Event-Driven Analytics Solutions

💭 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

A while back, I shared how I was experimenting with monitoring EMQX events and pushing them into ClickHouse for analytics. My initial design had EMQX send events to a webserver, which would process and transform the payload before inserting it into ClickHouse. This worked fine for small-scale scenarios, but at higher event rates, the webserver quickly became a throughput bottleneck. It also added operational overhead, extra network hops, and unnecessary cost. After diving deeper into ClickHouse’s capabilities, I realized it supports direct ingestion over HTTP. This allowed me to eliminate the webserver entirely and configure EMQX’s data bridge to send events directly into ClickHouse. The benefits were immediate - lower latency due to fewer hops, reduced infrastructure costs by removing an extra component, and a much simpler architecture that’s easier to debug and maintain. On top of that, EMQX’s rule engine allows in-flight payload manipulation. I can now filter, enrich, and reshape event data within EMQX before it ever reaches ClickHouse, keeping the pipeline both lightweight and flexible. This approach already feels far more scalable than my first attempt. I’m now exploring further optimizations - I have a few ideas, and I’ll be playing around to see how much more I can push this. For those interested in trying this out - check out this guide I wrote. Link: https://lnkd.in/dVZSw48F

Design for Scale - 2: The Power of Events in Distributed System Events are fundamental building blocks in modern distributed systems, yet their importance is often underappreciated. To understand their power, we must first distinguish events from commands and queries. Events represent immutable facts - things that have already occurred. In contrast, commands express intentions that may or may not succeed. While this distinction can be subtle, it's crucial for system design. Interestingly, we can also treat commands and queries themselves as event streams in different contexts, representing the historical record of customer interactions with our system. This event-centric thinking unlocks elegant solutions to traditionally complex problems. The most common type of event is Change Data Capture. I worked on a quota enforcement tracking resource usage system for millions of customers. The initial approach using scheduled batch queries placed enormous stress on the database. However, by recognizing that data volume was high but change velocity was relatively low, we pivoted to an event-driven approach: establish baseline counts and track mutations through events. This transformation converted a challenging scaling problem into simple in-memory counting. The durability of events provided built-in reliability - if processing failed, we could replay the event stream. We further optimized by buffering rapid add/delete operations in memory, allowing them to cancel out before writing to the quota system, dramatically reducing write pressure. Events can elegantly address the notorious distributed transaction problem through the Saga pattern. Instead of struggling with complex transaction coordination across heterogeneous datastores, we can listen to committed events from the primary system and reliably propagate changes. This approach transforms a difficult distributed transaction problem into a more manageable event-based synchronization challenge. This pattern isn't new - many database systems internally use similar approaches like write-ahead logs or commit logs for replication and synchronization. Events also provide a powerful foundation for system validation and auditing. Independent systems can cross-check correctness and completeness by consuming the same event streams. This pattern has proven successful even in language models for improving result accuracy. But events encompass more than just data changes. Metrics, application logs, audit trails, and user interactions all represent valuable event streams. This broader perspective enables creative solutions to seemingly intractable problems. Treating events as first-class citizens in distributed system design leads to more scalable, reliable, and maintainable architectures. Whether handling data mutations, system operations, or user interactions, event-driven approaches often simplify complex problems while providing built-in reliability and auditability. Befriend with your events!

Modern financial operations demand the ability to process millions of invoices daily, with low latency, high availability, and real-time business visibility. Traditional monolithic systems struggle to keep up with the surges and complexity of global invoice processing. By adopting an event-driven approach, organizations can decouple their processing logic, enabling independent scaling, real-time monitoring, and resilient error handling. Amazon Simple Queue Service (#SQS) and Amazon Simple Notification Service (#SNS) enable resilience and scale in this architecture. SNS acts as the event router and broadcaster in this architecture. After events are ingested (via API Gateway and routed through EventBridge), SNS topics are used to fan out invoice events to multiple downstream consumers. Each invoice status—such as ingestion, reconciliation, authorization, and posting—gets its own SNS topic, enabling fine-grained control and filtering at the subscription level. This ensures that only relevant consumers receive specific event types, and the system can easily scale to accommodate new consumers or processing requirements without disrupting existing flows. Each SNS topic fans out messages to one or more SQS queues. SQS provides the critical function of decoupling the event delivery from processing. This means that even if downstream consumers (like AWS Lambda functions or Fargate tasks) are temporarily overwhelmed or offline, no events are lost—SQS queues persist them until they can be processed. Additionally, SQS supports dead-letter queues (DLQs) for handling failed or unprocessable messages, enabling robust error handling and alerting for operational teams. Specific to resilience and scale, look at these numbers.... • Massive Throughput: SNS can publish up to 30,000 messages per second, and SQS queues can handle 120,000 in-flight messages by default (with quotas that can be raised). This supports surges of up to 86 million daily invoice events. • Cellular Architecture: By partitioning the system into independent regional “cells,” each with its own set of SNS topics and SQS queues, organizations can scale horizontally, isolate failures, and ensure high availability. • Real-Time Monitoring: The decoupled, event-driven flow—powered by SNS and SQS—enables near real-time dashboards and alerting, so finance executives and auditors always have up-to-date visibility into invoice processing status. #financialsystems #cloud #data #aws https://lnkd.in/gNnYpeu7

⚙️ Let’s say a user opens an app like Tiktok/Instagram/YouTube, scrolls through videos, likes, and comments on another - all in under 60 seconds. Each of those actions is an event that your systems need to capture, process, and react to --> in Real-time. 📌 Here’s how Kafka makes that possible, let's walkthrough using a real-life example: "Building a real-time engagement dashboard" 👇 🎯 The Use Case : Real-Time Video Engagement Dashboard 🔹Metrics we might need to track: 🚀 Views, likes, shares, and comments 🚀 Per region, per creator And then surface it within seconds to any internal analytics tools ================================= Why batch ETL is not a good option: ❌ Too slow (15–30 min delay = old data, based on your ingesting partitions) ❌Too rigid (hard to update schema on the fly) ❌Not scalable for billions of events per day ================================= 🧱 Kafka-Based Real-Time ETL Flow : 1️⃣ Producers (mobile apps and edge servers) stream click events to Kafka topics like video_views, likes, comments 2️⃣ Each topic is partitioned by video_id or user_id or date or region for parallel processing (based on your business requirement) 3️⃣ Spark Structured Streaming consumes these events in micro-batches, applies lightweight transformations (timestamp parsing, rolling counts, windowed aggregations etc) 4️⃣ Output is written to Data warehouse/Data lake or your storage components, partitioned by date or other fields - then ready for query. ================================= Real-time dashboards could query from this Data lake/Data warehouse directly or via materialized views. 🔍 What Kafka Enables Here ✅ Event-driven architecture - data flows in as it happens ✅ Fault-tolerance - missed data? You can replay them via offset settings (Based on your kafka retention time though) ✅ Loose coupling - teams writing producers don’t need to know about consumers ✅ High scale - billions of events/day, with horizontal scaling via partitions ================================= ⚠️ Key Tips from Experience ➡️ Monitor consumer lag - always. It tells you if your jobs are falling behind - monitor like your SLA depends on it (because it does) ➡️Handle schema evolution proactively ➡️Use checkpointing and exactly-once guarantees where possible ➡️Start small, test with mock events before connecting to topics in production ================================= 💬 Your Turn Are you using Kafka for real-time ETL too? Would love to hear what use cases you’re solving, and how you’ve handled scale, schema changes, or failure recovery #kafka #dataengineering #streamingdata #etl #realtimedata #bigdata #sparkstreaming #moderndatastack #tiktok #apachekafka #dataarchitecture #analyticsengineering #instagram #delta #youtube #learningtogether #realtimedata #kafkastreams #flink #dataarchitecture #realworldengineering

Energy Trading with Apache Kafka and Flink: Real Time Decisions in Action Real time data has transformed how the #energy sector operates. In energy trading, every second matters. Prices change fast. Supply and demand fluctuate. Weather patterns shift. #IoT sensors and smart meters constantly feed new information into trading systems. That is where #DataStreaming with #ApachaKafka and #ApacheFlink comes in. Together, they power the real time pipelines that make energy markets more transparent, responsive, and predictable. Leading companies such as #Uniper, #realto, and #Powerledger already rely on this architecture. Their results show how scalable, reliable, and event driven data streaming brings measurable business impact: • Faster decision making and improved risk management • Automated trading workflows and event driven alerts • Real time integration of IoT data from energy grids and sensors • Improved forecasting with fresh, contextual data Uniper uses Kafka and Flink to process millions of messages per day across trading, dispatch, and invoicing systems. Confluent Cloud provides the scalability and SLAs for mission critical workloads. Powerledger combines Kafka and #blockchain to enable peer to peer energy trading and renewable energy certificate tracking. re.alto connects smart meters, APIs, and #IIoT systems for solar and smart charging use cases. These examples show how Data Streaming creates the foundation for next generation #EnergyTrading systems, uniting financial and IoT data to deliver real time insights, flexibility, and compliance. In a world where milliseconds can mean millions, Apache Kafka and Apache Flink are not just technologies. They are strategic tools for modern energy companies. How is your organization preparing to handle the growing demand for real time data in trading and energy operations? #DataInMotion #IoT #StreamingAnalytics #EnergyInnovation #AI #EventDrivenArchitecture https://lnkd.in/eHAdJEcg

Event Stream Processing (ESP) is now indispensable for augmenting enterprise decision-making with AI, enabling the near-real-time responsiveness critical in a data-saturated operational landscape. Eschewing the latency of data-at-rest architectures, ESP processes data in motion as it arrives, providing immediate insights. This architecture is paramount for cost optimization, building highly efficient streaming data engineering pipelines that reduce storage, network, and processing overhead by filtering and condensing vast data volumes at or near the source. The widespread availability of open-source ESP technology, managed cloud services and GenAI's capability to process unstructured edge data with declining compute costs has lowered implementation barriers, making previously cost-prohibitive use cases viable. I champion the decision intelligence (DI) practice that explicitly engineers how decisions are made, starting with the desired outcome in mind and tracing the action-to-outcome path. This requires evaluating real-time data applicability, designing architectures and leveraging Decision Intelligence Platforms (DIPs) that seamlessly ingest real-time contextual data via ESP for decision orchestration and monitoring. DI necessitates decision modeling to make flows explicit. Get into the flow. Crucially, integrating GenAI copilots into ESP development offers substantial cost reduction and accelerates time-to-value by simplifying concepts and assisting code generation and pattern discovery. Prioritize internal streams before external, investigating GenAI interaction insights for use cases like personalization. However, avoid the red herrings: ESP platforms are overkill for low-volume streams or applications without stringent real-time needs; simpler tools suffice. Treating streaming data like batch for later processing introduces unacceptable latency. While stream analytics can run in ESP, not every algorithm needs a separate tool beyond the ESP framework. GenAI copilots aid development but demand human oversight for validation and mitigating hallucinations; their current limitations in reliability, transparency, and reasoning mean they are not yet widely trusted for decision automation or reliable support/augmentation in DIPs. Successfully integrating ESP and AI within DI solutions demands comprehensive organizational alignment – data management, governance, AI literacy, risk, and cultural change – not just technology. #DecisionIntelligence #EventStreamProcessing #RealTime