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There was no obvious way to share the logic as one system was very SQL centric, and the other had logic implemented in java.
On top of this our end users started pointing out other problems.
Unlike a batch system, this processing can happen as the events are coming in.
The logic that in a batch-oriented system is in the ETL layer and is implemented within Storm in this scenario.
A first practical problem is that data transformation and load logic needs to be duplicated in both the ETL branch and the real-time branch.
So when we wanted to add a reporting feature, we would need to add logic to both the ETL and real-time sides.
It’s worth noting that while we achieved our real-time reporting objective, we didn’t replace the existing batch oriented system.
That system was still powering the majority of our reporting and invoicing functions.
The key point here is that both the batch and real-time handlers are writing data to the same HDFS target system.Over the last two days, I’ve highlighted how the Fuze backend data platform has evolved to meet growing customer needs.From communication data collection to business intelligence, we needed to address our system architecture to keep things running smoothly.In the diagram above, generated events are sent to queues within Kafka on the left hand side.Then on the right side, Storm takes events from those queues and processes them.