Hi,
as far as I understand, given my limited experience with streaming I may be
wrong, DStreams are row based data and in case we want to transform them
to columnar based data storage then there is a computation overhead. That
may be one of the reasons why its better to avoid.
On other hand, I
Hi,
I started my Spark Streaming journey from Structured Streaming using Spark
2.3, where I can easily do Spark SQL transformations on streaming data.
But, I want to know, how can I do columnar transformation (like, running
aggregation or casting, et al) using the prior utility of DStreams? Is
