Real-time Streaming Data Visualization

I’m thrilled to share my latest publication in the International Journal of Computer Engineering and Technology (IJCET): Building a Real-Time Analytics Pipeline with OpenSearch, EMR Spark, and AWS Managed Grafana. This paper dives into designing scalable, real-time analytics architectures leveraging AWS-managed services for high-throughput ingestion, low-latency processing, and interactive visualization. Key Takeaways: ✅ Streaming Data Processing with Apache Spark on EMR ✅ Optimized Indexing & Query Performance using OpenSearch ✅ Scalable & Interactive Dashboards powered by AWS Managed Grafana ✅ Cost Optimization & Operational Efficiency strategies ✅ Best Practices for Fault Tolerance & Performance As organizations increasingly adopt real-time analytics, this framework provides a cost-effective and reliable approach to modernizing data infrastructure. 💡 Curious to hear how your team is tackling real-time analytics challenges—let’s discuss! 📖 Read the full article: https://lnkd.in/g8PqY9fQ #DataEngineering #RealTimeAnalytics #CloudComputing #OpenSearch #AWS #BigData #Spark #Grafana #StreamingAnalytics

🌐 Building Real-Time Observability Pipelines with AWS OpenSearch, Kinesis, and QuickSight Modern systems generate high-velocity telemetry data—logs, metrics, traces—that need to be processed and visualized with minimal lag. Here’s how combining Kinesis, OpenSearch, and QuickSight creates an end-to-end observability pipeline: 🔹 1️⃣ Kinesis Data Streams – Ingestion at Scale   Kinesis captures raw event data in near real time:   ✅ Application logs   ✅ Structured metrics   ✅ Custom trace spans  💡 Tip: Use Kinesis Data Firehose to buffer and transform records before indexing. 🔹 2️⃣ AWS OpenSearch – Searchable Log & Trace Store   Once data lands in Kinesis, it’s streamed to OpenSearch for indexing.   ✅ Fast search across logs and trace IDs   ✅ Full-text queries for error investigation   ✅ JSON document storage with flexible schemas  💡 Tip: Create index templates that auto-apply mappings and retention policies. 🔹 3️⃣ QuickSight – Operational Dashboards in Minutes   QuickSight connects to OpenSearch (or S3 snapshots) to visualize trends:   ✅ Error rates over time   ✅ Latency distributions by service   ✅ Top error codes or patterns  💡 Tip: Use SPICE caching to accelerate dashboard performance for high-volume datasets. 🚀 Why This Stack Works ✅ Low-latency ingestion with Kinesis   ✅ Rich search and correlation with OpenSearch   ✅ Interactive visualization with QuickSight   ✅ Fully managed services — less operational burden 🔧 Common Use Cases 🔸 Real-time monitoring of microservices health   🔸 Automated anomaly detection and alerting   🔸 Centralized log aggregation for compliance   🔸 SLA tracking with drill-down capability 💡 Implementation Tips Define consistent index naming conventions for clarity (e.g., logs-application-yyyy-mm)   Attach resource-based policies to secure Kinesis and OpenSearch access   Automate index lifecycle management to control costs   Embed QuickSight dashboards into internal portals for live visibility Bottom line:   If you need scalable, real-time observability without stitching together a dozen tools, this AWS-native stack is one of the most effective solutions. #Observability #AWS #OpenSearch #Kinesis #QuickSight #RealTimeMonitoring #Infodataworx #DataEngineering #Logs #Metrics #Traces #CloudNative #DevOps #C2C #C2H #SiteReliability #DataPipelines

⚙️ 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

An AWS lab that excited me and my friend Ajay Sakthi Shankar Mathiyalagan the most, is all about how your click streams can be analyzed and visualized by businesses. I delved deep into the power of AWS Kinesis and OpenSearch to handle real-time big data challenges. Here's a snapshot of what I learned: Problem Statement This lab focused on utilizing AWS services to ingest, process, and visualize streaming data from web server logs, aiming to enhance decision-making and insights into user interactions and system performance. We began by setting up the  infrastructure: 1) Amazon EC2 instance to host our web server. Here we will be giving as many clicks as possible for the links on the website so that we can have ample data to analyze the streaming data. 2) Kinesis Data Streams + firehose + lambda - to capture live streaming data. These click streams are carried seamlessly through the firehose to the lambda, where we will be doing some lightweight transformations for our click stream access logs. (observation: When I was connecting the lambda function to the firehose, I configured, buffer size = 1MB(means, accumulate the stream data until it is 1MB), buffer_interval = 60sec (Should invoke the lambda for every 60sec, which means even if the data is less than 1MB, the lambda function will be invoked with whatever data is available) The lambda function takes in the access logs(logs created after our clicks on the website) 3) Amazon OpenSearch Service (formerly Elasticsearch): Indexed and stored transformed data, which we then visualized using OpenSearch Dashboards. OpenSearch stood out by offering powerful, real-time analytics capabilities. Here’s how : - Built a dynamic dashboard to visualize live data, such as user activities and system performance metrics. - Utilized OpenSearch’s robust indexing features to handle large volumes of data without compromising on performance. - Created various visualizations, including pie charts and heat maps, to uncover insights from the web server logs. - Used IAM and Cognito for authentication and authorization purposes. Learnings and Takeaways: The ability to analyze streaming data in real-time with AWS OpenSearch has transformed how organizations can visualize and react to data as it's being collected. This lab was a hands-on demonstration of setting up data streams and creating meaningful visualizations, providing a practical approach to solving real-world data challenges with AWS. This integration of AWS services laid a strong foundation for our group project where we are designing a data architecture for law enforcement from scratch, encompassing both stream and batch data pipelines. I'll share more about this project in my next post. On to the next one!