Apache Spark Streaming Reviews

We're primarily using the solution for anomaly detection.

I like that it's Python. We have a Python ecosystem. Therefore, it fits perfectly.

The initial setup is simple. 

The solution can scale.

It's a stable product.

As an open-source solution, using it is basically free.

We would like to have the ability to do arbitrary stateful functions in Python.

We started using the solution half a year ago.

The solution is stable. There are no bugs or glitches. It doesn't crash or freeze.

It's enterprise-ready. It's very scalable.

As we're using it mostly for data science types of activities, there are maybe eight active users.

While we're purchasing external consulting to support us, the documentation is pretty good.

We tried Flink, however, it was not satisfactory.

It's not a complex implementation. In our case, it was easy as we have a hosted environment. The deployment only takes a couple of minutes.

The solution is open-source. That's pretty reasonable. It's basically free.

We are a customer and end-user.

We're using it in Azure, in Databricks. I don't know the exact version of Spark I'm using; it's one of the recent ones.

I would rate the product an eight out of ten.

The platform’s most valuable feature for processing real-time data is its ability to handle continuous data streams.

The product's event handling capabilities, particularly compared to Kaspersky, need improvement. Integrating event-level streaming capabilities could be beneficial. This aligns with the idea of expanding Spark's functionality to cover unaddressed areas, potentially enhancing its competitiveness.

We have been using Apache Spark Streaming for five years.

Spark is an affordable solution, especially considering its open-source nature. However, it could use support from experienced companies to resolve any issues effectively.

Spark does not encounter integration issues, particularly due to its utilization of JDBC connectors. These connectors facilitate seamless integration with third-party solutions. Furthermore, successful integration with tools like SAP HANA indicates its versatility in handling various data sources. Additionally, its performance surpasses Informatica in certain scenarios, especially when real-time streaming capabilities are crucial. It remains a preferred choice for businesses requiring efficient real-time data processing.

I rate it an eight.

The primary use of the solution is to implement predictive maintenance qualities. 

The solution is better than average and some of the valuable features include efficiency and stability.

There could be an improvement in the area of the user configuration section, it should be less developer-focused and more business user-focused. For example, it is still not plug and play and use as some of the cloud offerings that come ready to use. It is not up there in the reading leading edge.

I have been using this solution for approximately one and a half year. 

The solution is very stable.

The initial setup is developer-focused but it is not very complex. I can set up a stream in less than an hour. It will stream but It will not be a production-ready stream.

I rate Apache Spark Streaming a six out of ten.