Apache Spark Streaming vs. Google Cloud Dataflow

Apache Spark Streaming is a tool that we are using for almost a year and is excellent in managing batch processing. It is user-friendly. Using it, we can even process our massive data in fractions of seconds. Its pricing is its other plus point. Only its In-memory processing is its demerit as it occupies a large memory.

Incentivized

Based on my experience, streaming / real time / machine learning / AI type of processing and batch processing which needs less transformation are very well suited. Work load that needs complex transformation / multiple hops gets very complicated to implement. New feature like Dataflow SQL option will come in handy for sql heavy users.

• It is amazing in solving complicated transformative logic.
• It is straightforward to program.
• It is a very quick tool.
• It processes large data within a fraction of seconds.

Incentivized

• Streaming, Real time work load
• Batch processing
• Auto scaling
• flexible pricing

• There must be more documentation.
• It is a profoundly complex tool.
• Its in-memory processing consumes massive memory.

Incentivized

• inbuild template options can be expanded
• more data connector options
• easy of use

Apache Spark Streaming stands above all the huge data transformative tools because of its speed of processing which was quite slow in Presto as it takes a lot of our time in the data processing. Spark, comfortably provides integration with Jupyter like notebook environment. and Spark's combination with Jupyter and Python results in enhancing the speed .

Incentivized

Google Cloud Dataproc Cloud Datafusion

• Cost and time-effective tool for our business.
• We can integrate with Jupyter with many conveniences.
• Its high-speed data processing has proved beneficial for us.

Incentivized

• cost saving from managing our own data center for ETL servers
• consumption based pricing
• with auto scaling feature, we were able to expand components to support work load