Apache Spark Streaming vs. Azure Stream Analytics 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

Data enrichment is effectively done in stream analytics also checking the values with different functionality like windowing and group by clause is effectively working.

Incentivized

It is best in cases where you have batch as well as streaming data. Also in some cases where you have batch data right now and in future you will get streaming data. In those cases Dataflow is very good. Also in cases where most of your infra is on GCP. It might not be good when you already are on AWS or Azure. And also you want in-depth control over security and management. Then you can directly use Apache beam over Dataflow.

Incentivized

• 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

• Routing of data from multiple inputs to multiple output
• You create your own user define function.
• Intermediate query is working very effectively.

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

• Code competency is not that much effective
• Ml models can't be integrated with stream analytics

Incentivized

• More templates for Bigquery and App Engine. There is only limited options for templates so the things we use can limit.
• I would like native connectors for Excel (XLSX) to reduce the need for custom wrappers in financial pipelines.
• Debugging Google Cloud Dataflow using only logs in Cloud Logging can be overwhelming sometimes, and it’s not always obvious which specific element in the flow caused a failure. IT uses a lot of time.

Incentivized

It really saved a lot of time and it's flexibility really can give you infra which is future-proof for most of the use cases may it be streaming or batch data. And with this you can avoid use of resource-heavy big data offerings.

Incentivized

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

Azure Stream Analytics is easy to implement and also to integrate compare to other services like iot analytics

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

• Very nice roi while using it.
• Multiple integration is the best functionality

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