CloudFronts. CloudFronts configured a multi-level assessment framework — Scope, Category, Requirement, Criteria — to automate 100% of assessment generation based on user-selected certification types and versions. The solution integrated Microsoft Power Apps with Azure Blob Storage to provide a secure, centralised repository for thousands of pieces of certification evidence, linked directly to each requirement record. Microsoft Dynamics 365 Customer Service was configured to streamline global applicant inquiries with automated case routing across Marketing, Finance, and Info queues. Business impact: eliminated manual configuration errors, provided real-time progress visibility for global applicants, and established a scalable digital foundation for global circular economy standards. Table of Contents 01 Summary 02 Introduction 03 The Business Problem 04 The Solution 05 Implementation 06 Business Impact 07 FAQs 08 Conclusion Introduction In a world where manufacturers and brands are under increasing pressure to prove the sustainability credentials of their products, the rigour and speed of certification processes can directly determine an organisation’s ability to scale its global mission. For certification bodies operating across multiple geographies, managing assessments, evidence, and applicant communication through fragmented manual processes is a bottleneck that no amount of headcount can solve. For one Netherlands-based non-profit at the forefront of the global circular economy movement, this bottleneck was real and growing. Their certification programme, built on a rigorous multi-level standard covering material safety, circularity, and responsible production, was being administered through massive Excel files, disconnected email threads, and manual document searches. This blog documents how CloudFronts partnered with this organisation to replace those fragmented processes with a unified, automated certification platform built on Microsoft Power Apps, Azure Blob Storage, Dynamics 365 Customer Service, and Power Automate, reducing certification setup time from hours to under thirty seconds. The Business Problem The organisation operates as the leading global authority on circular economy certification, serving manufacturers and brands worldwide. Their certification programme evaluates products across categories like Material Health and Circularity, across multiple standard versions — v3.1 and v4.1 — each with its own hierarchy of scopes, categories, requirements, and criteria. Before partnering with CloudFronts, this complexity was managed almost entirely by hand: Each new certification application required assessors to manually configure assessment structures from sprawling Excel files with hundreds of rows, a process that took two to four hours per applicant. Supporting evidence such as product test reports, material declarations, and third-party certificates was stored without a structured system, making retrieval slow and validation unreliable. Neither applicants nor internal assessors had real-time visibility into application status or outstanding requirements, creating persistent communication delays. Managing different certification versions across different scopes manually made scaling the programme globally nearly impossible without proportionally growing the team. The organisation needed a platform that could encode their complex certification logic, automate the heavy lifting of assessment generation, and give every stakeholder a single, reliable view of the certification pipeline. The Solution CloudFronts implemented a comprehensive digital certification ecosystem anchored by a custom Microsoft Power Apps application — the Certification Manager. The platform automates the core logic of the certification standard end-to-end, from application intake through assessment generation, evidence management, and case resolution. Key Components Microsoft Power Apps Core Certification Manager application handling applications, multi-level assessments, and the applicant-facing UI. Azure Blob Storage Secure, centralised repository for all certification evidence, linked directly to individual requirement records. Dynamics 365 Customer Service Configured to streamline global applicant inquiries with automated case routing across Marketing, Finance, and Info queues. Microsoft Power Automate Automation layer handling document upload workflows and notification triggers throughout the certification lifecycle. How It Works, At a Glance The centrepiece of the solution is a version-driven automation engine. When an assessor creates a new certification application and selects the standard version and scope, the backend logic automatically generates the complete assessment structure — all categories, requirements, and criteria — without any manual configuration. What previously took hours now takes under thirty seconds. A custom HTML-based interface within Power Apps provides visual progress indicators, allowing assessors to track completion rates across requirements at a glance. All supporting evidence is stored in Azure Blob Storage and linked directly to the specific requirement record it supports, creating a fully auditable, ISO 17065-compliant evidence trail. Implementation 1 Step 1 Certification Scheme Definition and Version Logic The foundation of the platform is the Certification Scheme Definition module. CloudFronts built a backend logic engine that stores the full structure of each certification version including all scopes, categories, requirements, and criteria as configuration data rather than hardcoded templates. When a user selects a version and scope combination, this engine automatically pulls the correct downstream structure and generates it on the application record. Updates to global standards can be deployed instantly by updating the configuration, with no changes to the application logic required. The four-level assessment hierarchy: Scope, Category, Requirement, Criteria is the structural backbone of the entire certification standard, now encoded directly into the platform. 2 Step 2 Automated Assessment Generation Once the version and scope are selected on a new application, the platform’s automation engine generates the full assessment structure in under thirty seconds, replacing a manual Excel-driven process that previously took two to four hours per applicant. The generated assessment is displayed through a custom HTML interface inside Power Apps, with visual progress indicators showing completion rates at the category and requirement level. Assessors can immediately see which requirements are outstanding, which have linked evidence, and which are ready for review. 3 Step 3 Evidence Management via Azure Blob Storage A core architectural decision was to decouple evidence storage from the Power Platform’s native Dataverse storage. CloudFronts integrated Azure Blob Storage as the document repository, with each uploaded file linked directly to the specific requirement record it supports within Power Apps. This approach delivers high-performance scalability for large evidence files while significantly reducing long-term storage costs compared to storing files natively in Dataverse. Power Automate flows handle upload automation and trigger notifications … Continue reading How a Netherlands-Based Nonprofit Achieved Global Scalability with Microsoft Dynamics 365 CRM and Power Platform →

Summary This blog highlights how Azure Logic Apps was used to overcome a critical API limitation encountered during the integration of Zoho People with FNO for payroll management. During the implementation for a global manufacturing hardware enterprise, we discovered that Zoho’s API allows a maximum of 200 records to be fetched in a single request. While this limitation may not impact smaller organizations, it creates significant challenges for enterprises managing large employee datasets. To address this issue, a scalable Azure Logic Apps solution was developed that dynamically retrieves records in batches, consolidates the results, and returns a complete dataset for downstream processing. This blog explains: Table of Contents 1. Customer Scenario During the implementation of a payroll integration between Zoho People and FNO, employee master data needed to be synchronized automatically to support payroll processing. The organization maintained a large workforce within Zoho People, and payroll operations depended on accurate employee data being transferred to downstream systems. As the integration design progressed, a significant limitation was identified within Zoho’s API framework. The API could return a maximum of 200 records per request. For organizations with hundreds or thousands of employees, this restriction created a challenge in retrieving complete employee datasets efficiently. 2. Business Challenge The integration required access to the full employee dataset from Zoho People. However, the following challenges emerged: Limited API Response Size Zoho’s API only returns 200 records per request. Large Employee Dataset The organization maintained significantly more than 200 employee records. Manual Pagination Not Feasible Static API calls would require manual intervention or complex custom development. Scalability Concerns As employee counts continued to grow, the solution needed to support future expansion without requiring redesign. The objective was to create a scalable and automated mechanism capable of retrieving all employee records regardless of volume. 3. Integration Architecture The solution architecture follows a simple but highly scalable pattern. Process Flow 4. Configuration Steps Step 1: Add HTTP Trigger Step 2: Initialize Variables Step 3: Do Until Loop Step 4: HTTP Request Action Step 5: Output Variable Step 6: Compose Variable Step 7: Append to Array Variable Step 8: Set Variable Step 8: Increment Variable Step 9: Add Response Trigger 5. Why Azure Logic Apps? Azure Logic Apps was instrumental in creating a flexible and efficient solution. Key capabilities that made Logic Apps the ideal choice included: Dynamic Variable Management Allows runtime manipulation of counters and arrays. Scalable Workflow Execution Supports large datasets without requiring custom application development. Native API Integration Provides seamless connectivity with REST-based services. Low-Code Development Accelerates implementation and simplifies maintenance. Enterprise Reliability Offers monitoring, logging, and error-handling capabilities required for production environments. 6. Outcome The final solution successfully overcame Zoho’s API record limitation. The Logic App automatically: This approach ensured the success of the Zoho-FNO integration while maintaining scalability for future business growth. 7. Business Impact 1] Fully Automated Data Retrieval Employee data is retrieved without manual intervention. 2] Improved Scalability The solution can support organizations with thousands of employee records. 3] Reduced Development Complexity Logic Apps eliminated the need for extensive custom coding. 4] Faster Integration Processing Data retrieval occurs efficiently through automated pagination. 5] Improved Reliability Built-in monitoring and error handling improve operational stability. 6] Future-Proof Architecture The solution continues to perform effectively as employee counts grow. To conclude, Integration projects often reveal platform-specific limitations that require creative problem-solving. In this implementation, Zoho’s 200-record API limitation had the potential to impact payroll synchronization for a growing workforce. By leveraging Azure Logic Apps, we developed a scalable and automated solution capable of dynamically retrieving and consolidating employee data regardless of record volume. The solution not only resolved the immediate challenge but also established a reliable and future-ready integration framework capable of supporting continued organizational growth. For organizations facing similar API limitations, Azure Logic Apps provides a powerful platform for building scalable, low-code integration solutions that simplify complex data processing requirements.

Sustainability certification is one of the most operationally demanding programs a nonprofit can run. It is not just a badge on a product, it is a multi-year, multi-stakeholder process involving manufacturers, independent assessment bodies, scoring frameworks, document issuance, and public transparency requirements. When you are managing thousands of products across global industries, the cracks in a manual, spreadsheet-driven operation show up fast. This is exactly the situation a Netherlands-based nonprofit found itself in. The organization administers a globally recognized product sustainability certification program, assessing products across five dimensions: material health, product circularity, clean air and climate protection, water and soil stewardship, and social fairness. Products move through certification levels Bronze, Silver, Gold, and Platinum across a lifecycle that spans application, third-party assessment, issuance, and periodic recertification every three years. As certification volumes grew, so did the operational complexity. Disconnected tools, manual document preparation, and no single place to track everything meant the team was spending more time managing the process than running it. Rather than bolt on yet another external tool, the organization made a deliberate architectural choice: build the entire certification management platform inside Microsoft Dynamics 365, extend it with Azure Function Apps for automation, and expose public APIs for ecosystem transparency. The Goal Build a unified, scalable certification lifecycle management system inside Dynamics 365 that automates document generation, manages logo assets, and exposes public APIs for published certification data — all without introducing new platform dependencies. The Business Problem To understand what was built, you first need to understand what was broken. The organization’s operational teams were trying to answer some fairly fundamental questions every single day — What is the current certification status of a given product? Which products are approaching their recertification deadline? Which assessment body certified a product and when? Is the certificate document ready for issuance? None of these questions had a reliable, centralized answer. Certification records lived across disconnected spreadsheets and email threads, which meant any “current” view of a product’s status was only as accurate as the last person who updated a row. Certificate documents were manually composed for every issuance a slow, error-prone process that created formatting inconsistencies and delayed the experience for certified manufacturers. Logo assets were managed informally, with no version control or consistent delivery process. No Single Source of Truth Certification records scattered across spreadsheets and email threads with no reliable current view. Manual Document Creation Every certificate composed by hand slow, inconsistent, and a bottleneck manufacturers felt directly. Zero Public Transparency External stakeholders relied on manually updated static pages with no programmatic access to live data. Unscalable Operations Growing program volumes with no automation meant every new product added to the manual workload. The Solution Architecture The platform was designed around one principle: build close to where the operational data already lives, and automate at the right trigger points rather than everywhere at once. The solution runs on three deliberate layers. Critically, this architecture avoided over-engineering entirely — no separate data warehouse, no heavy ETL pipeline, no dedicated certification SaaS platform requiring its own licensing and maintenance. Everything runs inside the Microsoft ecosystem. 1 Data Layer — Custom Dynamics 365 Tables Purpose-built Dataverse tables that mirror the certification domain exactly, products, certification events, assessment bodies, category scores, and logo assets all in a single relational, auditable structure. 2 Automation Layer — Azure Function Apps, Dynamics Plugins Two event-driven Function Apps sit alongside the CRM one for certificate document generation, one for logo package delivery, both triggered by real state changes in the certification lifecycle, not a schedule. 3 Transparency Layer — Public REST APIs Public-facing APIs expose published certification data to external stakeholders, brands, retailers, regulators, and third-party platforms without any manual data exchange with the organization. Custom Dynamics 365 Data Model The data model is the foundation everything else rests on. Rather than forcing certification concepts into standard CRM entities that were never designed for this domain, the team built purpose-specific custom tables inside Dataverse that mirror how the certification program actually works. Product data Core product records, variants, and identifiers — the foundational layer that everything else references. Application handling Applications, assessments, category and requirements assessments — all managed within accounts. Assessment bodies and related workflows live here too. Public-facing entities Public tables for products, certifications, certificates, and product variants — the data layer that powers external visibility and API exposure. Together, these layers gave the organisation a complete, relational view of every certified product across its full lifecycle — all within a single operational platform. Certificate Document Generation via Azure Function App Before this system existed, every certificate document was created by hand. Someone would take a template, fill in the product details, format it, check it, and send it. For an organization issuing certificates across thousands of products, this was not just slow — it was a source of constant inconsistency and a bottleneck that manufacturers felt directly. The Azure Function App for certificate generation eliminated this entirely. Here is how it works end to end: How It Works ⚡ Trigger Certification record reaches the correct status in Dynamics 365 → 🔍 Fetch Pulls record + product data via Dynamics 365 Web API → 📄 Generate Selects correct template, populates all fields, generates document → 🔗 Store & Link Saves document and links it back to the certification record What this means in practice is that certificate issuance is now consistent, fast, and entirely hands-off for the operational team. Formatting is guaranteed every time because the template logic is defined once and applied uniformly. The function also runs independently of the CRM interface — making it resilient and reusable across multiple trigger scenarios, including bulk recertification processing. The impact: A task that previously required manual effort for every single issuance now requires none. Eliminated entirely. Logo Image Generation via Azure Function App A certified product comes with more than a document — it comes with the right to use the certification mark. For manufacturers, that logo is a commercial asset. It goes … Continue reading How a Netherlands-Based Non-Profit Transformed Certification Management with Dynamics 365 and Azure Functions →

Why Incremental Loads Matter When integrating data from external systems, whether it’s a CRM, an ERP like Business Central, or an HR platform like Zoho People, pulling all data every time is expensive, slow, and unnecessary. The smarter approach is to track what has changed since the last successful run and fetch only that delta. This is the core idea behind an incremental data pipeline: identify a timestamp or sequence field in your source system, persist the last-known watermark, and use it as a filter on your next API call. Azure Logic Apps, paired with Azure Table Storage as a lightweight checkpoint store, gives you everything you need to implement this pattern without managing any infrastructure. Architecture Overview Instead of one large workflow doing everything, we separate responsibilities. One Logic App handles scheduling and orchestration. Another handles actual data extraction. Core components: 3. Metadata Design (Azure Table) Instead of hardcoding entity names and fields inside Logic Apps, we define them in Azure Table Storage. Example structure: PartitionKey RowKey IncrementalField displayName entity businesscentral 1 systemCreatedAt Vendor Ledger Entry vendorLedgerEntries zohopeople 1 modifiedtime Leave leave Briefly, this table answers three questions: – What entity should be extracted?– Which column defines incremental logic?– What was the last successful checkpoint? When you want to onboard a new entity, you add a row. No redesign needed. 4. Logic App 1 – Scheduler Trigger: Recurrence (for example, every 15 minutes) Steps: This Logic App should not call APIs directly. Its only job is orchestration. Keep it light. 5. Logic App 2 – Incremental Processor Trigger: HTTP (called from Logic App 1) Functional steps: Example: This is where the real work happens. 6. Checkpoint Strategy Each entity must maintain: – LastSuccessfulRunTime– Status– LastRecordTimestamp After successful extraction: Checkpoint = max(modifiedOn) from extracted data. This ensures: Checkpoint management is the backbone of incremental loading. If this fails, everything fails. This pattern gives you a production-grade incremental data pipeline entirely within Azure’s managed services. By centralizing entity configuration and watermarks in Azure Table Storage, you create a data-driven pipeline where adding a new integration is as simple as inserting a row — no code deployment required. The two-Logic-App architecture cleanly separates orchestration from execution, enables parallel processing, and ensures your pipeline is resilient to failures through checkpoint-based watermark management. Whether you’re pulling from Business Central, Zoho People, or any REST API that exposes a timestamp field, this architecture scales gracefully with your data needs. Explore the case study below to learn how Logic Apps were implemented to solve key business challenges: Ready to deploy AIS to seamlessly connect systems and improve operational cost and efficiency? Get in touch with CloudFronts at transform@cloudfronts.com.

Managing and processing large datasets efficiently is a key requirement in modern data engineering. Azure Databricks, an optimized Apache Spark-based analytics platform, provides a seamless way to handle such workflows. This blog will explore how PySpark and SQL can be combined to dynamically process, and clean data using the medallion architecture (Only Raw → Silver) and store the results in Azure Blob Storage as PDFs. Understanding the Medallion Architecture: – The medallion architecture follows a structured approach to data transformation: Aggregated Layer (Gold): Optimized for analytics, reports, and machine learning. In our use case, we extract raw tables from Databricks, clean them dynamically, and store the refined data into the silver schema. Key technologies / dependencies used: – Step-by-Step Code Breakdown 1. Setting Up the Environment Install & import necessary libraries The above command installs reportlab, which is used to generate PDFs. This imports essential libraries for data handling, visualization, and storage. 2. Connecting to Azure Blob Storage This snippet authenticates the Databricks notebook with Azure Blob Storage and prepares a connection to upload the final PDFs; Initiates the Spark Session as well. 3. Cleaning Data: Raw to Silver Layer Fetch all raw tables This dynamically removes NULL values from raw data and creates a cleaned table in the silver layer. 4. Verifying and comparing the Raw and the Cleaned (Silver) 4. Converting Cleaned Data to PDFs 5. Converting Cleaned Data to PDFs Output at the Azure Storage Container This process reads cleaned tables, converts them into PDFs with structured formatting, and uploads them to Azure Blob Storage. 6. Automating cleaning at Databricks at fixed scheduleThis is automated by scheduling the notebook & it’s associated compute instance to run at fixed intervals and timestamps. Further actions: – Why Store Data in Azure Blob Storage? To conclude, by leveraging Databricks, PySpark, SQL, ReportLab, and Azure Blob Storage, we have automated the pipeline from raw data ingestion to cleaned and formatted PDF reports. This approach ensures: a. Efficient data cleansing using SQL queries dynamically. b. Structured data transformation within the medallion architecture. c. Seamless storage and accessibility through Azure Blob Storage. This methodology can be extended to include Gold Layer processing for advanced analytics and reporting. We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudFronts.com

In modern enterprises, documents stored across platforms like SharePoint often remain underutilized due to the lack of intelligent search capabilities. What if your organization could automatically extract meaning from those documents—turning them into searchable vectors for advanced retrieval systems? That’s exactly what we’ve achieved by integrating Azure Logic Apps with Azure AI Search. Workflow Overview Whenever a user uploads a file to a designated SharePoint folder, a scheduled Azure Logic App is triggered to: Once stored, a scheduled Azure Cognitive Search Indexer kicks in. This indexer: Technologies / resources used: –-> SharePoint: A common document repository for enterprise users, ideal for collaborative uploads. -> Azure Logic Apps: Provides low-code automation to monitor SharePoint for changes and sync files to Blob Storage. It ensures a reliable, scheduled trigger mechanism with minimal overhead. -> Blob Storage: Serves as the staging ground where documents are centrally stored for indexing—cheaper and more scalable than relying solely on SharePoint connectors. -> Azure AI Search (Cognitive Search): The intelligence layer that runs a skillset pipeline to extract, transform, and vectorize the content, enabling semantic search, multimodal RAG (Retrieval Augmented Generation), and other AI-enhanced scenarios. Why Not Vectorize Directly from SharePoint? Reference:-1. https://learn.microsoft.com/en-us/azure/search/search-howto-index-sharepoint-online2. https://learn.microsoft.com/en-us/azure/search/search-howto-indexing-azure-blob-storage How to achieve this? Stage 1: – Logic App to sync Sharepoint files to blob Firstly, create a designated Sharepoint directory to upload the required documents for vectorization. Then create the logic app to replicate the files along with it’s format and properties to the associated blob storage – 1] Assign the site address and the directory name where the documents are uploaded in Sharepoint – In the trigger action “When an item is created or modified”. 2] Assign a recurrence frequency, start time and time zone to check/verify for new documents and keep the blob container updated. 3] Add an action component – “Get file content using path”; and dynamically provide the full path (includes file extension), from the trigger 4] Finally, add an action to create blobs in the designated container that would be vectorized – provide the storage acc. name, directory path, the name of blob (Select to dynamically get the file name with extension for the trigger), blob content (from the get file content action). 5] On successfully saving & running this logic app, either manually or on trigger, the files are replicated in it’s exact form to the blob storage. Stage 2 :- Azure AI Search resource to vectorize the files in blob storage In Azure Portal (Home – Microsoft Azure), search for Azure AI Search service, and provide the necessary details, based on your requirement select a pricing tier. Once resource is successfully created, select “Import & vectorize data” From the 2 options – RAG and Multimodal RAG Index, select the latter one.RAG combines a retriever (to fetch relevant documents) with a generative language model (to generate answers) using text-only data. Multimodal RAG extends the RAG architecture to include multiple data types such as text, images, tables, PDFs, diagrams, audio, or video. Workflow: Now follow the steps and provide the necessary details for the index creation Enable deletion tracking, to remove the records of deleted documents from the index Provide a document intelligence resource to enable OCR, and to get location metadata for multiple document types. Select image verbalization (to verbalize text in images) or multimodal embedding to vectorize the whole image. Assign the LLM model for generating the embeddings for the text/images provide an image output location, to store images extracted from the files Assign a schedule to refresh the indexer and to keep the search index up to date with new documents. Once successfully created, search keywords in the search explorer of the index, to verify the vectorization, the results are provided based on it’s relevance and score/distance, to the user’s search query. Let us test this index in Custom Copilot Agent , by importing this index as an azure ai search knowledge source. On fetching details of certain document specific information, the index is searched for the most appropriate information, and the result is rendered in readable format by generative AI.  We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfronts.com.

Managing multiple Dynamics 365 integrations across environments often becomes complex when each integration depends on static or hardcoded configuration values like API URLs, headers, secrets, or custom parameters. We faced similar challenges until we centralized our configuration strategy using Azure Blob Storage to host the configs and Logic Apps to dynamically fetch and apply them during execution. In this blog, we’ll walk through how we implemented this architecture and simplified config management across our D365 projects. Why We Needed Centralized Config Management In projects with multiple Logic Apps and D365 endpoints: Key problems: Solution Architecture Overview Key Components: Workflow: Step-by-Step Implementation Step 1: Store Config in Azure Blob Storage Example JSON: json CopyEdit {   “apiUrl”: “https://externalapi.com/v1/”,   “apiKey”: “xyz123abc”,   “timeout”: 60 } Step 2: Build Logic App to Read Config Step 3: Parse and Use Config Step 4: Apply to All Logic Apps Benefits of This Approach To conclude, centralizing D365 integration configs using Azure Blob and Logic Apps transformed our integration architecture. It made our systems easier to maintain, more scalable, and resilient to changes.Are you still hardcoding configs in your Logic Apps or Power Automate flows? Start organizing your integration configs in Azure Blob today, and build workflows that are smart, scalable, and maintainable. I hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfronts.com.

If you’ve worked with Dynamics 365 CRM for any serious integration project, you’ve probably used Azure Logic Apps. They’re great — visual, no-code, and fast to deploy. But as your integration needs grow, you quickly hit complexity: multiple entities, large volumes, branching logic, error handling, and reusability. That’s when architecture becomes critical. In this blog, I’ll share how we built a modular, scalable, and reusable integration architecture using Logic Apps + Azure Functions + Azure Blob Storage — with a config-driven approach. Whether you’re syncing data between D365 and Finance & Operations, or automating CRM workflows with external APIs, this post will help you avoid bottlenecks and stay maintainable. Architecture Components Component Purpose Parent Logic App Entry point, reads config from blob, iterates entities Child Logic App(s) Handles each entity sync (Project, Task, Team, etc.) Azure Blob Storage Hosts configuration files, Liquid templates, checkpoint data Azure Function Performs advanced transformation via Liquid templates CRM & F&O APIs Source and target systems Step-by-Step Breakdown 1. Configuration-Driven Logic We didn’t hardcode URLs, fields, or entities. Everything lives in a central config.json in Blob Storage: { “integrationName”: “ProjectToFNO”,   “sourceEntity”: “msdyn_project”,   “targetEntity”: “ProjectsV2”,   “liquidTemplate”: “projectToFno.liquid”,   “primaryKey”: “msdyn_projectid” } 2. Parent–Child Logic App Model Instead of one massive workflow, we created a parent Logic App that: Each child handles: 3. Azure Function for Transformation Why not use Logic App’s Compose or Data Operations? Because complex mapping (especially D365 → F&O) quickly becomes unreadable. Instead: {   “ProjectName”: “{{ msdyn_subject }}”,   “Customer”: “{{ customerid.name }}” } 4. Handling Checkpoints For batch integration (daily/hourly), we store last run timestamp in Blob: {   “entity”: “msdyn_project”,   “modifiedon”: “2025-07-28T22:00:00Z” } This allows delta fetches like: ?$filter=modifiedon gt 2025-07-28T22:00:00Z After each run, we update the checkpoint blob. 5. Centralized Logging & Alerts We configured: This helped us track down integration mismatches fast. Why This Architecture Works Need How It’s Solved Reusability Config-based logic + modular templates Maintainability Each Logic App has one job Scalability Add new entities via config, not code Monitoring Blob + Monitor integration Transformation complexity Handled via Azure Functions + Liquid Key Takeaways To conclude, this architecture has helped us deliver scalable Dynamics 365 integrations, including syncing Projects, Tasks, Teams, and Time Entries to F&O all without rewriting Logic Apps every time a client asks for a tweak. If you’re working on medium to complex D365 integrations, consider going config-driven and breaking your workflows into modular components. It keeps things clean, reusable, and much easier to maintain in the long run. I hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfronts.com.