250 points by datawhiz 5 months ago flag hide 12 comments
datascientist 5 months ago next
Fascinating! I've been working with real-time data for years and couldn't agree more that our current approaches are inefficient. This revolutionary method is a game-changer. Thanks for sharing!
ncurious 5 months ago next
@data scientist Glad you like it. Our team faced similar struggles and we're thrilled to share our progress so far. Hoping this encourages discussion and improvement in real-time data processing!
fspreadeagle 5 months ago next
@NCurious I've been working with real-time data analysis tools for months and would like to suggest adding some visualization components. What do you think about integration with a library like D3.js?
ncurious 5 months ago next
@FSpreadeagle I appreciate the comment. This is an interesting idea and we've actually been playing around with something similar using Vegas library for Scala. It is not as mature as D3.js but very promising. We'll post an update!
datavore 5 months ago prev next
@NCurious I echo @FSpreadeagle's suggestion. Our team is very interested in real-time visualization and we believe this aspect will add great value to your work. Thanks!
codelion 5 months ago prev next
I've only skimmed the article, but I'm curious to know if this approach can deal with situations involving tens of thousands of data points per minute. Any insight?
hpcprogrammer 5 months ago next
@CodeLion Yes, the approach uses efficient stream-processing algorithms. Although, performance will depend on the specific big data framework you choose to implement it on. I've personally tested with Apache Flink and had success.
optimize 5 months ago prev next
@CodeLion Absolutely! Stream processing is essential to handle large volumes of data efficiently. With the appropriate configuration, you can easily handle tens of thousands of data points per minute.
parallelpro 5 months ago prev next
I'm working on a similar problem - real-time IoT data processing. I think this approach provides a robust solution for storing streaming data. I would love to know your thoughts about dealing with message loss and late arrival of data within the proposed method.
realstream 5 months ago next
@ParallelPro Message loss can be tough to eliminate completely, but using idempotence at the consumer level can help ensure data consistency. In our approach, we used the Kafka message store and followed the at-least-once design. Late arrivals may be solved by using the same system and implementing a watermark strategy.
bitquest 5 months ago prev next
The performance analysis and experimental results show significant speed-ups compared to existing methods. I'm looking forward to trying this with my data setup. Thanks for sharing your work!
scalawiz 5 months ago next
@BitQuest Thank you! We are delighted to hear you liked it. Commenting on performance, recent studies, including ours, show the impact of exploring better serialization, and more cache-friendly data structures in real-time data processing. Let us know your conclusions.