Google Cloud Dataflow vs. SAS Event Stream Processing

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

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

• 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

Google Cloud Dataproc Cloud Datafusion

• IT has automated our workflow and data enrichment steps which were very resource and time hungry steps.
• Unlike traditional ETL tools that require a 24/7 server, Dataflow scales to zero when there are no files are in GCS which is very important for us.
• With the Apache Beam SDK you can write a pipeline once and handle the entire GCS-to-BigQuery flow.

Incentivized