Camunda vs. Google Cloud Dataflow

Camunda Platform is well suited for scenarios where there are different stages in a business flow and the flow is driven by user action at each stage. For example placing of an order on an ecommerce platform. Depending on whether user was able to make the payment or not the workflow would go to dispatch or retry stage. Now the retry stage would trigger further actions like sending follow up emails etc. Likewise, dispatch stage would have a different set of actions. Since every order is important and we need to know where it stands, using Camunda Platform is imperative. Camunda Platform might not be a right choice where just a one off thing needs to be done. For example, uploading of product information by user or periodic processing of heavy images by a worker. These are all either one step processes or periodic automated processes where we can track the status without using a business modeler like Camunda Platform.

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

• Asynchronous functionality of Camunda Platform and different types of gateways.

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

• Camunda expects that you will develop your own user interfaces. This is either a benefit or a barrier depending on your perspective on packaged UI.
• As an open source vendor, Camunda is under-covered in analyst reports.

• 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

Camunda provides pretty standard product support offerings.

Lacks good documentation. Training and documentation is geared towards those who are already technically adept. Does not have as many data integrations as other full fledged products. Paid version of Camunda is not as fully fledged as other products.

Incentivized

Google Cloud Dataproc Cloud Datafusion

• The positive impact is that we are able to ensure the business process is being followed and that results in orders getting processed successfully leading to customer satisfaction and revenue
• Another positive impact is that we are able to track any anomalies and any errors in the order flow and retry them so that users don't have a negative experience.
• A negative point is that it is an overhead to maintain so there is significant engineering effort getting invested there

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