Adopting Headless Commerce

Let's be honest... most of us are living in digital chaos right now; Data, technology, and new product overload. How do you make sense of it all? Establishing your own set of Golden Rules Golden rules are the non-negotiable principles that offer a blueprint for success. In digital transformation, they are the critical load-bearing walls that support the entire structure of transformational change. Here are my 10 Golden Rules for Successful Digital Transformation: 𝟏. 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐞 𝐄𝐧𝐝-𝐔𝐬𝐞𝐫 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞: Always craft your digital interfaces and processes with the end-user in mind, ensuring that every interaction is intuitive, engaging, and satisfying. 𝟐. 𝐂𝐨𝐦𝐦𝐢𝐭 𝐭𝐨 𝐂𝐨𝐧𝐭𝐢𝐧𝐮𝐨𝐮𝐬 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠: Foster a culture where ongoing education is valued, enabling your team to stay ahead of the curve by mastering new technologies and methodologies as they emerge. 𝟑. 𝐔𝐩𝐡𝐨𝐥𝐝 𝐃𝐚𝐭𝐚 𝐒𝐞𝐜𝐮𝐫𝐢𝐭𝐲 & 𝐏𝐫𝐢𝐯𝐚𝐜𝐲: Vigilantly guard your customer’s data as if it were your own, implementing robust security protocols and privacy measures to maintain trust and compliance. 𝟒. 𝐄𝐦𝐛𝐫𝐚𝐜𝐞 𝐀𝐠𝐢𝐥𝐞 𝐌𝐞𝐭𝐡𝐨𝐝𝐨𝐥𝐨𝐠𝐢𝐞𝐬: Adopt a flexible and responsive approach to project management, allowing for rapid iteration and adaptation in the face of changing digital landscapes. 𝟓. 𝐁𝐫𝐞𝐚𝐤 𝐃𝐨𝐰𝐧 𝐃𝐚𝐭𝐚 𝐒𝐢𝐥𝐨𝐬: Encourage a collaborative environment where data flows freely between departments, enhancing decision-making and fostering a unified view of the business. 𝟔. 𝐂𝐨𝐧𝐝𝐮𝐜𝐭 𝐑𝐞𝐠𝐮𝐥𝐚𝐫 𝐓𝐞𝐬𝐭𝐢𝐧𝐠: Implement a rigorous testing regime to identify and address issues early on, ensuring that your digital offerings are resilient and reliable. 𝟕. 𝐃𝐞𝐬𝐢𝐠𝐧 𝐟𝐨𝐫 𝐅𝐮𝐭𝐮𝐫𝐞 𝐆𝐫𝐨𝐰𝐭𝐡: Anticipate the scalability of your digital solutions, ensuring that they can evolve and expand as your business grows and market demands shift. 𝟖. 𝐑𝐞𝐠𝐮𝐥𝐚𝐫𝐥𝐲 𝐑𝐞𝐯𝐢𝐬𝐞 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐞𝐬: Continually reassess and refine your digital strategies to stay relevant and effective in an ever-evolving technological ecosystem. 𝟗. 𝐄𝐧𝐠𝐚𝐠𝐞 𝐚𝐧𝐝 𝐈𝐧𝐯𝐨𝐥𝐯𝐞 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩: Ensure that your leadership is actively involved in driving digital initiatives, setting a visionary tone and aligning digital goals with business objectives. 𝟏𝟎. 𝐌𝐚𝐢𝐧𝐭𝐚𝐢𝐧 𝐓𝐫𝐚𝐧𝐬𝐩𝐚𝐫𝐞𝐧𝐭 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧: Cultivate an environment where communication is clear and open, establishing a foundation of transparency that builds trust and facilitates smoother digital transitions. Use this as a framework to write your own set of Golden Rules, and communicate them to EVERYONE who is a part of the transformation. 𝐅𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞: https://lnkd.in/e_TGu_4D What else would you add to the list?

When I first started working with Databricks Unity Catalog, I wish someone had told me this simple but crucial detail about catalog provisioning and storage locations. If you’re about to set up a new catalog and the objects underneath, here’s what you need to know: ⁉️ What really happens when you create a new catalog? 💡 Sure, a new catalog gets registered in Unity Catalog. But if you don’t specify a storage root (location) during creation, Databricks will make it a managed catalog—and all your data will automatically land in the default metastore storage location. ⁉️ Why does this matter? 💡 Let’s say you move on to create a schema, again without specifying a storage path. That schema will also default to the metastore location. And when you create tables under that schema, unless you explicitly set a location, those tables will be managed tables (again), stored in the central metastore location. 🤷♂️ The hidden impact: If you’re building a data mesh or want clear data ownership boundaries, this can be a big deal. All your data across different catalogs, schemas, and tables ends up in a single, central storage account that you might not fully control. This can complicate data governance, access control, and cost allocation down the road. Also, it could result in too many API calls towards the same storage account which could lead to throttling as Azure enforces scalability targets (limits) on requests per sec for storage accounts. ✅ My tip on best practices for catalogs and schemas: 👉 Always specify the storage location when creating catalogs and schemas if you want true data isolation and ownership. 👉 Review your Unity Catalog setup to ensure your data lands where you expect it to! ☘️ Irrespective of the type of tables (managed or external) you're provisioning, make sure they land in the appropriate storage account otherwise their migration (in the future) will be hell of a task. ✅ My tip on best practices to avoid throttling (be futuristic): 👉 Use multiple storage accounts for different catalogs, domains, or high-traffic workloads. 👉 For blob, organise your catalogs, schemas and tables in a well-defined hierarchy. ⚠️ Trust me! Besides above points, it will result in a problematic situation if at any point of time, your team plans to migrate your UC External tables to Managed ones (I'll talk about it in a future post 😉). #Databricks #UnityCatalog #DataGovernance #DataEngineering #BestPractices

𝗡𝗲𝘅𝘁-𝗟𝗲𝘃𝗲𝗹 𝗗𝗮𝘁𝗮 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲: 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗔𝗰𝗰𝗲𝘀𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 𝘄𝗶𝘁𝗵 𝗔𝗕𝗔𝗖 𝗶𝗻 𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀 Hej! 👋 We're all familiar with Role-Based Access Control (RBAC) in Unity Catalog: GRANT SELECT ON a_table TO data_analysts. This works well, but what happens when the rules get more complex? What if only certain users can access columns with PII, or if access depends on the user's department? This is where RBAC reaches its limits and we need a more dynamic approach: Attribute-Based Access Control (ABAC). Instead of hundreds of static rules, ABAC allows us to define universal policies based on metadata—or attributes. How It Works in Practice (Simplified) 1. Tag Data with Attributes: First, we classify our data. We assign a tag to a column, table, or schema. - Example: We tag the email column with the tag pii_data = 'true'. 2. Assign Attributes to Users/Groups: We define attributes for our users or groups. - Example: The group finance_de receives the attribute department = 'finance'. 3. Define Rules Connecting Attributes: Now for the magic. We create a rule that uses these attributes. - Example Rule: "ALLOW access to all data tagged with pii_data = 'true' ONLY for groups that have the attribute clearance = 'level_3'." Why is this important? - Scalability: When a new employee joins the team, we just need to assign them to the right group with the right attributes. We no longer have to execute 20 different GRANT commands. Access rights are determined automatically by their attributes. - Dynamism: If the status of data changes (e.g., from "confidential" to "public"), we only need to change the tag on the table. All access rules adapt immediately and automatically. - Fine-Grained Control: ABAC enables extremely detailed control that goes far beyond table or schema boundaries. It is the key to securely managing sensitive data in large organizations. For me, ABAC is the logical evolution of data governance in the Lakehouse. We're moving from a rigid, object-based permission model to a flexible, policy-based system that grows with the business. Are you already using tags in Unity Catalog to classify your data? Or do you already have initial use cases for ABAC in mind? Share your thoughts! 👇 #Databricks #DataGovernance #UnityCatalog #ABAC #BestPractices #DataInsightConsulting

SEO plan 2025 A – Audit Your Website: Begin with a comprehensive SEO audit. Use tools like Screaming Frog or Ahrefs to identify broken links, duplicate content, and technical errors. B – Build Backlinks: Quality backlinks remain crucial. Focus on guest posting, digital PR, and creating link-worthy content. C – Core Web Vitals: Optimize for Google’s Core Web Vitals (LCP, FID, CLS) to enhance user experience and improve rankings. D – Data-Driven Decisions: Use Google Analytics and Search Console to track performance and guide your SEO strategies. E – E-A-T Compliance: Establish Expertise, Authoritativeness, and Trustworthiness in your niche, especially for YMYL (Your Money Your Life) websites. F – Fresh Content: Regularly update or add new content. Google rewards websites that stay current and relevant. G – Google Business Profile: For local SEO, optimize and maintain an accurate Google Business Profile listing. H – Headings Optimization: Use H1, H2, H3 tags properly to structure content for both users and search engines. I – Internal Linking: Build a logical internal link structure to guide users and distribute link equity. J – JavaScript SEO: Ensure content rendered via JavaScript is crawlable and indexable by search engines. K – Keyword Research: Use modern tools like Semrush or Ubersuggest to identify long-tail and intent-driven keywords. L – Link Structure: Maintain clean and SEO-friendly URLs with proper slugs and no unnecessary parameters. M – Mobile Optimization: Ensure your website is mobile-responsive, as mobile-first indexing is now the standard. N – Niche Authority: Focus on creating depth in your content to become an authority in your niche. O – On-Page SEO: Optimize titles, meta descriptions, images (alt tags), and content around target keywords. P – Page Speed: Use tools like Google PageSpeed Insights to identify and fix slow-loading pages. Q – Quality Content: Always prioritize content that provides real value to users over keyword-stuffed articles. R – Responsive Design: Adapt your site design for all screen sizes and devices. S – Schema Markup: Implement structured data to enhance search listings with rich snippets. T – Technical SEO: Fix crawl errors, sitemaps, robots.txt, canonical tags, and other backend elements. U – User Experience (UX): A seamless UX improves dwell time, reduces bounce rate, and supports SEO. V – Voice Search Optimization: Target conversational queries and FAQs for better visibility in voice results. W – Web Security (HTTPS): Secure your site with SSL – it's a ranking factor and builds trust. X – XML Sitemap: Keep your XML sitemap updated and submit it to Google Search Console. Y – YouTube SEO: If you use videos, optimize titles, descriptions, and tags for better visibility on YouTube and Google. Z – Zero-Click Searches: Optimize for featured snippets, People Also Ask, and knowledge panels. #seoexpert #seo #topratedseoexpert #seotips #expartagency

How to Improve API Performance? If you’ve built APIs, you’ve probably faced issues like slow response times, high database load, or network inefficiencies. These problems can frustrate users and make your system unreliable. But the good news? There are proven techniques to make your APIs faster and more efficient. Let’s go through them: 1. Pagination - Instead of returning massive datasets in one go, break the response into pages. - Reduces response time and memory usage - Helps when dealing with large datasets - Keeps requests manageable for both server and client 2. Async Logging - Logging is important, but doing it synchronously can slow down your API. - Use asynchronous logging to avoid blocking the main process - Send logs to a buffer and flush periodically - Improves throughput and reduces latency 3. Caching - Why query the database for the same data repeatedly? - Store frequently accessed data in cache (e.g., Redis, Memcached) - If the data is available in cache → return instantly - If not → query the DB, update the cache, and return the result 4. Payload Compression - Large response sizes lead to slower APIs. - Compress data before sending it over the network (e.g., Gzip, Brotli) - Smaller payload = faster download & upload - Helps in bandwidth-constrained environments 5. Connection Pooling - Opening and closing database connections is costly. - Instead of creating a new connection for every request, reuse existing ones - Reduces latency and database load - Most ORMs & DB libraries support connection pooling If your API is slow, it’s likely because of one or more of these inefficiencies. Start by profiling performance and identifying bottlenecks Implement one optimization at a time, measure impact A fast API means happier users & better scalability. 𝐅𝐨𝐫 𝐌𝐨𝐫𝐞 𝐃𝐞𝐯 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐉𝐨𝐢𝐧 𝐌𝐲 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲 : Telegram - https://lnkd.in/d_PjD86B Whatsapp - https://lnkd.in/dvk8prj5 Happy learning !

Building Strong and adaptable Microservices with Java and Spring While building robust and scalable microservices can seem complex, understanding essential concepts empowers you for success. This post explores crucial elements for designing reliable distributed systems using Java and Spring frameworks. 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗮𝗹 𝗣𝗿𝗶𝗻𝗰𝗶𝗽𝗹𝗲𝘀 𝗳𝗼𝗿 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: The core principles of planning for failure, instrumentation, and automation are crucial across different technologies. While this specific implementation focuses on Java, these learnings are generally applicable when architecting distributed systems with other languages and frameworks as well. 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 𝗳𝗼𝗿 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: A typical microservices architecture involves: Multiple Microservices (MS) communicating via APIs: Services interact through well-defined Application Programming Interfaces (APIs). API Gateway for routing and security: An API Gateway acts as a single entry point, managing traffic routing and security for the microservices. Load Balancer for traffic management: A Load Balancer distributes incoming traffic efficiently across various service instances. Service Discovery for finding MS instances: Service Discovery helps locate and connect to specific microservices within the distributed system. Fault Tolerance with retries, circuit breakers etc.: Strategies like retries and circuit breakers ensure system resilience by handling failures gracefully. Distributed Tracing to monitor requests: Distributed tracing allows tracking requests across different microservices for better monitoring and debugging. Message Queues for asynchronous tasks: Message queues enable asynchronous communication, decoupling tasks and improving performance. Centralized Logging for debugging: Centralized logging simplifies troubleshooting by aggregating logs from all services in one place. Database per service (optional): Each microservice can have its own database for data ownership and isolation. CI/CD pipelines for rapid delivery: Continuous Integration (CI) and Continuous Delivery (CD) pipelines automate building, testing, and deploying microservices efficiently. 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗶𝗻𝗴 𝗦𝗽𝗿𝗶𝗻𝗴 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗳𝗼𝗿 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: Frameworks like Spring Boot, Spring Cloud, and Resilience4j streamline the implementation of: Service Registration with Eureka Declarative REST APIs Client-Side Load Balancing with Ribbon Circuit Breakers with Hystrix Distributed Tracing with Sleuth + Zipkin 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 𝗳𝗼𝗿 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗥𝗼𝗯𝘂𝘀𝘁 𝗠𝗶𝗰𝗿𝗼𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀: Adopt a services-first approach Plan for failure Instrument everything Automate deployment

Mastering Omnichannel Payments 💡 At its core, omnichannel payments is the ability to recognize customers, securely store credentials, cover all relevant payment use cases and payment methods with a common experience, and provide a single set of data and dashboard services to the merchant. Omnichannel payments is not a complex concept, but many PSPs and merchants struggle with infrastructure designed around a single channel, or disparate infrastructure resulting from acquisitions. We see omnichannel payments as driven by a handful of foundational principles: 1️⃣ For small merchants, it just has to be easy: Frictionless integration into the merchants' SaaS (WooCommerce plugins, etc.), single dashboard, single point of service, single set of settlement flows, etc. 2️⃣ For enterprise merchants, you also need a unified payment proposition … 🔹 Technical Integration Toolkit: a common set of technical tools (documentation, support, etc.), even if payment channels involve different APIs 🔹 Cross-channel tokenization: single tokenization service for recognizing customers across channels and, as needed, securely storing, and presenting the customer’s payment credentials 🔹 Settlement & Treasury: Automated and unified reconciliation across these channels achieved by the customers’ bank account harmonized in one single database 🔹 Data: Single data service with consolidated reporting for transactions across different channels and ideally, value-added analytics on customer behaviors 🔹 Commercial, relationship management: Single point of contact for account servicing, a common commercial and contracting model, and unified billing 3️⃣ … and for enterprise merchants, the PSP must enable all the relevant omnichannel use cases and transacting platforms: 🔹 Transacting Platforms: Web, In-app, POS device, Kiosk and Paylink (e-bill, chat, email, etc.) 🔹 Use Cases (noting that the commerce software does most of the work to enable these use cases, not the PSP): Click and collect, endless aisle, buy and return across channels, self-checkout, and Pre-auth or pay-as-you-go. PSPs must be omnichannel because most merchants (in mature markets) are now omnichannel and they increasingly demand an omnichannel payment proposition. Merchants need to be proactive and forward-looking when choosing a PSP that can facilitate their omnichannel strategy. PSPs, in turn, must adapt their offerings to deliver a unified proposition that is not channel siloed. Payment service providers that remain channel fragmented or limited will increasingly struggle to compete. Source: Flagship Advisory Partners - https://bit.ly/49gd2B5 #Fintech #Banking #Ecommerce #Retail #OpenBanking #API #FinancialServices #Payments #DigitalPayments #APM #Processing #Data #Omnichannel

If your CX Program simply consists of surveys, it's like trying to understand the whole movie by watching a single frame. You have to integrate data, insights, and actions if you want to understand how the movie ends, and ultimately be able to write the sequel. But integrating multiple customer signals isn't easy. In fact, it can be overwhelming. I know because I successfully did this in the past, and counsel clients on it today. So, here's a 5-step plan on how to ensure that the integration of diverse customer signals remains insightful and not overwhelming: 1. Set Clear Objectives: Define specific goals for what you want to achieve. Having clear objectives helps in filtering relevant data from the noise. While your goals may be as simple as understanding behavior, think about these objectives in an outcome-based way. For example, 'Reduce Call Volume' or some other business metric is important to consider here. 2. Segment Data Thoughtfully: Break down data into manageable categories based on customer demographics, behavior, or interaction type. This helps in analyzing specific aspects of the customer journey without getting lost in the vastness of data. 3. Prioritize Data Based on Relevance: Not all data is equally important. Based on Step 1, prioritize based on what’s most relevant to your business goals. For example, this might involve focusing more on behavioral data vs demographic data, depending on objectives. 4. Use Smart Data Aggregation Tools: Invest in advanced data aggregation platforms that can collect, sort, and analyze data from various sources. These tools use AI and machine learning to identify patterns and key insights, reducing the noise and complexity. 5. Regular Reviews and Adjustments: Continuously monitor and review the data integration process. Be ready to adjust strategies, tools, or objectives as needed to keep the data manageable and insightful. This isn't a "set-it-and-forget-it" strategy! How are you thinking about integrating data and insights in order to drive meaningful change in your business? Hit me up if you want to chat about it. #customerexperience #data #insights #surveys #ceo #coo #ai

Do you want to ensure high availability for your web applications on Azure? Check out my Disaster Recovery architecture, designed to keep your services running smoothly across multiple Azure regions. Here’s a step-by-step breakdown based on our architecture: 1. Azure Front Door manages traffic globally, providing quick failover to ensure users always reach your web apps, even during regional outages. 2. Azure App Service hosts APIs and web apps in both primary and secondary regions, maintaining availability and consistent performance. 3. Azure Queue Storage buffers incoming tasks for processing, handling spikes in traffic and keeping things running smoothly. 4. Azure Functions perform background tasks and monitor health status, ensuring timely responses and managing failovers. 5. Azure Cosmos DB supports multi-region replication, ensuring your data is available and up-to-date in both active and standby regions. 6. Azure Cache for Redis is deployed in multiple regions and replicates data to provide fast access, reducing load on the database and speeding up app performance. 7. Custom Replication Function ensures data consistency across Redis caches, making sure all regions have the latest updates. Benefits of a Two-Region Architecture: ✅ High Availability – Your applications remain accessible even if one region goes offline. ✅ Data Resilience – Multi-region replication and automated failover keep your data safe and accessible. ✅ Performance Optimization – Caches and distributed data storage enhance speed and reduce latency. Points to Consider: ➖ Regular monitoring is essential to detect any potential issues early and ensure automatic failovers work as expected. ➖ Conduct frequent testing of your disaster recovery setup to confirm that your system performs well when needed. Have you implemented a multi-region strategy for your cloud services? If not, then checkout my repo: https://lnkd.in/ehjvRJGA Share your experiences below! #Azure #CloudComputing #DisasterRecovery #SoftwareEngineering #DevOps

The "micro-" prefix is unfortunate. It's not about size. Microservices are more about managing PEOPLE than technology. They are widely misunderstood and misused. Use microservices to: - Reduce dependencies between teams. - Encapsulate *business* domains or functional areas. - More loosely couple services where flexibility is needed. This approach promises to improve the following: → Productivity — of each team → Agility — of each service → Evolvability — of the system → Scalability — of the system, or parts of it → Fault isolation These improvements make the system *as a whole* more complex, especially concerning: - End-to-end testing - Troubleshooting - Communications and networking - Deployment - Operations - Data consistency and management Like most decisions in engineering, it's a trade-off. It's a sliding scale, between the complexities of managing people and development processes at one end, and more complex technology operations at the other. The larger the scope of the product (in terms of business domains), the more engineers you need, and the more appealing it becomes to split your platform into distinct services. This typically happens once your engineering department grows past twenty people, and has teams specialising in well demarcated and divergent domains. The best way to split services is usually by business domain and functional area. These services end up being quite chunky, and I wouldn't call them *micro*services. "Macro" fits better than "micro," though a name with the word "domain" in it would do a lot more justice. Examples of functional and business domain-oriented services: - Authentication - Order processing - Product catalogue - Payment processing - Customer lifecycle management - Messaging (sends emails, tracks delivery) 👉 Your architectural design should be informed by business realities. Solid technological decisions are not made in a vacuum — they are business-driven.