Xiaorui Wang’s Post

This study is our comparison of #MicrosoftFabric Real-Time Intelligence against a leading competitive build solution that uses Confluent Kafka and Confluent Flink running on AWS. It offers measurements of scale, performance, and cost under common scenarios as one set of inputs to help organizations identify the platform best suited to their #realtime data needs. This evaluation tested two critical real-time use cases across low-volume (10 GB/hour) and high-volume (100 GB/hour) scenarios to reflect real-world enterprise demands: Scenario 1: Real-time #streaming ingestion and #analytical query executions Scenario 2: Real-time streaming ingestion and alert notifications https://lnkd.in/g8bQBJhW #datascience #artificialintelligence #bigdata #analytics #machinelearning #Cloud #BusinessIntelligence #dataintegration #datamanagement #digitaltransformation #DataLake #datawarehouse #MasterDataManagement #VectorDatabase #database #Streamingdata #DataObservability #LLM

Ever wondered how merging Azure Event Hubs with AWS Kinesis can enhance your data streaming pipeline? By implementing a cross-cloud architecture, a recent integration achieved a 40% increase in data throughput while reducing latency by 20ms. 🔄📊 The key was configuring Azure Event Hubs for high-volume ingress and pairing it with the real-time processing power of AWS Kinesis. This hybrid setup ensured robust scalability and faster data access across geographic regions. For those looking to implement a similar architecture, focusing on the seamless connectivity and security between services is paramount. Interested in exploring how this can be applied to your business? DM me for a breakdown of the setup and tips on securing cross-cloud integrations.#CloudIntegration,#DataStreaming,#Azure,#AWS,#HybridCloud,#LatencyImprovement,#SystemArchitecture

🚀 As Kubernetes adoption grows, many organizations are running it side-by-side with Cloud Foundry & raising new challenges for managing data services across both platforms. At Cloud Foundry Day, Julian Fischer, CEO of anynines will introduce the latest updates to Klutch, the open-source control plane for orchestrating database services in hybrid CF/K8s environments. ✨ Key highlights: Leverages  the familiar CF marketplace + OSBAPI for self-service data access New Klutch Service Broker implementation designed for Cloud Foundry users Expanded support for automation backends: BOSH, Kubernetes Operators, and cloud-native APIs like AWS RDS & S3 Streamlined provisioning, lifecycle management, and backup/restore at scale, without compromising developer experience Whether you’re running a classic CF setup or moving toward a Kubernetes-first future, Klutch helps bridge the gap and keep data services consistent across environments. #anynines #CloudNative #DevOps #CloudFoundryDay

Automating workflows is key in cloud architecture, and AWS S3 Event Notifications are your secret weapon! 🚀 Imagine your S3 bucket as a smart warehouse. Instead of constantly checking for new deliveries or changes, you've set up an intelligent alert system. When a new object is uploaded, modified, or deleted, S3 Event Notifications automatically broadcast a message. This message can instantly trigger a Lambda function for real-time processing, send a notification via SNS, or queue a task in SQS for asynchronous work. This powerful feature eliminates the need for continuous polling, reducing costs and latency while enabling highly responsive, event-driven architectures. From image resizing to data pipeline orchestration, the possibilities are endless! #AWSS3 #CloudComputing #EventDrivenArchitecture #Serverless #AWS

The latest update for #Confluent includes "Scaling Kafka Streams Applications: Strategies for High-Volume Traffic" and "Cross-Data-Center Apache Kafka Replication: Decision Framework & Readiness Playbook". #DataStreaming #DataPipelines #Cloud https://lnkd.in/dtgUKR_3

Ever wondered how integrating Azure's Event Hubs and AWS Kinesis can revolutionize data ingestion and streaming in real-time? Using a unified approach, we achieved a cross-cloud setup that processes over 1 million events per hour with seamless scalability and reliability. This system uses Azure Event Hubs for Kafka-compatible event ingestion, combined with AWS Kinesis for enhanced data stream management. This dual setup allows handling massive spikes without a hitch, proving critical during high-demand periods. Want to see how this can be leveraged in your operations? DM me to dive deeper into this architecture!#CloudArchitecture,#DataStreaming,#Azure,#AWS,#SystemsIntegration,#RealTimeData,#TechInnovation

Did you know integrating AWS Step Functions with Lambda can drastically streamline your event-driven architectures? Leveraging this combo, I designed a system handling 500K events/day, reducing latency to under 50ms. 📈 Here's a mini-config to illustrate: ```json { "Comment": "Trigger Lambda from Step Function", "StartAt": "InvokeLambda", "States": { "InvokeLambda": { "Type": "Task", "Resource": "arn:aws:lambda:example-function", "End": true } } } ``` Need to upscale your cloud architecture with such precision? DM me for insights and consultancy tailored to your business needs.#CloudArchitecture,#AWS,#StepFunctions,#Lambda,#EventDriven,#SystemIntegration,#CloudConsulting

How often do your cloud services integrate seamlessly? Let's deep dive into a key architecture using AWS Lambda and S3. By configuring Lambda to trigger from S3 events, we can process data in real-time, significantly boosting throughput while cutting operational costs. 🚀 In a recent setup for a tech SaaS, this architecture sliced their data processing costs by 30%, thanks to efficient, event-driven operations. Curious how this can apply to your business? DM me to leverage similar wins in your architecture!#CloudArchitecture,#AWSLambda,#DataProcessing,#CloudCostReduction,#TechSaaS,#SystemIntegration

In today’s cloud-driven environment, balancing performance, scalability, and cost is critical. Our team recently took a deep dive into our Kubernetes setup — and the results have been truly rewarding! 💡 Here’s what we achieved through smart orchestration and optimization: ✅ Right-sized our workloads — Eliminated over-provisioned resources by implementing dynamic scaling. ✅ Optimized cluster utilization — Leveraged node auto-scaling and affinity rules for efficient scheduling. ✅ Adopted spot and reserved instances for non-critical workloads, reducing compute cost significantly. ✅ Automated monitoring & alerting using Prometheus and Grafana to identify unused resources in real-time. 💰 Outcome: Reduced overall infrastructure cost by 30%, while maintaining system reliability and scalability. Kubernetes continues to prove itself as a game-changer — not just for automation and deployment, but also for strategic cost management in modern cloud ecosystems. 🌩️ #Kubernetes #CloudOptimization #DevOps #Microservices #CostEfficiency #CloudNative #EngineeringExcellence

Cloud data warehouse migrations promise scale and elasticity—but the hard part is rebuilding mappings, pipelines, and documentation without breaking trust. Our playbook weighs benefits/risks and shows where automation saves the most time (schema, SCDs, lineage, tests). You’ll also see how BimlFlex uses metadata to regenerate code and docs for phased cutovers. Read more: https://lnkd.in/eqqzbmKp