Apache Spark Streaming vs. Azure Stream Analytics vs. Spotfire Streaming

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

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

• 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

• 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.

• 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

• 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

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

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

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 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

• 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