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

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 Yaniv Ben Hemo, CEO of Superstream.ai, shares his recommendations.

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! 🙌

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! 🎗️

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!🙏🎗️

🚀 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

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%!

💡 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

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

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

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

🚀 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

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 😁

🚀 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

✋ 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.

🚀 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

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