Saasverse’s Post

✒️ Insights on real-time data processing with Azure Stream Analytics! 📊 💹 Harnessing the power of Azure Stream Analytics, we can now process and analyze massive streams of data in real-time. This capability is transforming how we derive actionable insights and make data-driven decisions. Key features of Azure Stream Analytics: 📎 Real-Time Processing: Instantly analyze data streams for timely insights. ⏱️ 📎 Scalability: Seamlessly handle data from various sources, scaling to meet your needs. ✨ 📎 Ease of Integration: Connect with Azure services like Event Hubs, IoT Hub, and Blob Storage. ☁️ 📎 Advanced Analytics: Use SQL-like language for complex event processing and pattern matching. 🔍 📎 Reliability and Security: Ensure data integrity with built-in redundancy and encryption. 🛡️🔐 📎 Build serverless streaming pipelines: Focus on your logic, not managing infrastructure. 📃 📎 Deploy on-premises or in the cloud: Get flexibility for your specific needs. 💭 #AzureStreamAnalytics #RealTimeData #DataProcessing #CloudComputing #DigitalTransformation #Azure #DataAnalytics #RealTime #Cloud #Serverless 🌐🚀📊🔄💡📂

I recently attended a thought-provoking webinar organized by Skills4U and Abdul Moiz, exploring the transformative potential of Artificial Intelligence (AI) in cloud computing. The session highlighted key trends and innovations shaping the future of AI in cloud, including AI-driven cloud infrastructure for enhanced scalability and efficiency, Machine Learning optimizations for cloud-based applications, and cloud-based AI platforms for streamlined model training and deployment. The discussion also touched on the exciting possibilities of Edge AI and IoT applications for real-time data processing, as well as crucial security and ethics considerations in cloud-based AI implementations. Looking ahead, the future of AI in cloud promises automated cloud management and optimization, intelligent data analytics and decision-making, enhanced customer experiences through personalized services, and accelerated innovation and reduced costs. Skills4U Abdul Moiz

Q.Need to stream 100 gb data each day what services will be usit inside azure? ✅For streaming 100 GB of data each day in Azure, Azure Stream Analytics is a solid platform choice ✅It's a fully managed real-time event processing service that's efficient and scalable, making it ideal for processing large volumes of streaming data. ✅Additionally, it seamlessly integrates with other Azure services like Azure Event Hubs, Azure IoT Hub, and Azure Data Lake Storage, providing a comprehensive solution for your streaming data needs. ✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅ #DataEngineering #BigData #ETL #DataPipeline #ApacheSpark #DataOps #DataIntegration #DataWarehousing #DataInfrastructure #CloudDataEngineering #DataArchitecture #StreamingData #DataIngestion #DataLake #DataEngineeringLife

🌟 Exploring Real-Time Analytics with Modern Data Streaming Architectures on AWS 🌟 Data streaming has become a game-changer across industries like IoT, media, and healthcare. Having the right architectures and following best practices for these workloads is key to achieving great performance. Last week, I worked on building a real-time analytics platform using AWS services, inspired by the concepts outlined in Modern data architecture blog (https://lnkd.in/dxmYUdpG). Here’s the architecture I developed: ✅ Requests are sent through a Streamlit web page and ingested via Kinesis Data Streams. ✅ Data is stored in Amazon S3, acting as a Data Lake. ✅ The stored data is queried using Athena and visualized in QuickSight to create interactive dashboards that facilitate data-driven decisions. This approach ensures scalability, low latency, and robust insights for real-time use cases. As data streaming continues to grow in importance, it’s exciting to see how these tools can be leveraged for innovation across industries! 🚀 #AWS #AmazonWebServices #Streaming #Kinesis #QuickSight

Interested in cloud computing and the latest trends? Beau has written a great guide...

Day 37: Event Processing Frameworks—The Power of Real-Time Insights Welcome to Day 37 of 100 Days of Cloud! Yesterday, we discussed event streaming. Today, let’s take it further by exploring event processing frameworks, which enable us to analyze and act on streaming data in real time. What Are Event Processing Frameworks? Event processing frameworks simplify the ingestion, processing, and transformation of event streams, allowing you to: • Detect patterns or trends in real time. • Trigger actions based on specific events. • Build powerful pipelines for continuous data processing. Key Frameworks in AWS & Azure 1. AWS Kinesis Data Analytics • Purpose: Analyze streaming data using SQL. • Use Case: Real-time dashboard updates, anomaly detection. 2. Azure Stream Analytics • Purpose: Serverless, real-time analytics. • Use Case: IoT data processing, log monitoring. Both frameworks integrate seamlessly with their respective event streaming services like Kinesis or Event Hubs. Use Cases in Industry • Financial Services: Monitor transactions for fraud detection. • E-Commerce: Personalize recommendations based on browsing history. • IoT: Process and act on sensor data from smart devices. • Healthcare: Analyze patient vitals for emergency alerts. Engagement Task Can you think of a system where event processing could optimize performance? • Share your ideas in the comments! Tomorrow, we’ll explore Serverless Computing and its role in modern event-driven architectures. #100DaysOfCloud #EventProcessing #AWS #Azure #RealTimeData #CloudComputing #DevOps

🌟 See You Tomorrow at the CDAO Fall Event in Boston, MA! 🌟 Are you ready to take your data strategy to the next level? Stop by our table at CDAO Fall to discover how MILL5 can help you eliminate data silos, set data governance standards, and optimize your data and AI/ML solutions for maximum ROI. Our data and AI experts will show you how to streamline data management and improve decision-making with actionable insights. Also, join us for a personalized demonstration of Microsoft Azure Data Fabric. Let’s talk about your data needs and how we can help! Comment below if you are also attending the event. #CDAOFall #Data #AI #ML #Cloud #IoT #MicrosoftFabric

How does Norway's leading IoT company Embriq ready its tech infrastructure to tackle climate change and related regulations? Learn more: https://ow.ly/geFL50SBYKm The blog discusses how Embriq teamed with Navisite, an Accenture company, and Amazon Web Services (AWS) to migrate their flagship meter data management service to the cloud, reaching new levels of flexibility, scalability and security in the process. ☁️🌏🚀🧡 #AWS #MAP #DigitalTransformation #AWSMarketplace

Top 5 DevOps trends to watch in 2024: 🚀 AI and Machine Learning Integration 🔒 DevSecOps and Enhanced Security 🌐 Edge Computing and IoT ☁️ Multi-Cloud and Hybrid Cloud Adoption 🔄 GitOps and Infrastructure as Code (IaC)

This is great starting point if you are looking to setup full blown IoT real time, edge and cloud analytics. https://lnkd.in/eAKdQs9M DuckDB is a promising option if you are looking to process GBs of data on the #edge This reference architecture is a great starting point. DuckDB with dbt Labs, QuestDB, Dagster together makes a great platform to build analytics on Edge. Messages can be sent further to Kafka or Azure IoT Hub to build cloud analytics using Azure Data Lake, dbt Labs Databricks. All the steps to setup your first end to end IoT and Edge platform are here. https://lnkd.in/eurvAPri https://lnkd.in/enGUxUW3