As organizations scale their analytics platforms, governance often becomes the hardest problem to solve. Data may be accurate, pipelines may run on time, and reports may look correct, but without proper governance, the platform becomes fragile. We see this pattern frequently in environments where Power BI reporting has grown around a mix of SQL Server databases, direct Dataverse connections, shared storage accounts, and manually managed permissions. Over time, access control becomes inconsistent, ownership is unclear, and even small changes introduce risk. Unity Catalog addresses this problem by introducing a centralized, consistent governance layer across Databricks and downstream analytics tools like Power BI. The Governance Problem Most Teams Face In many data platforms, governance evolves as an afterthought. Access is granted at different layers depending on urgency rather than design. Common symptoms include: As reporting expands across departments like Finance, HR, PMO, and Operations, this fragmented governance model becomes difficult to control and audit. Why Unity Catalog Changes the Governance Model Unity Catalog introduces a unified governance layer that sits above storage and compute. Instead of managing permissions at the file or database level, governance is applied directly to data assets in a structured way. At its core, Unity Catalog provides: This shifts governance from an operational task to an architectural capability. A Structured Data Hierarchy That Scales Unity Catalog organizes data into a simple, predictable hierarchy: Catalog → Schema → Table This structure brings clarity to large analytics environments. Business domains can be separated cleanly, such as CRM, Finance, HR, or Projects, while still being governed centrally. For Power BI teams, this means datasets are easier to discover, understand, and trust. There is no ambiguity about where data lives or who owns it. Centralized Access Control Without Storage Exposure One of the biggest advantages of Unity Catalog is that access is granted at the data object level, not the storage level. Instead of giving Power BI users or service principals direct access to storage accounts, permissions are granted on catalogs, schemas, or tables. This significantly reduces security risk and simplifies access management. From a governance perspective, this enables: Power BI connects only to governed datasets, not raw storage paths. Cleaner Integration with Power BI When Power BI connects to Delta tables governed by Unity Catalog, the reporting layer becomes simpler and more secure. Benefits include: This model works especially well when combined with curated Gold-layer tables designed specifically for reporting. Governance at Scale, Not Just Control Unity Catalog is not only about restricting access. It is about enabling teams to scale responsibly. By defining ownership, standardizing naming, and centralizing permissions, teams can onboard new data sources and reports without reworking governance rules each time. This is particularly valuable in environments where multiple teams build and consume analytics simultaneously. Why This Matters for Decision Makers For leaders responsible for data, analytics, or security, Unity Catalog offers a way to balance speed and control. It allows teams to move quickly without sacrificing governance. Reporting platforms become easier to manage, easier to audit, and easier to extend as the organization grows. More importantly, it reduces long-term operational risk by replacing ad-hoc permission models with a consistent governance framework. To conclude, strong governance is not about slowing teams down. It is about creating a structure that allows analytics platforms to grow safely and sustainably. Unity Catalog provides that structure for Databricks and Power BI environments. By centralizing access control, standardizing data organization, and removing the need for direct storage exposure, it enables a cleaner, more secure analytics foundation. For organizations modernizing their reporting platforms or planning large-scale analytics initiatives, Unity Catalog is not optional. It is foundational. If your Power BI and Databricks environment is becoming difficult to govern as it scales, it may be time to rethink how access, ownership, and data structure are managed. We have implemented Unity Catalog–based governance in real enterprise environments and have seen the impact it can make. If you are exploring similar initiatives or evaluating how to strengthen governance across your analytics platform, we are always open to sharing insights from real-world implementations. 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

When integrating Dynamics 365 with external systems, one of the first decisions you’ll face is real-time vs batch (scheduled) integration. It might sound simple, but choosing the wrong approach can lead to performance issues, unhappy users, or even data inconsistency. In this blog, I’ll Walk through the key differences, when to use each, and lessons we’ve learned from real projects across Dynamics 365 CRM and F&O. The Basics: What’s the Difference? Type Description Real-Time Data syncs immediately after an event (record created/updated, API call). Batch Data syncs periodically (every 5 mins, hourly, nightly, etc.) via schedule. Think of real-time like WhatsApp you send a message, it goes instantly. Batch is like checking your email every hour you get all updates at once. When to Use Real-Time Integration Use It When: Example: When a Sales Order is created in D365 CRM, we trigger a Logic App instantly to create the corresponding Project Contract in F&O. Key Considerations When to Use Batch Integration Use It When: Example: We batch sync Time Entries from CRM to F&O every night using Azure Logic Apps and Azure Blob checkpointing. Key Considerations Our Experience from the Field On one recent project: As a Result, the system was stable, scalable, and cost-effective. To conclude, you don’t have to pick just one. Many of our D365 projects use a hybrid model: Start by analysing your data volume, user expectations, and system limits — then pick what fits best. 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

If you’re new to Databricks, chances are someone told you “Everything starts with a Notebook.” They weren’t wrong. In Databricks, a Notebook is where your entire data engineering workflow begins from reading raw data, transforming it, visualizing trends, and even deploying jobs. It’s your coding lab, dashboard, and documentation space all in one. What Is a Databricks Notebook? A Databricks Notebook is an interactive environment that supports multiple programming languages such as Python, SQL, R, and Scala. Each Notebook is divided into cells you can write code, add text (Markdown), and visualize data directly within it. Unlike local scripts, Notebooks in Databricks run on distributed Spark clusters. That means even your 100 GB dataset is processed within seconds using parallel computation. So, Notebooks are more than just code editors they are collaborative data workspaces for building, testing, and documenting pipelines. How Databricks Notebooks Work Under the hood, every Notebook connects to a cluster a group of virtual machines managed by Databricks. When you run code in a cell, it’s sent to Spark running on the cluster, processed there, and results are sent back to your Notebook. This gives you the scalability of big data without worrying about servers or configurations. Setting Up Your First Cluster Before running a Notebook, you must create a cluster it’s like starting the engine of your car. Here’s how: Step-by-Step: Creating a Cluster in a Standard Databricks Workspace Once the cluster is active, you’ll see a green light next to its name now it’s ready to process your code. Creating Your First Notebook Now, let’s build your first Databricks Notebook: Your Notebook is now live ready to connect to data and start executing. Loading and Exploring Data Let’s say you have a sales dataset in Azure Blob Storage or Data Lake. You can easily read it into Databricks using Spark: df = spark.read.csv(“/mnt/data/sales_data.csv”, header=True, inferSchema=True)display(df.limit(5)) Databricks automatically recognizes your file’s schema and displays a tabular preview.Now, you can transform the data: from pyspark.sql.functions import col, sumsummary = df.groupBy(“Region”).agg(sum(“Revenue”).alias(“Total_Revenue”))display(summary) Or, switch to SQL instantly: %sqlSELECT Region, SUM(Revenue) AS Total_RevenueFROM sales_dataGROUP BY RegionORDER BY Total_Revenue DESC Visualizing DataDatabricks Notebooks include built-in charting tools.After running your SQL query:Click + → Visualization → choose Bar Chart.Assign Region to the X-axis and Total_Revenue to the Y-axis.Congratulations — you’ve just built your first mini-dashboard! Real-World Example: ETL Pipeline in a Notebook In many projects, Databricks Notebooks are used to build ETL pipelines: Each stage is often written in a separate cell, making debugging and testing easier.Once tested, you can schedule the Notebook as a Job running daily, weekly, or on demand. Best Practices To conclude, Databricks Notebooks are not just a beginner’s playground they’re the backbone of real data engineering in the cloud.They combine flexibility, scalability, and collaboration into a single workspace where ideas turn into production pipelines. If you’re starting your data journey, learning Notebooks is the best first step.They help you understand data movement, Spark transformations, and the Databricks workflow everything a data engineer need. 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

The Hidden Problem with Data Lakes Before Delta Lake, data engineers faced a common challenge. Jobs failed midway, data was partially written, and there was no way to roll back. Over time, these issues led to inconsistent reports and untrustworthy dashboards. Delta Lake was created to fix exactly this kind of chaos. What Is Delta Lake Delta Lake is an open-source storage layer developed by Databricks that brings reliability, consistency, and scalability to data lakes. It works on top of existing cloud storage like Azure Data Lake, AWS S3, or Google Cloud Storage. Delta Lake adds important capabilities to traditional data lakes such as: It forms the foundation of the Databricks Lakehouse, which combines the flexibility of data lakes with the reliability of data warehouses. How Delta Lake Works – The Transaction Log Every Delta table has a hidden folder called _delta_log.This folder contains JSON files that track every change made to the table. Instead of overwriting files, Delta Lake appends new parquet files and updates the transaction log. This mechanism allows you to view historical versions of data, perform rollbacks, and ensure data consistency across multiple jobs. ACID Transactions – The Reliability Layer ACID stands for Atomicity, Consistency, Isolation, and Durability. These properties ensure that data is never partially written or corrupted even when multiple pipelines write to the same table simultaneously. If a job fails in the middle of execution, Delta Lake automatically rolls back the incomplete changes.Readers always see a consistent snapshot of the table, which makes your data trustworthy at all times. Time Travel – Querying Past Versions Time Travel allows you to query older versions of your Delta table. It is extremely helpful for debugging or recovering accidentally deleted data. Example queries: SELECT * FROM sales_data VERSION AS OF 15; SELECT * FROM sales_data TIMESTAMP AS OF ‘2025-10-28T08:00:00.000Z’; These commands retrieve data as it existed at that specific point in time. Schema Enforcement and Schema Evolution In a traditional data lake, incoming files with different schemas often cause downstream failures.Delta Lake prevents this by enforcing schema validation during writes. If you intentionally want to add a new column, you can use schema evolution: df.write.option(“mergeSchema”, “true”).format(“delta”).mode(“append”).save(“/mnt/delta/customers”) This ensures that the new schema is safely merged without breaking existing queries. Practical Example – Daily Customer Data UpdatesSuppose you receive a new file of customer data every day.You can easily merge new records with existing data using Delta Lake: MERGE INTO customers AS targetUSING updates AS sourceON target.customer_id = source.customer_idWHEN MATCHED THEN UPDATE SET *WHEN NOT MATCHED THEN INSERT * This command updates existing records and inserts new ones without duplication. Delta Lake in the Medallion ArchitectureDelta Lake fits perfectly into the Medallion Architecture followed in Databricks. Maintenance: Optimize and VacuumDelta Lake includes commands that keep your tables optimized and storage efficient. Layer Purpose Bronze Raw data from various sources Silver Cleaned and validated data Gold Aggregated data ready for reporting OPTIMIZE sales_data;VACUUM sales_data RETAIN 168 HOURS. OPTIMIZE merges small files for faster queries.VACUUM removes older versions of data files to save storage. Unity Catalog IntegrationWhen Unity Catalog is enabled, your Delta tables become part of a centralized governance layer.Access to data is controlled at the Catalog, Schema, and Table levels. Example: SELECT * FROM main.sales.customers; This approach improves security, auditing, and collaboration across multiple Databricks workspaces. Best Practices for Working with Delta Lake a. Use Delta format for both intermediate and final datasets.b. Avoid small file issues by batching writes and running OPTIMIZE.c. Always validate schema compatibility before writing new data.d. Use Time Travel to verify or restore past data.e. Schedule VACUUM jobs to manage storage efficiently.f. Integrate with Unity Catalog for secure data governance. Why Delta Lake Matters Delta Lake bridges the gap between raw data storage and reliable analytics. It combines the best features of data lakes and warehouses, enabling scalable and trustworthy data pipelines. With Delta Lake, you can build production-grade ETL workflows, maintain versioned data, and ensure that every downstream system receives clean and accurate information. Convert an existing Parquet table into Delta format using: CONVERT TO DELTA parquet./mnt/raw/sales_data/; Then try using Time Travel, Schema Evolution, and Optimize commands. You will quickly realize how Delta Lake simplifies complex data engineering challenges and builds reliability into every pipeline you create. To conclude, Delta Lake provides reliability, performance, and governance for modern data platforms.It transforms your cloud data lake into a true Lakehouse that supports both data engineering and analytics efficiently. 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

Most reporting problems do not come from Power BI or visualization tools. They come from how the data is organized before it reaches the reporting layer. A lot of teams try to push raw CRM tables, ERP extracts, finance dumps, and timesheet files directly into Power BI models. This usually leads to slow refreshes, constant model changes, broken relationships, and inconsistent metrics across teams. A clean Medallion Architecture solves these issues by giving your data a predictable, layered structure inside Databricks. It gives reporting teams clarity, improves performance, and reduces rework across projects. Below is a senior-level view of how to design and implement it in a way that supports long-term reporting needs. Why the Medallion Architecture Matters The Medallion model gets discussed often, but in practice the value comes from discipline and consistency. The real benefit is not the three layers. It is the separation of responsibilities: This separation ensures data engineers, analysts, and reporting teams do not step on each other’s work. You avoid the common trap of mixing raw, cleaned, and aggregated data in the same folder or the same table, which eventually turns the lake into a “large folder with files,” not a structured ecosystem. Bronze Layer: The Record of What Actually Arrived The Bronze layer should be the most predictable part of your data platform. It contains raw data as received from CRM, ERP, HR, finance, or external systems. From a senior perspective, the bronze layer has two primary responsibilities: This means storing load timestamps, file names, and source identifiers. The Bronze layer is not the place for business logic. Any adjustment here will compromise traceability. A good bronze table lets you answer questions like:“What exactly did we receive from Business Central on the 7th of this month?”If your Bronze layer cannot answer this, it needs improvement. Silver Layer: Apply Business Logic Once, Use It Everywhere The Silver layer transforms raw data into standardized, trusted datasets. A senior approach focuses on solving root issues here, not patching them later.Typical responsibilities include: This is where you remove all the “noise” that Power BI models should never see. Silver is also where cross-functional logic goes.For example: Once the Silver layer is stable, the Gold layer becomes significantly simpler. Gold Layer: Data Structured for Reporting and Performance (Gold) represents the presentation layer of the Lakehouse. It contains curated datasets designed around reporting and analytics use cases, rather than reflecting how data is stored in source systems. A senior-level Gold layer focuses on: Gold tables should reflect business definitions, not technical ones. If your teams rely on metrics like utilization, revenue recognition, resource cost rates, or customer lifetime value, those calculations should live here. Gold is also where performance tuning matters. Partitioning, Z-ordering, and optimizing Delta tables significantly improves refresh times and Power BI performance. A Real-World Example In projects where CRM, Finance, HR, and Project data come from different systems, reporting becomes difficult when each department pulls data separately. A Medallion architecture simplifies this: The reporting team consumes these gold tables directly in Power BI with minimal transformations. Why This Architecture Works for Reporting Teams To conclude, a clean Medallion Architecture is not about technology – it’s about structure, discipline, and clarity. When implemented well, it removes daily friction between engineering and reporting teams.It also creates a strong foundation for governance, performance, and future scalability. Databricks makes the Medallion approach easier to maintain, especially when paired with Delta Lake and Unity Catalog. Together, these pieces create a data platform that can support both operational reporting and executive analytics at scale. 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

When working with Dynamics 365, one of the key decisions during integration design is whether to implement synchronous or asynchronous communication. Understanding the differences and use cases for each approach is critical to building reliable, efficient, and scalable integrations. Understanding the Difference When to Use Synchronous Integration Synchronous integration is appropriate when: Advantages: Immediate confirmation, straightforward error detection.Considerations: Can slow down the system if the target application experiences latency, less scalable for high-volume scenarios. When to Use Asynchronous Integration Asynchronous integration is better suited for scenarios where: Advantages: Highly scalable, non-blocking operations, suitable for batch processing.Considerations: Errors may not be detected immediately, and tracking processing status requires additional monitoring. Real-World Examples Decision-Making Approach When evaluating which approach to use, consider these questions: To conclude, both synchronous and asynchronous integrations have distinct advantages and trade-offs. Synchronous workflows provide real-time feedback and simpler error handling, while asynchronous workflows offer scalability and efficiency for high-volume or non-urgent processes. Selecting the right approach for your Dynamics 365 integration requires careful consideration of business requirements, data volume, and system performance. By aligning the integration method with these factors, you can ensure reliable, efficient, and maintainable integrations. 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.

Integrating external systems with Dynamics 365 often involves exchanging files like CSVs or XMLs between platforms. Traditionally, these integrations require custom code, complex workflows, or manual intervention, which increases maintenance overhead and reduces reliability. Thankfully, leveraging Azure Blob Storage and Logic Apps can streamline file-based integrations, making them more efficient, scalable, and easier to maintain. Why File-Based Integrations Are Still Common While APIs are the preferred method for system integration, file-based methods remain popular in many scenarios: The challenge comes in orchestrating file movement, transforming data, and ensuring it reaches Dynamics 365 reliably. Enter Azure Blob Storage Azure Blob Storage is a cloud-based object storage solution designed for massive scalability. When used in file-based integrations, it acts as a reliable intermediary: Orchestrating with Logic Apps Azure Logic Apps is a low-code platform for building automated workflows. It’s particularly useful for integrating Dynamics 365 with file sources: Real-Time Example: Automating Sales Order Uploads Traditional Approach: Solution Using Azure Blob and Logic Apps: Outcome: Best Practices Benefits To conclude, file-based integrations no longer need to be complicated or error-prone. By leveraging Azure Blob Storage for reliable file handling and Logic Apps for automated workflows, Dynamics 365 integrations become simpler, more maintainable, and scalable. The real-time sales order example shows that businesses can save time, reduce errors, and ensure data flows seamlessly between systems allowing teams to focus on their core operations rather than manual file processing. 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.

If you’ve worked on Dynamics 365 CRM projects, you know integration isn’t optional—it’s essential. Whether you’re connecting CRM with a legacy ERP, a cloud-based marketing tool, or a SharePoint document library, the way you architect your integrations can make or break performance and maintainability.  Azure Logic Apps makes this easier with its low code interface but using the right pattern matters. In this post, I’ll Walk through seven integration patterns I’ve seen in real projects, explain where they work best, and share some lessons from the field.  Whether you’re building real-time syncs, scheduled data pulls, or hybrid workflows using Azure functions, these patterns will help you design cleaner, smarter solutions.  A Common Real-World Scenario Let’s say you’re asked to sync Project Tasks from Dynamics 365 to an external project management system. The sync needs to be quick, reliable, and avoid sending duplicate data.  You might wonder:  Without a clear integration pattern, you might end up with brittle flows that break silently or overload your system.  Key Integration Patterns (With Real Use Cases)  1. Request-Response Pattern  What it is: A Logic App that waits for a request (usually via HTTP), processes it, and sends back a response.  Use Case: You’re building a web or mobile app that pulls data from CRM in real time—like showing a customer’s recent orders.  How it works:  Why use it:  Key Considerations:  2. Fire-and-Forget Pattern What it is: CRM pushes data to a Logic App when something happens. The Logic App does the work—but no one waits for confirmation.  Use Case: When a case is closed in CRM, you archive the data to SQL or notify another system via email.  How it works:  Why use it:  Keep users moving—no delays.  Great for logging, alerts, or downstream updates  Key Considerations:  Silent failures—make sure you’re logging errors or using retries  3. Scheduled Sync (Polling) What it is: A Logic App that runs on a fixed schedule and pulls new/updated records using filters.  Use Case: Every 30 minutes, sync new Opportunities from CRM to SAP.  How it works:  Why use it:  Key Considerations:  4. Event-Driven Pattern (Webhooks)  What it is: CRM triggers a webhook (HTTP call) when something happens. A Logic App or Azure Function listens and acts.  Use Case: When a Project Task is updated, push that data to another system like MS Project or Jira.  How it works:  Why use it:  Key Considerations:  5. Queue-Based Pattern  What it is: Messages are pushed to a queue (like Azure Service Bus), and Logic Apps process them asynchronously.  Use Case: CRM pushes lead data to a queue, and Logic Apps handle them one by one to update different downstream systems (email marketing, analytics, etc.)  How it works:  Why use it:  Key Considerations:  6. Blob-Driven Pattern (File-Based Integration)  What it is: Logic App watches a Blob container or SFTP location for new files (CSV, Excel), parses them, and updates CRM.  Use Case: An external system sends daily contact updates via CSV to a storage account. Logic App reads and applies updates to CRM.  How it works:  Why use it:  Key Considerations:  7. Hybrid Pattern (Logic Apps + Azure Functions)  What it is: Logic App does the orchestration, while Azure Function handles complex logic that’s hard to do with built-in connectors.  Use Case: You need to calculate dynamic pricing or apply business rules before pushing data to ERP.  How it works:  Why use it:  Key Considerations:  Implementation Tips & Best Practices  Area  Recommendation  Security  Use managed identity, OAuth, and Key Vault for secrets  Error Handling  Use “Scope” + “Run After” for retries and graceful failure responses  Idempotency  Track processed IDs or timestamps to avoid duplicate processing  Logging  Push important logs to Application Insights or a centralized SQL log  Scaling  Prefer event/queue-based patterns for large volumes  Monitoring  Use Logic App’s run history + Azure Monitor + alerts for proactive detection  Tools & Technologies Used Common Architectures You’ll often see combinations of these patterns in real-world systems. For example:  To conclude, integration isn’t just about wiring up connectors, it’s about designing flows that are reliable, scalable, and easy to maintain.  These seven patterns are ones I’ve personally used (and reused!) across projects. Pick the right one for your scenario, and you’ll save yourself and your team countless hours in debugging and rework.  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.

You’re integrating Dynamics 365 CRM with other systems—but you’re confused:Should I use Azure Data Factory or Logic Apps?Both support connectors, data transformation, and scheduling—but serve different purposes. When you’re working on integrating Dynamics 365 with other systems, two Azure tools often come up: Azure Logic Apps and Azure Data Factory (ADF). I’ve been asked many times — “Which one should I use?” — and honestly, there’s no one-size-fits-all answer. Based on real-world experience integrating D365 CRM and Finance, here’s how I approach choosing between Logic Apps and ADF. When to Use Logic Apps Azure Logic Apps is ideal when your integration involves: 1. Event-Driven / Real-Time Integration 2. REST APIs and Lightweight Automation 3. Business Process Workflows 4. Quick and Visual Flow Creation Azure Data Factory is better for: 1. Large Volume, Batch Data Movement 2. ETL / ELT Scenarios 3. Integration with Data Lakes and Warehouses 4. Advanced Data Flow Transformation Feature Comparison Table Feature Logic Apps Data Factory Trigger on Record Creation/Update Yes No (Batch Only) Handles APIs (HTTP, REST, OData) Excellent Limited Real-time Integration Yes No Large Data Volumes (Batch) Limited Excellent Data Lake / Warehouse Integration Basic (via connectors) Deep support Visual Workflow Visual Designer Visual (for Data Flows) Custom Code / Transformation Limited (use Azure Function) Strong via Data Flows Cost for High Volume Higher (Per Run) Cost-efficient for batch Real-World Scenarios 2. Use ADF When: To conclude, choose Logic Apps for real-time, low-volume, API-based workflows.Use Data Factory for batch ETL pipelines, high-volume exports, and reporting pipelines. Integrations in Dynamics 365 CRM aren’t one-size-fits-all—pick the right tool based on the data size, speed, and transformation needs. We hope you found this blog useful, and if you would like to discuss anything, you can reach out to us at transform@cloudfonts.com