Google Cloud Dataflow vs. Google Cloud Pub/Sub 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

If you want to stream high volumes of data, be it for ETL streaming or event sourcing, Google Cloud Pub/Sub is your go-to tool. It's easy to learn, easy to observe its metrics and scales with ease without additional configuration so if you have more producers of consumers, all you need to do is to deploy on k8s your solutions so that you can perform autoscaling on your pods to adjust to the data volume. The DLQ is also very transparent and easy to configure. Your code will have no logic whatsoever regarding orchestrating pubsub, you just plug and play. However, if you are not in the Google Cloud Pub/Sub environment, you might have trouble or be most likely unable to use it since I think it's a product of Google Cloud.

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

• With a pub/sub architecture the consumer is decoupled in time from the publisher i.e. if the consumer goes down, it can replay any events that occurred during its downtime.
• It also allows consumer to throttle and batch incoming data providing much needed flexibility while working with multiple types of data sources
• A simple and easy to use UI on cloud console for setup and debugging
• It enables event-driven architectures and asynchronous parallel processing, while improving performance, reliability and scalability

Incentivized

• 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

• Would be nice if the queue could be extended beyond 7 days.
• We found it a bit tricky replay unacknowledged messages when needed.

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 serves all of our purposes in the most transparent way I can imagine, after seeing other message queueing providers, I can only attest to its quality.

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

It is easy to create Google Cloud Pub/Sub topics from both Web Console and CLI commands.

Google Cloud Pub/Sub supports creation of one or more subscriptions.

By supporting a BigQuery Pub/Sub subscription to automatically write to a BigQuery table it simplifies development by avoiding implementation of a custom micro service for writes to BigQuery.

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

I have never faced a single problem in 4 years.

It's very fast, can be even better if you use protobuf.

They have decent documentation, but you need to pay for support. We weren't able to answer all our questions with the documentation and didn't have time to setup support before we needed it so I can't give it a higher rating but I think it tends to be a bit slow unless you're a GCP enterprise support customer.

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

Having used Amazon Web Services SNS & SQS I can say that even if the latter may offer more features, Google Cloud Pub/Sub is easier to use. On the other hand, usage of SNS & SQS as well as documentation and troubleshooting is easier with the AWS solution. Since we are not using GCP only for Pub/Sub the choice depends on other variables.

Incentivized

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

You can just plug in consumers at will and it will respond, there's no need for further configuration or introducing new concepts. You have a queue, if it's slow, you plug in more consumers to process more messages: simple as that.

• 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

• Increased Efficiency with reliable and Google managed services up all the time wit Disaster Recovery in place as well
• Definitely Lower costs being a cloud based solution and easier to setup
• Faster Project delivery and go to market plan for the business use cases basis this technology at the back end

Incentivized

• 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