Superstream.ai

Your Kafka client props isn't one-size-fits-all, but most teams treat it that way. We found that using the same settings everywhere leads to: - High data transfer costs (up to 97%) - Inefficient cluster utilization (small message size + small batch size) TBH, no easy solution here, but worth knowing. More to come in upcoming posts

🚀 How @expedia Group Optimized Costs & Performance 💡📊 The Problem: Expedia Group needed a real-time data analytics solution capable of handling 4,500 events per second while maintaining a latency under 15 seconds. Their existing approach was costly and inefficient, impacting their ability to scale analytics effectively. The Solution: Optics – a high-performance, real-time analytics system designed to process vast amounts of data quickly, efficiently, and cost-effectively. Optics was built with a lightweight architecture to reduce infrastructure overhead and ensure seamless scalability. How Optics Works: ✅ Decoupled Data Pipelines – Eliminates bottlenecks, allowing parallel processing for maximum efficiency. ✅ Optimized Query Engine – Reduces compute costs while ensuring real-time insights. ✅ Scalable & Cost-Effective – Adapts dynamically to traffic demands, keeping latency low and costs manageable. The Results: 🔹 40% Cost Reduction – Expedia significantly lowered infrastructure expenses while improving efficiency. 🔹 Sub-15 Second Latency – Real-time analytics became faster and more reliable. 🔹 Improved Scalability – Optics supports high throughput without compromising performance, making it future-proof for growing data demands. By implementing Optics, Expedia Group transformed its real-time analytics capabilities, reducing costs while maintaining high performance and unlocking the power of real-time decision-making. 🚀 💭 Could this approach help other businesses facing similar real-time data challenges? Let’s discuss! 👇 #RealTimeData #BigData #DataAnalytics #CloudComputing #ScalableSolutions #CostOptimization @expedia @shubham1689

✋ You are overspending by at least 43% on your Confluent Cloud(!!!) One of the reasons is that you have to increase CKUs just because of one spiky metric, and the worst of them all would be requests per second. You optimize everything, but one tiny metric spikes—and CKUs go up. Superstream is here to help! 🥁🥁🥁 When connecting a Confluent Cloud cluster to Superstream, you are getting full metric coverage to automatically optimize every bit of your cluster to reduce CKUs or at least ensure you are protected from exponential growth (efficiency). ✌️Reducing requests/s count Requests per second in Confluent Cloud measures how many API or client requests your Kafka cluster handles each second. This includes produce, fetch (consume), and metadata requests. Usually derived from many small messages or inefficient client behavior. 💪 Superstream will help to: 1️⃣ Gain critical visibility to understand what are the most impactful locations to fix 2️⃣ Understand how to fix them 3️⃣ Remediate Start saving today! ❤️

🚀 Revolutionizing AI Training: Smarter, Faster, and More Affordable! 🤖💡 Training large language models (LLMs) like ChatGPT or LLaMA to follow instructions well is a costly and complex process. Traditionally, this involves: ❌ Expensive Human Annotations – High-quality datasets require thousands of human-labeled examples, which are slow and costly to produce. 💰 ❌ Dependence on Proprietary AI Models – Many AI teams use GPT-4 to generate synthetic training data, but this creates licensing risks and high costs. 🔒 ❌ Forgetting – AI models often struggle to learn new tasks without forgetting old knowledge. 🧠💭 🔹 Introducing LAB (Large-scale Alignment for Chatbots), a breakthrough approach from MIT-IBM Watson AI Lab & IBM Research designed to make AI training more scalable, efficient, and cost-effective! How LAB Solves the Problem: ✅ Synthetic Data Generation, the Smart Way – LAB uses a taxonomy-driven approach to create highly diverse and high-quality instruction datasets—without expensive human labeling or reliance on proprietary AI. 🌍📚 ✅ Multi-Phase Tuning for Smarter AI – LAB prevents catastrophic forgetting by structuring training into phases, ensuring that new knowledge is added without erasing prior learning. 🏗️📈 ✅ Lower Costs, Higher Performance – Instead of using expensive GPT-4-generated data, LAB leverages the open-source Mixtral model to create training datasets at a fraction of the cost. 💡 The Impact 🌎🚀 🔹 With LAB, AI teams can train and align LLMs faster and cheaper, making powerful LLMs more accessible to companies and researchers. 🔹 LAB-aligned models have already shown state-of-the-art performance, competing with models trained using expensive human-labeled or GPT-4-generated data. 🔹 This approach democratizes AI, allowing more developers to fine-tune powerful models without breaking the bank. 💭 Could this be the key to scaling AI training efficiently while keeping costs low? Let’s discuss! 👇 #AI #MachineLearning #LLMs #AITraining #Chatbots #IBMResearch #OpenSourceAI #SyntheticData #AIInnovation

Did you know that you can reduce up to 97% of your Kafka traffic by simply matching each payload with its matching compression algorithm? Now, you're probably saying: "WHO HAS THE TIME TO DO THAT MATCHING????" We hear you. No one. That's why we added that to Superstream 😁 Superstream will automatically benchmark each topic and connected cluster to recommend the most efficient compression algorithm, ensuring maximum cost savings. Connect to Superstream and analyze your traffic for free ❤️ hubs.ly/Q03ctZyg0

🚀 Meta’s new AI tool is a piece of amazing news for software testing! 🔍🤖 Meta has recently launched Automated Compliance Hardening (ACH), an AI-powered tool that automatically finds and fixes software bugs before they cause problems. Unlike traditional testing, which checks for coverage on a predefined set of scenarios, ACH simulates real-world scenarios and generates tests to catch them! 💥 🔹 How it works: ✅ Engineers describe potential issues in plain text. ✅ ACH, powered by LLMs, generates realistic faults in the code. ✅ The AI then creates smart test cases to catch and prevent these issues in the future. 💡 Why it matters: 🔹 Saves developers time by automating test creation 🔹 Improves bug detection & software reliability 🔹 Already running across Facebook, Instagram, WhatsApp, and Messenger This could be a game-changer for software testing, making software more secure, efficient, and resilient. Meta is pushing the boundaries—will AI-powered testing become the new standard? Let’s discuss! 👇💬

Apache Iceberg vs. Delta Lake vs. Hudi key differences When to Choose ❄️ Iceberg: Cost Effectiveness. Best for large-scale analytics, multi-engine support, and simpler partition handling. 🌊 Delta Lake: Closed ecosystem. Great if you’re deeply tied to Spark or Databricks ecosystems. Hudi: Perfect for real-time data ingestion and upserts (e.g., CDC logs). Pro Tip ⚙️ Evaluate your current pipelines, query engines, and update patterns. Each format shines in different areas. Full comparison in the image below. Like the content? Follow us for more!

Apache GraphAr – Graph Databases, But Optimized Graph databases are awesome for relationships, but storing huge graphs can be slow and costly. 🤔 What Is GraphAr? - Columnar Storage for graph data. Similar to how Parquet stores tabular data. - Compression & Indexing for efficient queries on big graphs. - Interoperable with existing graph frameworks like Neo4j, ArangoDB, and GraphScope. 👉 Why It Matters - Performance: Columnar storage reduces random I/O, improving query times. - Scalability: Handle massive node and edge counts without blowing up memory. - Flexibility: Integrate with multiple graph processing engines and existing pipelines. - (And most importantly) Knowledge Graphs: Large-scale entity relationships with complex queries. In the context of AI, knowledge graphs serve as a foundational framework for storing and organizing complex information in a way that's both machine-readable and semantically rich. By integrating knowledge graphs, AI systems can tap into vast, interconnected knowledge domains, leading to more accurate insights and human-like understanding. Would you give GraphAr a try? hubs.li/Q03b0ZDl0 Follow us for more Data Engineering content!

Is Apache Iceberg also the future of Databases? If you haven’t completely understood the reason behind its creation, this might help - 🚀 Data lakes can get messy. Large files, complicated partition strategies, endless schema changes—it's a lot. Engineers need a table format that’s robust and built for modern data needs. What Is It? * High-performance for massive datasets. * It separates logical table operations from the physical data layout or computes them from the storage. * Designed to handle petabyte-scale data with minimal overhead. Key Features - Hidden Partitioning 🔍 – Iceberg handles partition pruning under the hood. No more manual partition logic. - Schema Evolution 🔄 – Add or drop columns without rewriting entire tables. - Time Travel ⏪ – Query older snapshots of data for debugging or historical analysis. - ACID Transactions ✅ – Ensures data consistency in distributed environments. - Multi-Engine Support 🔥 – Spark, Flink, Trino, and more. You pick the engine you love. Why Should You Care? Obvious reasons: - Speed: Faster queries thanks to advanced partition pruning and metadata. - Reliability: Ensures data integrity with atomic operations. - Flexibility: Adapt to changing data structures seamlessly. Less obvious reasons: No vendor locking! One engine might be great for one type of job, and the other better for a different one. That is the beauty of decoupling Like the content? Leave a like and follow us to learn more!

💡 Using Kafka by @Aiven? Want to slash your costs by up to 50%? 🚀 Let us introduce Superstream—the ultimate cost-saving solution for your Kafka clusters. Read more here: hubs.li/Q02Z1f1x0

Introducing the game-changing autoscaler for AWS MSK (Kafka) and Aiven Kafka, designed with a precise mission: revolutionizing the way you manage cluster sizing. Transform your cluster sizing from static to dynamic, and significantly reduce your compute costs by up to 50%!

🚀 Effortlessly Cut Your AWS MSK Costs by 50%! 🚀 We’re thrilled to announce the launch of our Auto Scaler for AWS MSK – a game-changer for anyone looking to optimize their MSK (Kafka) compute costs without compromising ANYTHING. hubs.ly/Q02WLZYm0

Wishing everyone a meaningful Sukkot filled with love and hope. 🌿🍋 May this holiday bring safety to our soldiers and the return of all 101 hostages still in captivity!🙏🎗️

Shanah Tovah! 🍎🍯 Here’s to a year filled with joy, peace, and togetherness. As we celebrate, we hold the 101 hostages in our thoughts and pray for their safe return to their families soon. Hag Sameach! 🎗️

Can your Kafka be cheaper, faster, & smarter, with no extra effort?🤔 We've cracked the code!💡 Visit booth #304 at #Current2024 to see how our unique platform reduces costs, boosts performance, & enhances reliability in minutes—without changing your setup. See you soon! 🙌

In the era of big data, Apache Kafka has emerged as a cornerstone of modern data streaming, but managing costs while maintaining its performance and reliability can be a complex challenge. Contributor @Yanivbh1, CEO of @SuperstreamAI, shares his recommendations. stackoverflow.blog/2024/09/04/bes…

We’re excited to be Silver Sponsors at #Current2024! Join us in Austin, Texas 🤠Sep 17-18 Stay tuned! 🎉

Based on a true story....

@Shay23Bra 😂😂

Genie: I'll give you a billion dollars if you can spend 100 million in a month. You only have to follow 3 conditions: no gifting, no gambling, and no throwing it away. CTO: Can I use any Kafka cloud provider? Genie: There are 4 conditions.

After working extensively with #Kafka I have identified three critical pitfalls that can cause a Kafka setup to fail if not addressed from the beginning. Trust me, you’ll want to read this ⤵️ P.S. Feel free to DM us with any questions 😎