Google Cloud Dataflow vs. SAS Event Stream Processing vs. Spotfire Streaming

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

Taking data from various sources including files, databases, web services, applying some complex rules, transforming, aggregating and producing a result. This is what Spotfire Streaming does best.
- If one needs connectivity to special services as secured databases or web services, building interactive web apps, those are probably tasks that shall be addressed with different tools.

Incentivized

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

• Processing events in real-time with real low latency and high throughput.
• 100% visual program language, which can be extended by common languages like Java, Python and .NET.
• Reduced time to prototype, create an application and deployment, which reduces the software lifecycle.
• Real robust engine and server. Barely heard of customers having issues in production.

• 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

• Being a niche tool, there's not much community support
• It is on prem, making it slow to boot. not cloud native (It could be that it is our org's issue)
• The dev environment is very tough to understand for large projects due to the wire style UI/UX

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

The usability is good in terms that it gets well integrated with the Spotfire suite but the only few issues I have is the tough UI/UX (learning curve, if the project is huge) and unable to find many users and devs to help with the queries. At the end it is solely based on the documentation provided which is never enough

Incentivized

Spotfire Streaming support is prompt and to the point. They help with best practices and learning from existing projects.

Incentivized

Google Cloud Dataproc Cloud Datafusion

We are using Dataflow (by Google).The development time in Spotfire Streaming is definitely shorter because its GUI based. Dataflow handles late arrivals after the window closes, not sure Spotfire Streaming can do that. Dataflow can run GCP as a managed service which is why we chose that tool for our new product.

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

• While we haven't specifically integrated Spotfire Streaming into our product development, it has allowed us to see the benefits of real-time streaming data.
• We have much more visibility into how our longer term roadmap will look and what we should focus on.

Incentivized