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The data is processed as it arrives and is also written to HDFS.

The key point here is that both the batch and real-time handlers are writing data to the same HDFS target system.

This is critical as it avoids having to wait around for a job to run to collect the source data from log files. Many real-time data streaming architectures make use of Kafka, which serves as a queue for events that need to be processed.

This is important as the rate at which events are generated may exceed at times the rate at which events can be processed.

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.

We started getting bug reports coming in around data being different in the batch powered reports vs the real-time powered reports.

When this happened we would have to start working our way backwards from the report back to the data source to figure out where the divergence was taking place.A deeper question that this begged was, in face of discrepancies, what is the system of record?One possible solution to the problems with the dual stream scenario described above is the so-called lambda architecture.So what we really did was add a second data integration pipeline to our environment.Several problems started to become apparent as we lived in this dual pipeline scenario.Unlike a batch system, this processing can happen as the events are coming in.

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