Yaniv Ben Hemo, Founder/CEO at Memphis Dev, discusses Memphis Cloud, an alternative architecture for delivering streaming data. He explains why a streaming alternative like Memphis is needed, the differences between a broker and a streaming stack, and the challenges with existing tools like Kafka. The podcast also explores the shift from batch processing to real-time streaming in data processing.
36:53
forum Ask episode
web_stories AI Snips
view_agenda Chapters
auto_awesome Transcript
info_circle Episode notes
question_answer ANECDOTE
Founder Built Memphis After Fighting Streaming Ops
Yaniv built large-scale data platforms and found himself spending more time on infra tweaks than delivering value.
That frustration led him to create Memphis to let developers focus on extracting value instead of fighting streaming infrastructure.
insights INSIGHT
Kafka Works But Imposes Heavy Operational Costs
Kafka is well architected but creates high operational and learning overhead for modern teams.
Yaniv framed the problem as day-one complexity, day-two ops, and a persona gap between platform engineers and data consumers.
insights INSIGHT
Three Core Layers Of A Streaming Stack
A streaming stack has three core layers: broker, schema/transform, and stream processing.
Memphis maps these explicitly (broker, schemovers for schema, and processing) to simplify end-to-end pipelines.
Get the Snipd Podcast app to discover more snips from this episode
Yaniv Ben Hemo (@yanivbh1, Founder/CEO at @memphis_Dev) talks about Memphis Cloud, an alternative architecture to delivering streaming data for applications.
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.app
Topic 1 - Welcome to the show. Tell us a little bit about your background, and what brought you to create Memphis.Dev
Topic 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?
The Reasoning Show