Apache Spark Streaming vs. TIBCO Streaming (StreamBase)

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

Taking data from various sources including files, databases, web services, applying some complex rules, transforming, aggregating and producing a result. This is what SB 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

• 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

• Not all problems are suited to the event driven paradigm.
• As the complexity of an application grows, finding your way around code in the GUI takes some getting used to.
• The StreamBase development environment is built in Eclipse, which is not everyone's cup of tea.

StreamBase 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

We are using Dataflow (by Google).The development time in StreamBase is definitely shorter because its GUI based. Dataflow handles late arrivals after the window closes, not sure StreamBase 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

• While we haven't specifically integrated StreamBase 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