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

It is a stable solution, but when it comes to patch updates and different reports being updated, it can be a headache. 

When an application is built on top of certain reports with specific versions, and then the version changes, it can lead to a lot of things needing to be adjusted. This is something that definitely needs improvement.

The platform’s stability depends on how effectively we write the code. We encountered a few issues related to programming languages.

The solution is stable, but not completely. For example, we use Exadata as an extremely stable data warehouse, but that's not possible with big data. There are things that you have to fix sometimes. The stability is similar to the cloud solution, but that depends on the solution you need.

Stability-wise, I rate the solution a nine out of ten.

I have not encountered any significant stability issues and it has proven to be a robust and reliable platform without major crashes. However, there have been instances where I needed to address query optimization and similar tasks to ensure optimal performance. I would rate it nine out of ten.

It offers a high level of stability. I would rate it nine out of ten.

Stability-wise, I rate the solution an eight and a half out of ten.

The solution was definitely stable. There were no unstable services in it. Since most services were in Azure, everything worked better. 

Azure's networking products, like ExpressRoute and Private Link service, are very stable. We didn't encounter any issues with the solution. 

It took some time to complete, but after that, we haven't had a single support case.

Apache Spark is stable.

Apache Spark is a very stable solution. The community is still working on other parts, like performance and removing bottlenecks. However, from a stipulative point of view, the solution's stability is very good.

I rate Apache Spark a nine out of ten for stability.

It is a stable product. I rate its stability an eight out of ten.

Apache Spark is generally considered a stable product, with rare instances of breaking down. Issues may arise in sudden increases in data volume, leading to memory errors, but these can typically be managed with autoscaling clusters. Additionally, schema changes or irregularities in streaming data may pose challenges, but these could be addressed in future software versions.

The tool is stable. I rate the stability a ten out of ten.

The stability is very good. There are no bugs or glitches and it doesn't crash or freeze. It's a reliable solution. 

The solution is stable. 

It is the most stable platform. As compare to Flink, Spark is good, especially in terms of clusters and architecture. My colleagues who set up these clusters say that Spark is the easiest.

It is a stable solution. The solution is ten out of ten on stability. 

I rate the product's stability a ten out of ten.

The solution is highly stable. I rate it a perfect ten.

The tool is stable. I rate the stability more than nine out of ten.

I haven't faced any stability issues. It has been stable in my experience.

We have found this solution to be stable during our time using it.

The solution is stable.

We haven't had any issues with stability so far. 

It provides excellent stability. We never faced any issues with it.

See above regarding memory usage.

Spark Streaming is difficult to stabilize as you're always dependant to your stream flow. If you start to be late on the consumer you've a serious problem. We've encountered a lot of stability issue to configure it as expected

Generally, Spark works correctly without any errors. It may give out some errors if your data changes, but in that case, it's a problem with the configuration, not with Spark.

The stability of the solution is very, very good. It doesn't crash or have glitches. It's quite reliable for us.

In terms of stability, I have not seen any bugs, glitches or crashes. Even if there is, that's fine, because I would probably take care of it and then I'd have progressed further in the process.

At times when users do not know how to use Spark and request a lot of resources, then the underlying JVMs can crash, which is a big sense of worry. 

There were bugs three to four years ago, which have been resolved. There were a couple of issues related to slowness when we did a lot of transformations using the Width columns. I was writing a POC on ETL for moving from Informatica to Spark SQL for the ETL pipeline. It required the use of hundreds of Width columns to change the column name or add some transformation, which made it slow. It happened in versions prior to version 1.6, and it seems that this issue has been fixed later on.

I found the solution stable. We haven't had any problems with it.

This product is pretty stable. Companies like Facebook, Uber, and Netflix are all using Apache Spark. It's stable enough to be used all over the world.

In the past we deployed Spark 1.3 to use Spark SQL but unfortunately one of our queries failed because of a bug fixed in following releases. Then we moved to Spark 1.6 but still some queries were failing when run against huge datasets. Now we are using version 2.1: it is more stable, it ensures better performances and the SQL/ML parts are reacher than before.

We met some standalone deployment issues, which showed that its stability is not that good. So we plan to switch to Yarn or Mesos mode

No.

I ran into Spark application performance issues. For instance, Spark JDBC write performance needs to be improved.

It's been stable for us.

This solution is difficult for users who are just beginning and they experience out of memory errors when dealing with large amounts of data.

Yes, occasionally with different APIs.

Apache Spark is stable.

There have been no issues with the stability.

The solution is very stable.

No, I did not encounter any problems with the stability. It is also quite backwards compatible.

There have been no stability issues.

The 2.3 version is quite stable. All of our customers use it, there are around 100,000+ users, and it runs 24/7.

We don't have any problems with stability.