I love this video. It's informative and really well made. I also love to read the comments on these videos as, no matter how good your architecture is, there is always something to improve. Looking forward to see an update on this (especially regarding the data extraction process). But once again. This is awesome content. Keep going :) :)
I generally believe this should be lakehouse design instead of warehouse also the database replication into data lake is classified under change data capture (cdc). If you push your data both into queue and database it’s hard to ensure consistency between the datalake and the database ie cases like rollback or database failure. Typically the state of the art solution for this type of problem is to rely on the database journal for instance through the binlog or WAL
Thanks for sharing. But there's a thing that i do not understand, what is the point of using bus system like kafka or kinesis if the main goal is to process data in scheduled intervals? Would it to be more cost efficient to just use old school batch processing pipeline?
Sometimes yes, sometimes no. If we have the ability to process streaming data, this results in less overall data being processed and more up to date results. Sometimes this isn’t possible though, and we have to process the data in batch.
Usually we’d dump things into Kafka at the same time they’re being dumped into the database. But this is highly dependent on the system-some databases support this natively, sometimes it’s easier just to use a batch job.
@@interviewpen I think it is more of a watered down average Joe explanations rather than higher level. Which is ok but should be reflected in the title. "Data warehousing concepts for the average Joe" would be a better title for a generic video whose audience are accountants are libertarians ... If you call it "Design a data warehouse" like you did, professionals will think that you will provide what you said you would ... Which did not materialize ...
spark is not a tool for streaming data. Using Flink enables you to do real-time data ingestion along with the transformations. It is mostly used in fraud and anomaly detection usecases.
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The timing could not be better. I'm about to take on a new role literally about the contents of this video so thank you so much for making this!
Glad you liked it!
Years of experience all packed in to 14 min, Thank you Sir!
As with all your videos, great work
Thanks for watching!
I love this video. It's informative and really well made. I also love to read the comments on these videos as, no matter how good your architecture is, there is always something to improve. Looking forward to see an update on this (especially regarding the data extraction process). But once again. This is awesome content. Keep going :) :)
Thanks for watching!
I generally believe this should be lakehouse design instead of warehouse also the database replication into data lake is classified under change data capture (cdc). If you push your data both into queue and database it’s hard to ensure consistency between the datalake and the database ie cases like rollback or database failure. Typically the state of the art solution for this type of problem is to rely on the database journal for instance through the binlog or WAL
Cool thanks
It's indeed a lakehouse design
Thanks for sharing.
But there's a thing that i do not understand, what is the point of using bus system like kafka or kinesis if the main goal is to process data in scheduled intervals?
Would it to be more cost efficient to just use old school batch processing pipeline?
Sometimes yes, sometimes no. If we have the ability to process streaming data, this results in less overall data being processed and more up to date results. Sometimes this isn’t possible though, and we have to process the data in batch.
Hi I love your videos ❤
Thanks!
Which tech is used or concept to put DB changes to the queue ? Does PG native support this? It's also scary since they can't be desynchronized
Usually we’d dump things into Kafka at the same time they’re being dumped into the database. But this is highly dependent on the system-some databases support this natively, sometimes it’s easier just to use a batch job.
Does consumers of Kafka also generally horizontally scale? Or are the consumers always unique (different from each other, rather than being replicas)
Yes, the consumers are stateless so scaling them just means replicating it.
I'm wondering which app were you using for demo
We use GoodNotes on an iPad
In today's lesson we explain motor vehicles ... We will go over everything ... But the engine ....
DW means show OLTP Schema design vs OLAP
Our youtube videos are usually higher level-If you’re looking for more in-depth content we have plenty on interviewpen.com :)
@@interviewpen
I think it is more of a watered down average Joe explanations rather than higher level. Which is ok but should be reflected in the title.
"Data warehousing concepts for the average Joe" would be a better title for a generic video whose audience are accountants are libertarians ...
If you call it "Design a data warehouse" like you did, professionals will think that you will provide what you said you would ... Which did not materialize ...
I'm still quite confused on why we prefer queue + Flink (stream processing) instead of just ingesting data directly from sources using spark.
spark is not a tool for streaming data. Using Flink enables you to do real-time data ingestion along with the transformations. It is mostly used in fraud and anomaly detection usecases.
I didn't learn how to design a data warehouse from this video. Misleading title. Bad.
Surprised that dbt was not mentioned since we're talking about a modern approach👀. Appreciated the video otherwise
Yep, dbt can certainly be used as an alternative to the solutions mentioned. Thanks!
Why not Use CDC?