Streaming alternatives to Kafka

Yaniv Ben Hemo (@yanivbh1, Founder/CEO at @memphis_Dev) talks about Memphis Cloud, an alternative architecture to delivering streaming data for applications.  SHOW: 747CLOUD NEWS OF THE WEEK - http://bit.ly/cloudcast-cnotwNEW TO CLOUD? CHECK OUT - "CLOUDCAST BASICS"SHOW SPONSORS:Reduce the complexities of protecting your workloads and applications in a multi-cloud environment. Panoptica provides comprehensive cloud workload protection integrated with API security to protect the entire application lifecycle.  Learn more about Panoptica at panoptica.appCloudZero – Cloud Cost Visibility and Savings​​CloudZero provides immediate and ongoing savings with 100% visibility into your total cloud spendAWS Insiders is an edgy, entertaining podcast about the services and future of cloud computing at AWS. Listen to AWS Insiders in your favorite podcast player. Cloudfix HomepageSHOW NOTES:Memphis.dev (homepage)Getting Started with Memphis (docs page)Apache Kafka vs. MemphisMemphis on GitHubTopic 1 - Welcome to the show. Tell us a little bit about your background, and what brought you to create Memphis.DevTopic 2 - Let’s start at the beginning. Most folks will want to know why a streaming alternative. Isn’t Kafka good enough? What challenges did you personally encounter?Topic 3 - In reviewing the architecture, it mentions differences between a broker and a streaming stack. Can you elaborate on what that means? What components are typically needed for a proper data streaming solution?Topic 4 - One of the common issues with Kafka I hear about is operations complexity over time. It isn’t uncommon that the more a system scales, the more complex it is to operate and also maybe the harder it is to get insights and mine for key data for instance. Have you seen this in your experience?Topic 5 - Let’s talk use cases. How do you envision organizations using Memphis Cloud? What problems are you trying to solve in the market? Is Memphis Cloud a SaaS offering? How would it be implemented in an organization?Topic 6 - The data management side of all of this to always be problematic. Where and how is the data managed? What does the lifecycle of the data look like and what design considerations went into this aspect?Topic 7 - When building large distributed streaming systems, I’m sure there are trade offs and optimizations of features to consider. What are you optimizing for and what are the design tradeoffs developers need to consider?FEEDBACK?Email: show at the cloudcast dot netTwitter: @thecloudcastnetFEEDBACK?Email: show @ the enterprise ai show dot comeBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow