Data Synchronization Tools

💭 Ever faced the challenge of keeping your data consistent across regions, clouds, and systems — in real time? A few years ago, I worked on a global rollout where CRM operations spanned three continents, each with its own latency, compliance, and data residency needs. The biggest question: 👉 How do we keep Dataverse and Azure SQL perfectly in sync, without breaking scalability or data integrity? That challenge led us to design a real-time bi-directional synchronization framework between Microsoft Dataverse and Azure SQL — powered by Azure’s event-driven backbone. 🔹 Key ideas that made it work: Event-driven architecture using Event Grid + Service Bus for reliable data delivery. Azure Functions for lightweight transformation and conflict handling. Dataverse Change Tracking to detect incremental updates. Geo-replication in Azure SQL to ensure low latency and disaster recovery. What made this special wasn’t just the technology — it was the mindset: ✨ Think globally, sync intelligently, and architect for resilience, not just performance. This pattern now helps enterprises achieve near real-time visibility across regions — no more stale data, no more integration chaos. 🔧 If you’re designing large-scale systems on the Power Platform + Azure, remember: Integration is not about moving data. It’s about orchestrating trust between systems. #MicrosoftDynamics365 #Dataverse #AzureIntegration #CloudArchitecture #PowerPlatform #AzureSQL #EventDrivenArchitecture #DigitalTransformation #CommonManTips

Ever tried keeping Salesforce data in sync with an external system, only to run into polling delays, missed deletes, or performance bottlenecks? I’ve found Change Data Capture (CDC) to be a game-changer for event-driven integrations. With CDC, every record create, update, delete, or undelete fires a “change event” into Salesforce’s event bus. External systems subscribe once and get only the changes they need—no more round-the-clock polling. Some favorite use cases: Sales Cloud → ERP sync: Account and Opportunity changes flow in real time to your finance system. Service Cloud → Ticketing: Case updates automatically create or update tickets in Jira or ServiceNow. On-platform automation: Complex recalculations or external callouts happen asynchronously via CDC triggers, not inside the user’s save. Pro tip: Leverage the ChangeEventHeader—it tells you exactly which fields changed, when, and even who triggered the change. Use changeOrigin to avoid feedback loops when syncing bi-directionally. How are you using CDC in your org? Share your experiences or questions below!

𝐖𝐡𝐚𝐭 𝐢𝐬 𝐂𝐡𝐚𝐧𝐠𝐞 𝐃𝐚𝐭𝐚 𝐂𝐚𝐩𝐭𝐮𝐫𝐞? Being a Data Engineer its not just important to build pipelines to deliver data to the consumers, but synchronising the data to empower organisations for propagating real-time data changes across distributed systems is equally important. Change data capture helps to capture, track and enable teams to replicate data instantly and incrementally by continuously monitoring a source database for changes (inserts, updates, and deletes) and capturing these modifications as they happen. The changes once captured are streamed to target systems such as data warehouses, data lakes, or other databases to keep them in sync with minimal latency. The working of the CDC follows the following steps: 1. Detect 2. Extract 3. Transform 4. Deliver Each of these leverages several approaches to implementing CDC depending on the technical capabilities: -> Log-Based  -> Query-Based -> Trigger-Based As a data engineer, when working with Change Data Capture, do not miss to consider the following: - Schema Evolution Handling - Error Handling and Recovery - Data Synchronization - Monitoring and Observability - Security and Compliance There could be various ways to implement CDC, but with the evolving industry some handy tools and services to leverage include: • AWS: AWS Database Migration Service (DMS) • GCP: Datastream (as shown in our architecture) • Azure: Azure Data Factory • Open Source: Debezium, Maxwell, Airbyte, Kafka Connect Implementing CDC effectively, can help data engineers build more resilient, efficient, and timely data pipelines with real-time data availability to deliver significant business value. 𝗛𝗼𝘄 𝗱𝗼 𝘆𝗼𝘂 𝗶𝗻𝗰𝗼𝗿𝗽𝗼𝗿𝗮𝘁𝗲 𝗖𝗵𝗮𝗻𝗴𝗲 𝗗𝗮𝘁𝗮 𝗖𝗮𝗽𝘁𝘂𝗿𝗲 𝗶𝗻 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝘀 𝗱𝗮𝘁𝗮 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀? Also, do let me know in comments if you would like me to share a detailed article on change data capture with some real time use case!! #data #engineering #cloud #azure #gcp #aws #bigdata

"Keeping Multiple #Databases in Sync in Real-Time Using #ApacheKafka Connect and #ChangeDataCapture" #Microservices architectures have now been widely adopted among developers, and with a great degree of success. However, drawbacks do exist. Data silos can arise where information processed by one microservice is not visible to the other microservice. This blog post will review the advantages and disadvantages inherent to moving data from a database using #JDBC and #CDC, and then explores the real use case of how a legacy bank used #KafkaConnect to bridge the silos and keep multiple applications/databases in sync. https://lnkd.in/esv_m5-w