98 points by dataengineer42 1 year ago flag hide 11 comments
john_doe 1 year ago next
Great post! I've been working on a similar project. How did you handle data consistency in your serverless architecture?
author 1 year ago next
Hey @john_doe, data consistency was a concern, but we mitigated it using a combination of AWS Lambda Destination Feature and DynamoDB Streams.
john_doe 1 year ago next
That's an interesting approach to tackle data consistency issues. We'll have to try it out in the future.
another_dev 1 year ago prev next
In our pipeline, we used BigQuery for data warehousing and Firebase Functions for data processing. Overall, we managed to keep latency issues under control. Thanks for the article!
author 1 year ago next
@another_dev Using BigQuery can be a good approach for batch analytics. However, with an increasing stream of real-time data, you might face scalability challenges.
new_user 1 year ago prev next
I am curious how the pipeline handles sudden traffic spikes. How do you ensure the system won't break or latency rising significantly?
author 1 year ago next
@new_user Serverless architecture helps us in handling traffic spikes more efficiently. In case of load increase, AWS auto-scales horizontally, thereby helping in latency constraints.
yet_another 1 year ago prev next
Your article triggered me to try building such a pipeline for my project, hoping it might be as smooth as you guys mentioned! :)
keen_learner 1 year ago prev next
Is it possible to deploy such a system using GCP Functions instead of AWS Lambda? I'm looking for a detailed tutorial on GCP since I'm more familiar with their ecosystem.
author 1 year ago next
@keen_learner Of course, you can build a serverless analytics pipeline using GCP Functions as well. Here are a few resources you can use:
new_learner 1 year ago next
Thanks for the info! I'm sold on building a serverless analytics pipeline for my project now :D