Mindy Ferguson’s Post

📰🚨Introducing Express brokers for Amazon MSK to deliver high throughput and faster scaling for your Kafka clusters by Channy Yun (윤석찬) 📈 AWS introduces Express brokers for Amazon MSK! Experience up to 3x throughput per broker, 20x faster scaling, and 90% quicker recovery. Simplifying Kafka management while enhancing resilience makes it a game-changer for your streaming apps! #AWS #Kafka #MSK 🚀 #AWSNews #AWS

Check out my first ever blog post! #redis #kafka

If you're interested in using Amazon Managed Streaming for Apache Kafka (Amazon MSK) for streaming data, you might want to know that we've just published a comprehensive tutorial on connecting Kafka to Kouncil 1.7 via Amazon MSK! We go step-by-step through the process of creating a Kafka cluster and present not one but two ways to hook up Kouncil 1.7 to a Kafka cluster set up on Amazon MSK. Check out our article and see how easy it is to connect these powerful tools! 🔗 https://lnkd.in/dbx7Qeu7 #AmazonMSK #Kouncil #DataStreaming #AWS #Kafka

Amazon MSK Replicator now supports a new configuration that enables you to preserve original Kafka topic names while replicating streaming data across Amazon Managed Streaming for Apache Kafka (Amazon MSK) clusters. Amazon MSK Replicator is a feature of Amazon MSK that lets you reliably replicate data across MSK clusters in the same or different AWS region(s) with just a few clicks.

🚀 Unlocking the Power of Redis: A Game-Changer for Data Management 🚀 In today’s fast-paced digital world, efficient data management is crucial for delivering high-performance applications. That’s where Redis comes in! 🌟 Redis, an open-source, in-memory data structure store, is renowned for its speed and versatility. Whether you’re dealing with caching, real-time analytics, or message brokering, Redis has got you covered. Here are a few reasons why Redis should be on your radar: 🔵 Blazing Fast Performance: Redis operates in-memory, which means data access is lightning-fast. This makes it ideal for applications requiring quick response times. 🔵 Versatile Data Structures: From strings and hashes to lists and sets, Redis supports a variety of data structures, allowing you to choose the best fit for your use case. 🔵 Scalability: Redis can handle millions of requests per second with ease, making it perfect for scaling applications. 🔵 Rich Feature Set: With features like persistence, replication, and Lua scripting, Redis offers robust tools to enhance your application’s capabilities. Curious to learn more? Dive into the world of Redis and see how it can transform your data management strategy! 🌐🔍 https://lnkd.in/dqzvfVnF #Redis #DataManagement #TechInnovation #HighPerformance #Scalability

https://lnkd.in/dHTYqJ6T [ main site: https://lnkd.in/d3mcNX-C ] << ...WarpStream is an Apache Kafka® compatible data streaming platform built directly on top of object storage: no inter-AZ bandwidth costs, no disks to manage, and infinitely scalable, all within your VPC... >>

Kafka has been there for quite some time. Everybody and their uncle uses it for data streaming and processing. You know it can perform, and we know it. But how do you get the most bang for your buck out of it? We rigorously tested Kafka performance, comparing different environments to find the most cost-effective setup in AWS or GCP. We ran dozens of tests to find which compute generation, CPU, and JVM would yield the best price-performance ratio for Kafka. Specifically, we measured how many millions of rows we can ingest into the Kafka broker per one cent. Read more in our blog (spoiler: ARM rocks): https://lnkd.in/eJ7aRpbw

Really excited to be working with my friend and fellow committer Jordan West on a performance patch for Apache Cassandra that's already showing a MASSIVE 6x reduction in IOPS usage during compaction which makes a huge impact on EBS. In our initial testing we saw a 3x improvement to compaction throughput, just on one thread, and multiple threads will deliver an even bigger improvement. The same patch will reduce system overhead on *any* deployment, but will have the biggest impact when using disks that use quotas on IOPs. The impact is you'll be able to run WAY denser nodes on EBS for the same cost, more predictable random read performance, and we're not even finished. This same patch should deliver improvements to range scans (hello spark jobs!) and repairs. You can follow along here: https://lnkd.in/d5_hcyJQ

You can now upgrade your Amazon Managed Streaming for Apache Kafka (Amazon MSK) provisioned clusters running on X-86 based M5 or T3 instances and replace them with AWS Graviton3-based M7G instances with a single click of a button. In-place upgrades allows you to seamlessly switch over your existing provisioned clusters to M7G instance type for better price performance, while continuing to serve reads and writes for your connecting client applications. https://lnkd.in/gjCTWNTF

Introducing Express Brokers for Amazon MSK. 🚅 Want to scale your Kafka clusters faster and achieve higher throughput? Express Brokers for Amazon MSK is now available! 🙌 Key benefits include: - Faster scaling from 10s to 100s brokers. ⏩ - Higher throughput with upto 10X more connections. 📈 - Optimized for event streaming apps.   - No code changes required. ⚡️ Express Brokers use a proxyless architecture optimized for performance. They simplify operations with auto-scaling and auto-healing. Ready to take your Kafka streaming to the next level? Learn more about Express Brokers below! https://lnkd.in/eJp4ynHG #aws #amazon #dataanalytics #datascience #realtimedata #streamingdata #awsgraviton #amazonsilicon #arm #aarch64 #amazonmsk #kafka #realtimedata #streamingdata