200 points by datawhiz 5 months ago flag hide 15 comments
architect_user 5 months ago next
This is a really interesting topic. The architecture for real-time data pipelines has always been a challenge.
dataengineer_john 5 months ago next
I completely agree! I've been working on a similar problem and it's not easy. What are your thoughts on using a stream processing approach vs traditional batch processing?
architect_user 5 months ago next
@dataengineer_john We've seen some success with stream processing. It's been able to reduce the latency in our real-time data analysis. However, it does come with some added complexity.
dataengineer_john 5 months ago prev next
@architect_user Thanks for the insight! Do you think stream processing is worth the complexity for most teams, or only for teams with specific use cases and resources?
machinelearning_mike 5 months ago prev next
We've been using a combination of real time and batch processing for our pipelines. It's been working great for us.
bigdatabob 5 months ago prev next
Stream processing has become more accessible with tools like Apache Kafka and Apache Flink. I think it's at least worth considering for most teams.
architect_user 5 months ago next
@bigdatabob I agree. The eco-system around stream processing has definitely improved and made it more accessible. Thanks for adding that!
scalable_sam 5 months ago prev next
We've been using Apache Beam to handle our real-time and batch processing. It allows us to easily switch between both and it's been a game changer.
realtime_richard 5 months ago prev next
I'm interested in how teams are handling disaster recovery and fault tolerance in real-time data pipelines.
infrastructure_ian 5 months ago next
We use Apache Kafka's built-in replication and have seen good results. We've also looked into using tools like DuckbillDB for real-time backups and redundancy.
systems_sally 5 months ago prev next
We use a combination of process checkpointing and data replication to ensure high availability in our real-time pipelines.
dataguard_dave 5 months ago prev next
Avoiding data loss and maintaining system availability are critical in real-time data pipelines. How have you seen teams addressing this?
scalable_sam 5 months ago next
At my previous job, we used an event sourcing approach to keep track of all the data changes and events in our application. It worked really well for us.
architect_user 5 months ago prev next
We've seen teams leveraging event sourcing and message queues as a way to ensure data durability and handle failures.
dataengineer_john 5 months ago prev next
I've also seen a lot of projects use message queues for fault tolerance. Apache Kafka is particularly popular for this use case.