Live Event Streaming Setup

A Senior Data Engineer candidate was asked to design a real-time analytics pipeline during his interview at Netflix. Another candidate in a different loop at Uber got the same prompt. Real-time dashboards look simple until you add one layer of reality: – Add late arrivals? Now you need watermarks, session windows, and late-firing logic. – Add out-of-order events? Now event-time vs processing-time becomes your entire correctness model. – Add exactly-once semantics? Now idempotent sinks and transactional commits are non-negotiable. – Add backpressure? Now Kafka is lagging or your sink is choking and alerts are firing. – Add historical corrections? Now you're reconciling streaming state with batch recomputes. Here's my checklist of 15 things you must get right when building real-time analytics: 1. Start with your latency and correctness contract → Define what "real-time" actually means: sub-second? 5 minutes? End-to-end or just processing? And define correctness: approximate is fine, or must be exact? 2. Choose your processing model: Lambda vs Kappa → Lambda = separate batch + stream paths, eventually consistent. Kappa = stream-only, simpler but harder to backfill. Most companies say Kappa but run Lambda in disguise. 3. Pick your event-time strategy early → Use event timestamps, not processing timestamps. If events don't have timestamps, you're already behind. Decide: use producer time, log append time, or application time? 4. Design your windowing logic to match business semantics → Tumbling windows for fixed intervals. Hopping for overlapping aggregations. Session windows for user activity. Getting this wrong means your metrics lie. 5. Implement watermarking to handle late data → Watermark = "no events before this timestamp will arrive." But late data still arrives. Set your watermark delay based on observed lateness, not wishful thinking. 6. Build a late-firing strategy that doesn't break downstream → When late data arrives after the window closes, decide: update the past metric (retractions), append a correction, or drop it. Each has trade-offs for downstream consumers. 7. Handle out-of-order events with buffering and sorting → Events rarely arrive in order. Buffer and sort within your watermark delay. If you don't, your aggregations are wrong and nobody will notice until the CEO asks why revenue dropped. 8. Design for exactly-once semantics from source to sink → Kafka supports exactly-once within Kafka. Flink supports exactly-once with transactional sinks. But your sink (Postgres, Elasticsearch) must be idempotent or transactional too. 9. Make every sink operation idempotent → Assume every write happens twice. Use upsert patterns: INSERT ON CONFLICT, MERGE, or idempotency keys. Never use blind INSERT or INCREMENT operations. (Continued in comments)

Ever wonder why Netflix recommends shows instantly, but your monthly sales report takes hours? It's not magic—it's architecture. Choosing between batch, micro-batch, and streaming isn't just a tech decision. It's the difference between delivering insights tomorrow vs. stopping fraud right now. Here are the data processing paradigms that actually matter: 𝗕𝗔𝗧𝗖𝗛 𝗣𝗥𝗢𝗖𝗘𝗦𝗦𝗜𝗡𝗚 The overnight delivery truck—picks up everything at 5 PM, delivers by 8 AM. 𝘓𝘢𝘵𝘦𝘯𝘤𝘺: Hours to Days | Cost: Low | Accuracy: Highest Perfect for: → Month-end financial reports → Data warehouse loads → Compliance audits where "good enough by morning" works Tech: Spark, Hadoop MapReduce, dbt, SQL ETL If your CEO can wait until tomorrow, batch saves you money and headaches. 𝗠𝗜𝗖𝗥𝗢-𝗕𝗔𝗧𝗖𝗛 Amazon Prime delivery—small packages every few hours, not one giant shipment. 𝘓𝘢𝘵𝘦𝘯𝘤𝘺: Seconds to Minutes | Cost: Medium | Accuracy: High Perfect for: → Hourly sales dashboards → Marketing campaign tracking → Inventory updates that matter "soon, not instantly" Tech: Spark Streaming, Storm Trident, Databricks Delta Live Tables The sweet spot between "real-time" bragging rights and "I can actually afford this." 𝗡𝗘𝗔𝗥 𝗥𝗘𝗔𝗟-𝗧𝗜𝗠𝗘 Your smartwatch health alerts—not instant, but fast enough to matter. Latency: Sub-second to Minutes | Cost: Medium-High Perfect for: → Operational monitoring alerts → Business KPI notifications → "Something's wrong, fix it within the hour" scenarios Tech: Kafka + ksqlDB, AWS Kinesis, Azure Stream Analytics Real enough for business users, forgiving enough for engineers to sleep. 𝗦𝗧𝗥𝗘𝗔𝗠 𝗣𝗥𝗢𝗖𝗘𝗦𝗦𝗜𝗡𝗚 Think of it like Self-driving car sensors—react NOW or crash. Latency: Milliseconds | Cost: High | Accuracy: Good (eventually consistent) Perfect for: → Credit card fraud detection → Live gaming leaderboards → Dynamic pricing (surge fees, stock trading) Tech: Apache Flink, Kafka Streams, Spark Structured Streaming Expensive, complex, but worth it when milliseconds = millions saved. How to Actually Decide? Ask yourself 3 questions: 1️⃣ What breaks if data is 1 hour late? Nothing → Batch | UX suffers → Micro-batch | Money/lives at risk → Stream 2️⃣ What's your budget reality? Tight budget → Batch first | Enterprise scale → Hybrid approach (all three) 3️⃣ Can your team maintain it at 3 AM? Batch sleeps when you sleep | Streaming needs 24/7 on-call ready If you find this easy to understand, explore these projects to dive in: Batch Pipeline by Ansh Lamba - https://lnkd.in/dRh5cB6Y Micro-Batch Pipeline by DataGuy - https://lnkd.in/dXJTj7CU Streaming Pipeline by Yusuf Ganiyu - https://lnkd.in/deCzt_Ru Which architecture is running your most critical pipeline today? And more importantly—𝘪𝘴 𝘪𝘵 𝘵𝘩𝘦 𝘙𝘐𝘎𝘏𝘛 𝘰𝘯𝘦, 𝘰𝘳 𝘫𝘶𝘴𝘵 𝘵𝘩𝘦 𝘰𝘯𝘦 𝘺𝘰𝘶 𝘪𝘯𝘩𝘦𝘳𝘪𝘵𝘦𝘥? Drop your setup below. Let's compare notes. 👇

What Netflix Actually Taught Us About Live Streaming After the Tyson–Paul live event exposed some very public cracks, Netflix did something unusually useful: it published a five-part technical breakdown of how it built live streaming at scale. This article on the Streaming Learning Center summarizes the key lessons from each post and highlights what’s reusable at a scale well below Netflix's. Behind the Streams: Live at Netflix: How Netflix rebuilt its control plane to survive massive, synchronized play storms, handling millions of simultaneous session requests without cascading retries or metadata failures. Building a Reliable Cloud Live Streaming Pipeline: A detailed look at cloud-based ingest, redundancy, and encoding pipelines, and how Netflix replaced traditional broadcast infrastructure with automated cloud workflows. Real-Time Recommendations for Live Events: Why live events break traditional caching and recommendation systems, and how Netflix combined prefetching with broadcast triggers to update over 100 million devices without melting backend services. Netflix Live Origin: An inside look at the custom live origin layer that decouples publishing from read storms, isolates failures, and keeps latency predictable under extreme concurrency. Building a Robust Ads Event Processing Pipeline: How Netflix scaled ad telemetry, metadata, and billing signals for live and VOD without overwhelming devices or downstream systems. Even if your service volume never approaches Netflix traffic levels, the architectural patterns around surge control, observability, and failure isolation still apply.

Check out this video of the tool I created for my 𝗠𝗮𝘀𝘁𝗲𝗿’𝘀 𝗧𝗵𝗲𝘀𝗶𝘀—I tackled one of the oldest challenges in 3D modeling: 𝗿𝗲𝗹𝘆𝗶𝗻𝗴 𝗼𝗻 𝗮 𝟮𝗗 𝘀𝗰𝗿𝗲𝗲𝗻 𝘁𝗼 𝗰𝗿𝗲𝗮𝘁𝗲 𝗮 𝟯𝗗 𝗺𝗼𝗱𝗲𝗹. So, I built a tool that 𝘀𝘁𝗿𝗲𝗮𝗺𝘀 𝟯𝗗 𝗺𝗼𝗱𝗲𝗹𝘀 𝗳𝗿𝗼𝗺 𝗕𝗹𝗲𝗻𝗱𝗲𝗿 (𝗮𝗻𝗱 𝗼𝘁𝗵𝗲𝗿 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲) 𝗶𝗻𝘁𝗼 𝗠𝗶𝘅𝗲𝗱 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝘂𝘀𝗶𝗻𝗴 𝗮 𝗤𝘂𝗲𝘀𝘁 𝟯, allowing artists to interact with their work as if it were physically in front of them. 𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: ✔ 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝘀𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 of 3D models into Mixed Reality ✔ 𝗜𝗻𝘀𝘁𝗮𝗻𝘁 𝘂𝗽𝗱𝗮𝘁𝗲𝘀—changes made in Blender are reflected immediately ✔ 𝗛𝗮𝗻𝗱𝘀-𝗼𝗻 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻—scale, rotate, and manipulate individual pieces ✔ 𝗘𝘃𝗲𝗻 𝘀𝘂𝗽𝗽𝗼𝗿𝘁𝘀 𝗮𝗻𝗶𝗺𝗮𝘁𝗶𝗼𝗻𝘀 I used 𝗨𝗻𝗶𝘁𝘆’𝘀 𝗼𝗽𝗲𝗻-𝘀𝗼𝘂𝗿𝗰𝗲 𝗠𝗲𝘀𝗵𝗦𝘆𝗻𝗰 for the heavy lifting and adapted it into a Mixed Reality workflow. After doing a user study with 3D modeling experts, the feedback was 𝗼𝘃𝗲𝗿𝘄𝗵𝗲𝗹𝗺𝗶𝗻𝗴𝗹𝘆 𝗽𝗼𝘀𝗶𝘁𝗶𝘃𝗲. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: This approach is especially useful for: ▪️𝗦𝗰𝘂𝗹𝗽𝘁𝗶𝗻𝗴 ▪️ 𝗤𝘂𝗶𝗰𝗸𝗹𝘆 𝗿𝗲𝘃𝗶𝗲𝘄𝗶𝗻𝗴 𝗰𝗼𝗺𝗽𝗹𝗲𝘅 𝗺𝗼𝗱𝗲𝗹𝘀 ▪️ 𝗥𝗮𝗽𝗶𝗱𝗹𝘆 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝗽𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲𝘀 ▪️ 𝗦𝗵𝗼𝘄𝗶𝗻𝗴 𝗺𝗼𝗱𝗲𝗹𝘀 𝘁𝗼 𝗼𝘁𝗵𝗲𝗿𝘀 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗲𝘅𝘁𝗿𝗮 𝘀𝗲𝘁𝘂𝗽 The idea isn’t new, but 𝘁𝗵𝗲𝗿𝗲’𝘀 𝘀𝘁𝗶𝗹𝗹 𝗮 𝗹𝗮𝗰𝗸 𝗼𝗳 𝗽𝗹𝘂𝗴-𝗮𝗻𝗱-𝗽𝗹𝗮𝘆 𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝘀 that seamlessly integrate into existing 3D modeling workflows. That’s what I aimed to change. 𝗡𝗲𝘅𝘁 𝗦𝘁𝗲𝗽𝘀: I’m planning to release this tool in the future—stay tuned!

Recently, our AI voice bot was giving 2–3 seconds latency locally, perfectly fine. but in production? 5–6 seconds!! For something that’s supposed to feel “real-time,” that’s a disaster. My first instinct: it’s a deployment issue. But the problem was to find where is the issue as the deployment stack had too many moving parts to just “guess”: - The agent responsiveness and how quickly it returns audio - The telephony layer (Twilio) — maybe there was some lag or processing overhead there - The EC2 instance size and networking capacity - The reverse proxy (nginx) behavior in front of the app Here’s how it went down: Step 1: Agent tuning I started by tweaking the agent’s internal responsiveness, thinking maybe it was buffering too much before streaming back. Tested multiple configs, no noticeable difference. Step 2: Telephony tweaks Jumped into Twilio settings. Checked if media streaming or jitter buffers were adding delay. Reduced buffer sizes, changed audio settings, still stuck at 5–6 seconds. Step 3: Server horsepower Maybe the EC2 was choking. Upgraded from a smaller t3 to a larger t3's, then to larger c5 for better network performance. CPU and memory were healthy, but… latency didn’t reduced. Step 4: The reverse proxy suspicion I had nginx running as a reverse proxy, forwarding all HTTP and WebSocket traffic from 80/443 to a custom port for the bot. AI suggested the issue might be nginx’s caching and buffering behavior, which could add milliseconds that, in real-time streaming, feel huge. Step 5: The real fix Swapped nginx entirely with an AWS ALB (Application Load Balancer), keeping the same SSL termination and routing rules. Immediately saw latency drop to 1–2 seconds consistently. The reason was that nginx was caching data which prevented real time interaction and causing larger delays. Key learnings: - Throwing more compute at the problem doesn’t guarantee speed, you have to hunt for bottlenecks. - AI is fantastic for hinting at possible angles, but you need your own architectural intuition to validate and act. - In complex systems, the real enemy is often a small, overlooked layer, not the obvious big pieces.

I started a live stream and the comments I got cannot be shared here. What can be shared is, how live stream comments work ? 💬 1. Comment Ingestion - POST /comment API is called. - Validates and authenticates the user. - Optionally passes the comment to an AI/moderation service (like AWS Comprehend or Perspective API). - Pushes comment into a Kafka topic (or Redis Stream). 🔁 2. Real-time Fan-out - Use WebSockets or Server-Sent Events (SSE) to push comments in real-time. - Each live stream has its channel/room ID. - WebSocket servers consume from Kafka and broadcast to subscribed clients. If a WebSocket server goes down, any active socket connections it managed are immediately lost. Clients must implement auto-reconnect logic with exponential backoff. - Scaling: Use sharding by stream ID and partitioned Kafka topics to scale horizontally. 🏪 3. Data Storage - Hot Path: Use Redis to store latest N comments per stream (fast access). - Cold Path: Persist all comments in Cassandra, DynamoDB, or PostgreSQL (partitioned by stream_id). 🧠 4. Moderation Service (Optional) - Can be synchronous (pre-display filtering) or asynchronous (post-display moderation). - Use ML/NLP to classify comments for hate speech, spam, etc. - Optionally allow community reporting or human moderators. 🔁 5. Replay Comments - Store timestamped comments. - During VOD replay, fetch comments and display based on timestamps (like YouTube's live chat replay). 📈 Scaling Strategy - Kafka for decoupling ingestion and delivery. - Sharded Redis for fast comment retrieval. - Horizontal scaling of WebSocket servers. - Use CDNs and edge caching for static stream content, but not comments. PS: The comments can't be shared because I forgot to copy them

How AI Chat Apps Show Responses Before the API Call Finishes Ever wondered AI tools show you responses word-by-word in real-time? Here's the technical breakdown: 𝗧𝗛𝗘 𝗢𝗟𝗗 𝗪𝗔𝗬 (𝗧𝗿𝗮𝗱𝗶𝘁𝗶𝗼𝗻𝗮𝗹 𝗥𝗘𝗦𝗧 𝗔𝗣𝗜) Your app sends a request, waits, receives complete response, then displays everything at once. Problem: For a 5-second AI response, users stare at a blank screen for 5 seconds. Bad UX. 𝗧𝗛𝗘 𝗠𝗢𝗗𝗘𝗥𝗡 𝗪𝗔𝗬 (𝗦𝗲𝗿𝘃𝗲𝗿-𝗦𝗲𝗻𝘁 𝗘𝘃𝗲𝗻𝘁𝘀 - 𝗦𝗦𝗘) The connection stays OPEN and the server sends events as they happen: → At 0ms: Connection opens → At 100ms: Server sends event → UI turns blue (thinking) → At 2000ms: Server sends event → UI turns green (processing) → At 2500ms: Server sends event → UI turns pink (responding) → At 3000-5000ms: Server sends text chunks → Text appears word by word → At 5000ms: Connection closes HOW IT WORKS TECHNICALLY: 𝗙𝗿𝗼𝗻𝘁𝗲𝗻𝗱 𝘂𝘀𝗲𝘀 𝗘𝘃𝗲𝗻𝘁𝗦𝗼𝘂𝗿𝗰𝗲 𝗔𝗣𝗜 𝘁𝗼 𝗹𝗶𝘀𝘁𝗲𝗻 𝗳𝗼𝗿 𝘀𝗲𝗿𝘃𝗲𝗿 𝗲𝘃𝗲𝗻𝘁𝘀. When an event arrives with type "𝗧𝗢𝗢𝗟_𝗖𝗔𝗟𝗟_𝗦𝗧𝗔𝗥𝗧", the UI immediately changes to blue. When type is "TEXT_MESSAGE_CONTENT", each text chunk gets appended to the screen in real-time. Backend sets special headers like "Content-Type: text/event-stream" and "Connection: keep-alive". Then it sends data prefixed with "data:" as JSON objects containing the event type and content. WHY THIS MATTERS Better UX: Users see progress immediately instead of waiting Perceived Performance: App feels 10x faster even if actual processing time is the same 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸: - Streaming lets users see partial tokens as the AI generates them, so they know the system is working instead of being stuck. 𝗖𝗮𝗻𝗰𝗲𝗹𝗹𝗮𝘁𝗶𝗼𝗻: - You can cancel long-running responses using AbortController, which stops the stream instantly. 𝗦𝗲𝗿𝘃𝗲𝗿-𝗦𝗲𝗻𝘁 𝗘𝘃𝗲𝗻𝘁𝘀 (𝗦𝗦𝗘): One-way communication (server to client only) Uses regular HTTP protocol Automatic reconnection built-in Perfect for streaming AI responses 𝗪𝗲𝗯𝗦𝗼𝗰𝗸𝗲𝘁𝘀: Two-way communication (server and client) Separate protocol requiring upgrade Manual reconnection logic needed Better for real-time chat between multiple users 𝗧𝗛𝗘 𝗠𝗔𝗚𝗜𝗖 𝗛𝗘𝗔𝗗𝗘𝗥 1] "Content-Type: text/event-stream" → correct for SSE streaming. 2] "Content-Type: multipart/mixed" → correct for OpenAI-style multipart streaming, not general SSE. These headers tell the client to keep the 𝗰𝗼𝗻𝗻𝗲𝗰𝘁𝗶𝗼𝗻 𝗼𝗽𝗲𝗻 and 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗱𝗮𝘁𝗮 as it arrives, instead of waiting for the full response. 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 𝗶𝘀𝗻'𝘁 𝗮𝗯𝗼𝘂𝘁 𝗺𝗮𝗸𝗶𝗻𝗴 𝘁𝗵𝗶𝗻𝗴𝘀 𝗳𝗮𝘀𝘁𝗲𝗿 𝗶𝘁'𝘀 𝗮𝗯𝗼𝘂𝘁 𝗺𝗮𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝘄𝗮𝗶𝘁 𝗳𝗲𝗲𝗹 𝗯𝗲𝘁𝘁𝗲𝗿. The API call takes the same time, but users perceive it as instant because they see progress immediately. If you're building AI-powered apps, implementing SSE streaming is the difference between an app that feels sluggish and one that feels magical.

Jake Paul may have beaten Mike Tyson but the real challenge IMO was for Netflix who bumped into envoy overloaded error leading upto the live stream of Paul Tyson bout. Platforms like Disney+ Hotstar and JioCinema have set benchmarks in handling massive live audiences—like Hotstar’s record-breaking 25 million concurrent viewers during the Cricket World Cup. What are they doing right, and how can Netflix (or any streaming giant) learn from them? Key Lessons for Reliable Live Streaming: 1. Scalable Infrastructure: Build systems that scale gracefully with demand. Hotstar’s infrastructure has proven capable of managing the world’s largest audiences. 2. CDN Optimization: Efficient content delivery networks reduce latency by strategically placing servers closer to viewers. 3. Adaptive Bitrate Streaming: Dynamically adjust video quality to match users’ internet speeds, ensuring uninterrupted playback. 4. Load Balancing: Distribute traffic across multiple servers to avoid bottlenecks and ensure stability during peak usage. 5. Pre-Event Simulations: Rigorous testing before high-profile events can uncover potential issues, enabling teams to fine-tune their systems. 6. User Experience Enhancements: Features like language options, real-time stats, and interactive elements go beyond streaming, engaging users and retaining their loyalty. If Netflix integrates these strategies, it could pave the way for delivering not just live events but also unforgettable experiences. I am sure they have taken these and more already in consideration. As we push the boundaries of what’s possible with live streaming, collaboration and learning from leaders in the space will be essential. What’s your take on the future of live sports streaming? How can streaming platforms better prepare for such high-pressure scenarios? Let’s discuss! #softwaretesting #softwareengineering #livestreaming #brijeshsays

Excited to share our latest research from MIT and NVIDIA: StreamingVLM: Real-Time Understanding for Infinite Video Streams! A critical challenge for Vision-Language Models (VLMs) is understanding near-infinite video streams without escalating latency and memory usage. Current approaches fall short: 1. Full Attention leads to quadratic computational costs and fails on long videos. 2. Sliding Window methods: either break coherence by resetting context or suffer from high latency due to redundant recomputation. This makes real-time applications like autonomous agents and live assistants impractical. To solve this, we introduce StreamingVLM, a unified framework that aligns training with streaming inference. Our key innovations are: 1. A Streaming-Aware KV Cache: During inference, we maintain a compact cache by reusing the states of attention sinks, a short window of recent vision tokens, and a long window of recent text tokens. This preserves long-term memory while minimizing computational overhead. 2. An Aligned Training Strategy: We instill this streaming ability via a simple supervised fine-tuning (SFT) strategy that applies full attention on short, overlapped video chunks. This effectively mimics the inference-time attention pattern without training on prohibitively long contexts. To validate our approach, we built Inf-Streams-Eval, a new benchmark with videos averaging over two hours that requires dense, per-second alignment. The results are compelling: 1. On Inf-Streams-Eval, StreamingVLM achieves a 66.18% win rate against GPT-40 mini. 2. It maintains stable, real-time performance at up to 8 FPS on a single NVIDIA H100. 3. Notably, our SFT strategy also enhances general VQA abilities without VQA-specific fine-tuning, improving performance on LongVideoBench by +4.30 and OVOBench Realtime by +5.96. We believe StreamingVLM is a significant step towards deploying VLMs in real-world settings that require continuous, real-time perception. We welcome you to read the paper, explore the code, and try our interactive demo! Paper: https://lnkd.in/esnDcK6B Code: https://lnkd.in/eP4C-epi Interactive Demo: https://lnkd.in/eG4ekrep Video Demo: https://lnkd.in/e5CnnJEk #AI #MachineLearning #DeepLearning #ComputerVision #VLM #RealTime #MIT #NVIDIA #StreamingVLM