> use Ignite to update the data in transactional manner and Spark for
> analytics.
Yes but the data would not be in sync when both(updates and analytics) are
done concurrently, right? I will have to discard the spark
rdd/dataset/dataframe every time the data is updated in ignite through the
Ignite API. As I understand the data remains in sync only when we use the
IgniteRDD api. Correct me if my understanding is wrong.
I have an additional question on the same topic - Even when ignite runs in
an embedded mode with spark, the memory footprint behavior is the same as it
is when ignite runs in standalone mode, right? i.e When spark fetches the
ignite cache through the IgniteRDD api (val igniteRDD =
igniteContext.fromCache("<cache-name>") a copy of data is created in the
spark worker's memory.
Thanks.
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