The fastest way to lose control of enterprise data? Managing governance separately across workspaces. Unity Catalog solves this with one centralized layer for security, lineage, and discovery. Data governance is crucial for any organization looking to manage and secure its data assets effectively. Databricks’ Unity Catalog is a centralized solution that provides a unified interface for managing access control, auditing, data lineage, and discovery. This blog will guide you through the process of setting up Unity Catalog in your Databricks workspace. What is Unity Catalog? Unity Catalog is Databricks’ answer to centralized data governance. It enables organizations to enforce standards-compliant security policies, apply fine-grained access controls, and visualize data lineage across multiple workspaces. It ensures compliance and promotes efficient data management. Key Features: 1] Standards-Compliant Security: ANSI SQL-based access policies that apply across all workspaces in a region. 2] Fine-Grained Access Control: Support for row- and column-level permissions. 3] Audit Logging: Tracks who accessed what data and when. 4] Data Lineage: Provides visualization of data flow and dependencies. Unity Catalog Object Hierarchy Before diving into the setup, it’s important to understand the hierarchical structure of Unity Catalog: 1] Catalogs: The top-level container (e.g., Production, Development) that represents an organizational unit or environment. 2] Schemas: Logical groupings of tables, views, and AI models within a catalog. 3] Tables and Views: These include managed tables fully governed by Unity Catalog and external tables referencing existing cloud storage. Here is the procedure to setup a Unity Catalog Metastore in association with Azure Storage, as I have done for one of our products (SmartPitch Sales & Marketing Agent) – 1] First create a storage account  with primary service being – “Azure Blob Storage or Azure Data Lake Storage Gen 2”; Performance and Redundancy can be chosen based on the requirement for which the DataBricks service is being used.Here for my Mosaic AI Agent, I have used Locally Redundant Storage & Data Lake Gen 2 2] Once the storage account is created, ensure that you have enabled “Hierarchical Namespace” When creating a Unity Catalog metastore with Azure Blob Storage, Hierarchical Namespace (HNS) is required because Unity Catalog needs: a] Folder-like structure to organize catalogs, schemas, and tables. b] Atomic operations (rename, move, delete) on directories and files. c] POSIX-style access controls for fine-grained permissions. d] Faster metadata handling for lineage and governance. HNS turns Azure Blob into ADLS Gen2, which supports these features. 3] Upload any Raw/Unclean files to your metastore folder in the blob storage, which would be required for your use in DataBricks. 4] Create a Unity Catalog Connector in Azure Portal and assign it “Storage Blob Data Contributor” Role . 5] Assign CORS (Cross-Origin Resource Sharing) settings for that storage account. Why this is necessary: In short: Without configuring CORS, Databricks cannot communicate with your storage container to read/write managed tables, schema metadata, or logs. 6] Generate SAS Token   7] Navigate to your Workspace and select “Manage Account” – this should be done from the account admin.  8] Select Catalog tab on the left and then click “Create Metastore”    9] Assign a Name, Region (Same as Workspace), The path to the storage account, and the connector id. 10] Once the Metastore is created, assign it to a workspace .  11] Once this is done, the catalogs and the schemas, and tables in within it can be created. How does Unity Catalog differ from Hive Metastore ? Feature Hive Metastore Unity Catalog Scope Workspace or cluster-specific Centralized, spans multiple workspaces and regions Architecture Single metastore tied to Spark/Hive Cloud-native service integrated with Databricks Object Hierarchy Databases → Tables → Partitions Catalogs → Schemas → Tables/Views/Models Data Assets Supported Tables, views Tables, views, files, ML models, dashboards Security Basic GRANT/DENY at database/table level Fine-grained, ANSI SQL–based (catalog, schema, table, column, row) Lineage Not available Built-in lineage and impact analysis Auditing Limited or external Integrated audit logs across workspaces Storage Management Points to storage locations; no governance Manages external and managed tables with governance Cloud Integration Primarily on cluster storage or external path Secure integration with ADLS Gen2, S3, GCS Permissions Model Spark SQL statements Attribute- and role-based access, unified policies Use Cases Basic metadata store for Spark/Hive workloads Enterprise-wide data governance, sharing, and compliance To conclude, Unity Catalog is the next-generation governance and metadata solution for Databricks, designed to give organizations a single, secure, and scalable way to manage data and AI assets. Unlike the older Hive Metastore, it centralizes control across multiple workspaces, supports fine-grained access policies, delivers built-in lineage and auditing, and integrates seamlessly with cloud storage like Azure Data Lake, S3, or GCS. When setting it up, key steps include: 1] Creating a metastore and linking it to your workspaces. 2] Enabling hierarchical namespace on Azure storage for folder-level security and operations. 3] Configuring CORS to allow Databricks domains to interact with storage. 4] Defining catalogs, schemas, and tables for structured governance. By implementing Unity Catalog, you ensure stronger security, better compliance, and faster data discovery, making your Databricks environment enterprise-ready for analytics and AI. Business Outcomes of Unity Catalog By implementing Unity Catalog, organizations can achieve: Why now? As data volumes and regulatory requirements grow, organizations can no longer rely on fragmented or legacy governance tools. Unity Catalog offers a future-proof foundation for unified data management and AI governance—essential for any modern data-driven enterprise. At CloudFronts, we help enterprises implement and optimize Unity Catalog within Databricks to ensure secure, compliant, and scalable data governance for enterprise data governance.Book a consultation with our experts to explore how Unity Catalog can simplify compliance and boost productivity for your teams.Contact us today at Transform@cloudfronts.com to get started. To learn more about functionalities of DataBricks and other Azure AI services, please refer to my other blogs from the links given below: – 1] The Hidden Cost of Bad Data:How Strong Data Management Unlocks Scalable, Accurate AI – CloudFronts 2] Automating Document Vectorization from SharePoint Using Azure Logic Apps and Azure AI Search – CloudFronts 3] Using Open AI and Logic Apps to develop a Copilot agent for … Continue reading Setting Up Unity Catalog in Databricks for Centralized Data Governance →

Most organizations don’t fail because of bad AI models; they fail because their data isn’t clear. When your data is consistent, connected, and governed, your systems begin to “do the talking” for your teams. In our earlier article on whether your tech stack is holding you back from achieving AI success, we discussed the importance of building the right foundation.  This time, we go deeper because even the best stack cannot deliver without data clarity. That’s when deals close faster, billing runs itself, projects stay on track, and leaders decide with confidence. This blueprint shows how to move from fragments to structure to precision. The Reality Check For many businesses today, operations still depend on spreadsheets, scattered files, and long email threads. Reports exist in multiple versions, important numbers are tracked manually, and teams spend more time reconciling than acting. This is not a tool problem, it is a clarity problem. If your inventory lives in spreadsheets, if the “latest report” means three versions shared over email, no algorithm will save you. What scales is clarity: one language for data, one source of truth, and one way to connect systems and decisions. 💡 If you cannot trust your data today, you will second guess your decisions tomorrow. What Data Clarity Really Means (in Plain Business Terms) Clarity is not a buzzword. It makes data usable and enables scalability, giving every AI-ready business its foundation. Here’s what that looks like in practice: 💡Clarity is not another software purchase. It is a shared business agreement across systems and teams. From Chaos to Clarity: The Three Levels of Readiness Data clarity evolves step by step. Think of it as moving from fragments to gemstones, to jewels. Level 1: Chaos (fragments everywhere) Data is spread across applications, spreadsheets, inboxes, and vendor portals. Duplicates exist, numbers conflict, and no one fully trusts the information. Level 2: Structure (the gemstone taking shape) Core entities like Customers, Products, Projects, and Invoices are standardized and mapped across systems. Data is stored in structured tables or databases. Reporting becomes stable, handovers reduce, and everyone begins pulling from a shared source. Level 3: Composable Insights (precision) Data is modular, reusable, and intelligent. It feeds forecasting, guided actions, and proactive alerts. Business leaders move from asking “what happened?” to acting on “what should we do next?” [Image Alt: Data Fragments to Jewel Levels] How businesses progress: 💡Fragments refined into gemstones, and gemstones polished into jewels. Data clarity and maturity are the jewels of your business. In fragments, they hold little value. The Minimum Readiness Stack (Microsoft First) You don’t need a long list of tools. You need a stack that works together and grows with you: 💡A small, well-integrated Tech stack outperforms a large, disconnected toolkit, every time. What Data Clarity Unlocks Clarity does not just organize your data. It transforms how systems work with each other and how your business operates. CloudFronts provided solutions, the real-world impact: Benefits enabled across departments in your business: 💡When systems talk, your teams stop chasing data. They gain clarity. And clarity means speed, control, and growth. Governance That Accelerates Governance is not red tape, it is acceleration. Here’s how effective governance translates into business impact.  Proof in action includes: 💡Trust in data is engineered, not assumed. Outcomes That Matter to the Business Clarity shows up in business outcomes, not just dashboards. Tinius Olsen-Migrating from TIBCO to Azure Logic Apps for seamless integration between D365 Field Service and Finance & Operations  – CloudFronts BÜCHI’s customer-centric vision accelerates innovation using Azure Integration Services | Microsoft Customer Stories 💡Once clarity is established, every new process, report, or integration lands faster, cleaner, and with more impact. To conclude, getting AI-ready is not about chasing new models. It is about making your data consistent, connected, and governed so that systems quietly remove manual work while surfacing what matters. All of this leads to one truth: clarity is the foundation for AI readiness. This is where Dynamics 365, Power Platform, and Azure integrations shine, providing a Microsoft-first path from fragments to precision. If your business is ready to move from fragments to precision, let’s talk at transform@cloudfronts.com CloudFront will help you turn data chaos into clarity, and clarity into outcomes.

. Key Takeaways 1. Bad data kills ML performance — duplicate, inconsistent, missing, or outdated data can break production models even if training accuracy is high.2. Databricks Medallion Architecture (Raw → Silver → Gold) is essential for turning raw chaos into structured, trustworthy datasets ready for ML & BI.3. Raw layer: capture all data as-is; Silver layer: clean, normalize, and standardize; Gold layer: curate, enrich, and prepare for modeling or reporting.4. Structured pipelines reduce compute cost, improve model reliability, and enable proactive monitoring & feature freshness.5. Data quality is as important as algorithms — invest in transformation and governance for scalable, accurate AI. While developing a Machine Learning Agent for Smart Pitch (Sales/Presales/Marketing Assistant chatbot) within the Databricks ecosystem, I’ve seen firsthand how unclean, inconsistent, and incomplete data can cripple even the most promising machine learning initiatives. While much attention is given to models and algorithms, what often goes unnoticed is the silent productivity killer lurking in your pipelines: bad data. In the chase to build intelligent applications, developers often overlook the foundational layer—data quality. Here’s the truth: It is difficult to scale machine learning on top of chaos. That’s where the Raw → Silver → Gold data transformation framework in Databricks becomes not just useful, but also essential. The Hidden Cost of Bad Data Imagine you’ve built a high-performance ML model. It’s accurate during training, but underperforms in production. Why?Here’s what I frequently detect when I scan input pipelines:– Duplicate records– Inconsistent data types– Missing values– Outdated information– Schema drift (Schema drift introduces malformed, inconsistent, or incomplete data that breaks validation rules and compromises downstream processes.)– Noise in DataThese issues inflate compute costs, introduce bias, and produce unstable predictions, resulting in wasted hours debugging pipelines, increased operational risks, and eroded trust in your AI outputs. Ref.: Data Poisoning: A Silent but Deadly Threat to AI and ML Systems | by Anya Kondamani | nFactor Technologies | Medium As you can see in this example – Despite successfully fetching data, it was unable to render it, due to hitting maximum request limit, as bad data was involved, thus AI Agents face issues if directly brute forced with Raw/Unclean data. But this isn’t just a data science problem. It’s a data engineering problem. And the solution lies in structured, governed data management—beginning with a robust medallion architecture. Ref.: Data Intelligence End-to-End with Azure Databricks and Microsoft Fabric | Microsoft Community Hub Raw → Silver → Gold: The Databricks Way Databricks ML Agents thrive when your data is managed through the medallion architecture, transforming raw chaos into clean, trustworthy features. For those new to Medallion architecture in Databricks, it is a structured data processing framework that progressively improves data quality through bronze (raw), silver (validated), and gold (enriched) layers, enabling scalable and reliable analytics. Raw Layer: Ingest Everything The raw layer is where we land all data, regardless of quality. It’s your unfiltered feed—logs, events, customer input, third-party APIs, CSV dumps, IoT signals, etc. e.g. CloudFronts Case studies as directly fetched from WordPress API This script automates the extraction, transformation, and optional upload of WordPress-based case study content into Azure Blob Storage as well as locally to dbfs of Databricks, making it ready for further processing (e.g., vector databases, AI ingestion, or analytics). What We’ve Done On running the above Raw to Silver Cleaning code in Databricks notebook we get a much formatted, relevant and cleaned fields specific to our requirement In Databricks Unity Catalog → Schema → Tables, it looks something like this: – Silver Layer: Structure and Clean Once ingested, we promote data to the silver layer, where transformation begins. Think of this layer as the data refinery. What happens here: Now we start to analyze trends, infer schemas, and prepare for more active feature generation. Once I have removed noise; I begin to see patterns. This script is part of a data pipeline that transforms unstructured or semi-clean data from a Raw Delta Table (knowledge_base.raw.case_studies) (Actually Silver, as we have already done much of the cleaning part in previous step) into a Gold Delta Table (knowledge_base.gold.case_studies) using Apache Spark. The transformation focuses on HTML cleaning, type parsing, and schema standardization, enabling downstream ML or BI workflow What have we done here ? Start Spark Session Initializes a Spark job using SparkSession, enabling distributed data processing within the Databricks environment. Define UDFs (User-Defined Functions) Read Data from Silver Table Loads structured data from the Delta Table: knowledge_base.raw.case_studies, which acts as the Silver Layer in the medallion architecture. Clean & Normalize Key Columns Write to Gold Table Saves the final transformed DataFrame to the Gold Layer as a Delta Table: knowledge_base.gold.case_studies, using overwrite mode and allowing schema updates. As you can see we have maintained separate schemas for Raw, Silver Gold etc; and at gold we finally managed to get a fully cleaned noiseless data suitable for our requirement. Gold Layer: Ready for ML & BI At the gold layer, data becomes a polished product, curated for specific use cases—ML models, dashboards, reports, or APIs. This is the layer where scale and accuracy become feasible, because it finally has clean, enriched, semantically meaningful data to learn from. Ref.: What is a Medallion Architecture? We can further make it more efficient to fetch with vectorization  Once we reach stage and test again in the Agent Playground, we no longer face the error we had seen previously as now it is easier for the agent to retrieve gold standard vectorized data. Why This Matters for AI at Scale The difference between “good enough” and “state of the art” often hinges on data readiness. Here’s how strong data management impacts real-world outcomes: Without Medallion Architecture With Raw → Silver → Gold Data drift goes undetected Proactive schema monitoring Models degrade silently Continuous feature freshness Expensive debugging cycles Clean lineage via Delta Lake Inconsistent outputs Predictable, testable results (Delta Lake is an open-source storage layer that brings ACID transactions, scalable metadata handling, and unified batch processing to data lakes, enabling reliable analytics on massive datasets.) … Continue reading The Hidden Cost of Bad Data:How Strong Data Management Unlocks Scalable, Accurate AI →

The AI Race Has Begun but Most Businesses Are Crawling Artificial Intelligence (AI) is no longer experimental it’s operational. Across industries, companies are trying to harness it to improve decision-making, automate intelligently, and gain competitive edge. But here’s the problem: only 48% of AI projects ever make it to production (Gartner, 2024). It’s not because AI doesn’t work.It’s because most tech stacks aren’t built to support it. The Real Bottleneck Isn’t AI. It’s Your Foundation You may have data. You may even have AI tools. But if your infrastructure isn’t AI-ready, you’ll stay stuck in POCs that never scale. Common signs you’re blocked: AI success starts beneath the surface, in your data pipelines, infrastructure, and architecture. Most machine learning systems fail not because of poor models, but because of broken data and infrastructure pipelines. What Does an AI-Ready Tech Stack Look Like? Being AI-Ready means preparing your infrastructure, data, and processes to fully support AI capabilities. This is not a checklist or quick fix. It is a structured alignment of technology and business goals. A truly AI-ready stack can: Area Traditional Stack AI-Ready Stack Why It Matters Infrastructure On-premises servers, outdated VMs Azure Kubernetes Service (AKS), Azure Functions, Azure App Services; then: AWS EKS, Lambda; GCP GKE, Cloud Run AI workloads need scalable, flexible compute with container orchestration and event-driven execution Data Handling Siloed databases, batch ETL jobs Azure Data Factory, Power Platform connectors, Azure Event Grid, Synapse Link; then: AWS Glue, Kinesis; GCP Dataflow, Pub/Sub Enables real-time, consistent, and automated data flow for training and inference Storage & Retrieval Relational DBs, Excel, file shares Azure Data Lake Gen2, Azure Cosmos DB, Microsoft Fabric OneLake, Azure AI Search (with vector search); then: AWS S3, DynamoDB, OpenSearch; GCP BigQuery, Firestore Modern AI needs scalable object storage and vector DBs for unstructured and semantic data AI Enablement Isolated scripts, manual ML Azure OpenAI Service, Azure Machine Learning, Copilot Studio, Power Platform AI Builder; then: AWS SageMaker, Bedrock; GCP Vertex AI, AutoML; OpenAI, Hugging Face Simplifies AI adoption with ready-to-use models, tools, and MLOps pipelines Security & Governance Basic firewall rules, no audit logs Microsoft Entra (Azure AD), Microsoft Purview, Microsoft Defender for Cloud, Compliance Manager, Dataverse RBAC; then: AWS IAM, Macie; GCP Cloud IAM, DLP API Ensures responsible AI use, regulatory compliance, and data protection Monitoring & Ops Manual monitoring, limited observability Azure Monitor, Application Insights, Power Platform Admin Center, Purview Audit Logs; then: AWS CloudWatch, X-Ray; GCP Ops Suite; Datadog, Prometheus AI success depends on observability across infrastructure, pipelines, and models In Summary: AI-readiness is not a buzzword. Not a checklist. It’s an architectural reality. Why This Matters Now AI is moving fast and so are your competitors. But success doesn’t depend on building your own LLM or becoming a data science lab. It depends on whether your systems are ready to support intelligence at scale. If your tech stack can’t deliver real-time data, run scalable AI, and ensure trust your AI ambitions will stay just that: ambitions. How We Help We work with organizations across industries to: Whether you’re just starting or scaling AI across teams, we help build the architecture that enables action. Because AI success isn’t about plugging in a tool. It’s about building a foundation where intelligence thrives. 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.

When we say building a bridge? Does it mean something isn’t connected together? And what is it?It’s AI itself and your systems that are not connected. What this means if although your AI can access your systems to derive information, it’s still unreliable, slow. What is needed for AI to be successful? In order for AI to be successful, below is what to avoid: In order to eliminate the above, we must have a layer of ‘catalog’ which will house all business data together so that a common vocabulary is established between systems. AI then pools from this ‘Data catalog’ to perform agentic actions. The diagram below best explains, on a high level, how this looks : And all this is defined by how well the integrations between these systems are established. How CloudFronts Can Help? CloudFronts has deep integration expertise where we connected cloud-based applications with each other with the below in mind – Often times, we find ready-made plug and play cloud-based integration solutions which come with their own hefty licensing that keeps going up every few years. Using such integration tools not only affects cash flow but also adds a layer of opaqueness, as we don’t control the flow of integration, and we cannot granularize it beyond what’s offered. Custom integration gives you better control and analytics, which readymade solutions can’t.Here’s a CloudFronts Case Study published by Microsoft, wherein we connected systems for our customer with multiple systems driving data and insights. To conclude, AI Agents are meant to be for your organization aren’t optimized to work right away. This disconnect needs to be engineered just like any other implementation project today. As this gap is real and must be fulfilled by something called Unity Catalog and integrations, CloudFronts can help bridge this gap and make AI work for your organization to continue to optimize cash flow against rising costs. 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.

In today’s world, we use many different applications for our daily work. One single application can’t handle everything because some apps are designed for specific tasks. That’s why organizations use multiple applications, which often leads to data being stored separately or in isolation. In this blog, we’ll take you on a journey from siloed systems to connected systems through a customer success story. About BÜCHI Büchi Labortechnik AG is a Swiss company renowned for providing laboratory and industrial solutions for R&D, quality control, and production. Founded in 1939, Büchi specializes in technologies such as: Their equipment is widely used in pharmaceuticals, chemicals, food & beverage, and academia for sample preparation, formulation, and analysis. Büchi is known for its precision, innovation, and strong customer support worldwide.  Systems Used by BÜCHI To streamline operations and ensure seamless collaboration, BÜCHI leverages a variety of enterprise systems: Infor and SAP Business One are utilized for managing critical business functions such as finance, supply chain, manufacturing, and inventory. Reporting Challenges Due to Siloed Systems Organizations often rely on multiple disconnected systems across departments — such as ERP, CRM, marketing platforms, spreadsheets, and legacy tools. These siloed systems result in: The Need for a Single Source of Truth To solve these challenges, it’s critical to establish a Single Source of Truth (SSOT) — a central, trusted data platform where all key business data is: How We Helped Büchi Connect Their Systems To build a seamless and scalable integration framework, we leveraged the following Azure services: >Azure Logic Apps – Enabled no-code/low-code automation for integrating applications quickly and efficiently. >Azure Functions – Provided serverless computing for lightweight data transformations and custom logic execution. >Azure Service Bus – Ensured reliable, asynchronous communication between systems with FIFO message processing and decoupling of sender/receiver availability. >Azure API Management (APIM) – Secured and simplified access to backend services by exposing only required APIs, enforcing policies like authentication and rate limiting, and unifying multiple APIs under a single endpoint. BÜCHI’s case study was published on the Microsoft website, highlighting how CloudFronts helped connect their systems and prepare their data for insights and AI-driven solutions. Why a Single Source of Truth (SSOT) Is Important A Single Source of Truth means having one trusted location where your business stores consistent, accurate, and up-to-date data. Key Reasons It Matters: How we did this We used Azure Function Apps, Service Bus, and Logic Apps to seamlessly connect the systems. Databricks was implemented to build a Unity Catalog, establishing a Single Source of Truth (SSOT). On top of this unified data layer, we enabled advanced analytics and reporting using Power BI. In May, we hosted an event with BÜCHI at the Microsoft Office in Zurich. During the session, one of the attending customers remarked, “We are five years behind BÜCHI.” Another added, “If we don’t start now, we’ll be out of the race in the future.” This clearly reflects the urgent need for businesses to evolve. Today, Connected Systems, a Single Source of Truth (SSOT), Advanced Analytics, and AI are not optional — they are essential for sustainable growth and improved human efficiency. The pace of transformation has accelerated: tasks that once took months can now be achieved in days — and soon, perhaps, with just a prompt. To conclude, if you’re operating with multiple disconnected systems and relying heavily on manual processes, it’s time to rethink your approach. System integration and automation free your teams from repetitive work and empower them to focus on high impact, strategic activities. 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.

Since the turn of 2025, AI has been thrown around a lot in conversations – both individual and also at an organizational level. Major technology providers have started their own suite of tools to build AI agents.  While these tools are good enough for simpler AI use cases like fetching data from systems and presenting to us, but complex use cases like predicting patterns, collating data from multiple systems and driving insights from connected systems – that’s where AI implementations need to be looked at like projects which needs architecting and implementing with organization’s vision of AI.  Let’s look at how we can make sure that AI implementations give us over 95% accuracy and not just answers every time which we assume might be correct.  Is AI enough by itself?  Common perception that AI Agents are deployed on top of applications which can be used to interact with the underlying systems to do what users are supposed to get done from AI.  This perception stems from our use of AI tools like ChatGPT/Claude/Gemini as they interact with the Internet to get your queries answered. Since this is a tool available independently, there’s not technical setup and it is ready to go.  Speaking of being Copilot being enough on itself, it depends on where the data is sourced from – and what the intent of the Agent is. If your Custom Copilot / AI Agent is meant to only look at some SharePoint files, some websites and within 1 system in your M365 gated access, you should be able to patch to knowledge sources and be good enough to let AI Agent give you the information in the format you need.  Challenge occurs where you expecting the AI Agents to make sense of the data which is stored differently in different systems with different naming conventions – that’s when AI agents will fall through because it cannot understand when you are pointing to an “Account” in CRM, but the same is stored as a “Customer” in Business Central.  And this is where something like a Unity Catalog comes into picture. The term itself describes that the data comes together in a catalog for common access and AI agents to source from.  Let’s look at how we can imagine this unity catalog to be in the next section.  Unity Catalog  Unity Catalog can be thought of as an implementation strategy and collection of connected systems over which AI Agents can be based upon.  Here’s how I summarize this process –   Above diagram is a summary for how AI implementations will scale within organizations and have different variations of the same.  To encapsulate, while independent AI agents can be implemented for personal use within the organization, given the appropriate privileges, for AI to make sense of and enable trusted decision making, AI implementations need to have data readiness in place with clarity.  Hopefully, this topic summarizes the direction in which organizations can think of AI implementation, more than just building agents.  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.

In today’s fast-paced business environment, reliable financial reporting is not just a compliance requirement it’s a strategic necessity. Organizations of all sizes, across industries, must make informed decisions quickly to stay competitive, manage risks, and ensure long-term sustainability. At the heart of this financial clarity lie two fundamental reports: the Income Statement and the Balance Sheet. The Income Statement provides a snapshot of an organization’s financial performance over a specific period detailing revenues, expenses, and profits. It answers the critical question: “Are we making money?” We’ll cover the customer journey from implementation to insight-driven strategy and include key steps and best practices. Steps to Achieve goal: Step 1: Understanding the Need – for any Financial Complexity Before deploying any tool, Team should identified key challenges in their financial operations: Step 2.: Configuring the Income Statement Once the foundational setup was complete, configuring the Income Statement enabled the organization to: Configuration Steps: 3. Enable Dimensional Reporting: Use dimensions to drill down into cost centers. 4. Schedule Reports: Automate delivery to leadership teams for weekly snapshots. Step 3: Real-Time Financial Monitoring One of the most significant value propositions for any system is providing real-time visibility. Key Features in Action: Step 4: Strategic Decision-Making with Insights Optimize Routes: Identify profitable vs. underperforming flight paths Financial Reporting Best Practices for Modern Enterprises To conclude, accurate and timely financial reporting is essential for informed decision-making and long-term business success. With tools like Microsoft Dynamics 365 Business Central, enterprises can turn financial data into strategic insights that drive growth and efficiency. 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.

Consider the scenario of a pharmaceutical manufacturer facing an unexpected shortage of essential raw materials. This situation inevitably leads to production delays, potentially causing missed deadlines and expensive product recalls. In today’s dynamic pharmaceutical sector, where adherence to regulations and a responsive supply chain are crucial, outdated information poses a significant risk. What if these disruptions could be predicted and mitigated before they materialize? What if you had immediate, comprehensive visibility into your entire operational landscape? This is the advantage offered by real-time data, and solutions like Microsoft Business Central are spearheading the evolution of pharmaceutical decision-making. The Shortcomings of Traditional Pharmaceutical Data: Historically, the pharmaceutical industry has operated with data that is often delayed. Reports generated several days or weeks after events occur provide a historical perspective, lacking the current operational awareness needed for effective management. This results in: Real-Time Data: A Game-Changer for Pharma: Our Business Central Pharma module provides a unified platform that delivers real-time visibility across your entire pharmaceutical operation. This empowers you to: Practical Implementation and Tangible Benefits: Implementing Business Central can seem daunting, but the benefits are undeniable. By having the end-to-end process in one system can be very beneficiary. The Future of Data-Driven Pharma: The future of pharma lies in leveraging the power of data. Imagine being able to anticipate potential supply chain disruptions or quality issues before they occur. This is the promise of data-driven Pharma Module. To encapsulate, in the pharmaceutical industry, where precision and speed are critical, real-time data is no longer a luxury—it’s a necessity. Our Business Central pharma module helps companies to embrace the data revolution, enabling faster, more informed decisions that drive efficiency, compliance, and growth. Ready to unlock the power of real-time data for your pharmaceutical operations? Contact us today at transform@cloudfonts.com to learn how our Business Central pharma module can transform your business.

In today’s dynamic business environment, organizations rely on multiple applications, systems, and cloud services to drive operations, making scalable enterprise integrations essential. As businesses grow, their data flow and process complexity increase, demanding integrations that can handle expanding workloads without performance bottlenecks. Scalable integrations ensure seamless data exchange, real-time process automation, and interoperability between diverse platforms like CRM, ERP, and third-party services. They also provide the flexibility to adapt to evolving business needs, supporting digital transformation and innovation. Without scalable integration frameworks, enterprises risk inefficiencies, data silos, and high maintenance costs, limiting their ability to scale operations effectively.  Are you finding it challenging to scale your business operations efficiently?  In this blog, we’ll look into key Azure Integration Services that can help overcome common integration hurdles.  Before we get into AIS, let’s start with some business numbers—after all, money is what matters most to any business.  Several organizations have reported significant cost savings and operational efficiencies after implementing Azure Integration Services (AIS). Here are some notable examples:  Measurable Business Benefits with AIS  A financial study evaluating the impact of deploying AIS found that organizations experienced benefits totalling $868,700 over three years. These included:  Here are some articles to support this data:   Modernizing Legacy Integration: BizTalk to AIS  A financial institution struggling with outdated integration adapters transitioned to Azure Integration Services. By leveraging Service Bus for reliable message delivery and API Management for secure external API access, they reduced operational costs by 25% and improved system scalability.  These examples demonstrate the substantial cost reductions and efficiency improvements that businesses can achieve by leveraging Azure Integration Services.  To put this into perspective, we’ll explore real-world industry challenges and how Azure’s integration solutions can effectively resolve them.  Example 1: Secure & Scalable API Management for a Manufacturing Company  Scenario: A global auto parts manufacturer supplies components to multiple automobile brands. They expose APIs for:  Challenges: However, they are facing serious challenges  These are some simple top-level issues there can be many more complexities.  Solution: Azure API Management (APIM)  The manufacturer deploys Azure API Management (APIM) to secure, manage, and monitor their APIs.   Step 1: Secure APIs – APIM enforces OAuth-based authentication so only authorized suppliers can access APIs. Rate limiting prevents overuse.  Step 2: API Versioning – Different suppliers use v1 and v2 of APIs. APIM ensures smooth version transitions without breaking old integrations.  Step 3: Analytics & Monitoring – The company gets real-time insights on API usage, detecting slow queries and bottlenecks.  Result:  Example 2: Reliable Order Processing with Azure Service Bus for an E-commerce Company  Scenario: A fast-growing e-commerce company processes over 50,000 orders daily across multiple sales channels (website, mobile app, and third-party marketplaces). Orders are routed to:  Challenges:  Solution: Azure Service Bus (Message Queueing)  Instead of direct connections, the company decouples services using Azure Service Bus.  Step 1: Queue-Based Processing – Orders are sent to an Azure Service Bus queue, ensuring no data loss even if systems go down.  Step 2: Asynchronous Processing – Inventory, payment, and fulfilment consume messages independently, avoiding system overload.  Step 3: Dead Letter Queue (DLQ) Handling – Failed orders are sent to a DLQ for retry instead of getting lost.  Result:  Example 3: Automating Invoice Processing with Logic Apps for a Logistics Company  Scenario: A global shipping company receives thousands of invoices from suppliers every month. These invoices must be:  Challenges:  Solution: Azure Logic Apps for End-to-End Automation  The company automates the entire invoice workflow using Azure Logic Apps.  Step 1: Extract Invoice Data – Logic Apps connects to Office 365 & Outlook, extracts PDFs, and uses AI-powered OCR to read invoice details.  Step 2: Validate Data – The system cross-checks invoice amounts and supplier details against purchase orders in the ERP.  Step 3: Approval Workflow – If all details match, the invoice is auto-approved. If there’s a discrepancy, it’s sent to finance via Teams for review.  Step 4: Update SAP & Notify Suppliers – Once approved, the invoice is automatically logged in SAP, and the supplier gets a payment confirmation email.  Result:  With Azure API Management, Service Bus, and Logic Apps, businesses can:  Many organizations are also shifting towards no-code solutions like Logic Apps for faster integrations. Whether you’re looking for API security, event-driven automation, or workflow orchestration, Azure Integration Services has a solution for you.  Azure Integration Services (AIS) is not just a collection of tools—it’s a game-changer for businesses looking to modernize their integrations, reduce operational costs, and improve scalability. From secure API management to reliable messaging and automation, AIS provides the flexibility and efficiency needed to handle complex business workflows seamlessly.  The numbers speak for themselves—organizations have saved hundreds of thousands of dollars while improving their integration capabilities. Whether you’re looking to streamline supplier connections, optimize order processing, or migrate from legacy systems, AIS has a solution for you.  What’s Next?  In our next article, we’ll take a deep dive into a real-world scenario, showcasing how we helped our customer Buchi transform their integration landscape with Azure Integration Services.  Next Up: Why AIS? How Easily Azure Integration Services Can Adapt to Your EDI Needs.  Would love to hear your thoughts! How are you handling enterprise integrations today? Comment down below ???? or contact us at transform@cloudfronts.com