Apache Spark Streaming Reviews

The primary use case of this solution is for streaming data. It can stream large amounts of data in small data chunks which are used for Databricks data. I've been using the solution for personal research purposes only and not for business applications. I'm a customer of Apache. 

Data streaming would be the best feature of Spark and that includes when it's compared to Hadoop or Hive or Cassandra. It's the fastest solution on the market with low latency data on data transformations. I like that it's open source and easy to integrate with other data sources. 

The initial setup is quite complex. 

I've been using this solution for six months. 

The solution is stable. 

The solution is easily scalable in the cloud as per the limitations of the subscription. 

I have previously used a variety of different streaming platforms. I have written a paper analyzing various solutions for efficient streaming for cluster analysis and have published it. I found that Spark has the most features and is the quickest solution compared to the others when it comes to the transformation of data without any latencies or issues.

With a few commands it's possible to install. I installed it in a Linux environment. That said, the initial setup is complex because we have to learn either Java or Scala language. Spark Streaming has a few features in GitHub and its libraries, so we need to get some code to maintain some methods or functions to integrate with any data sources, and then we'll try to run those integrations. It may be that only a high level programmer familiar with Scala and Java can implement. There are quite extensive pre-requirements for using it properly.  

I'm using the open-source version of Spark, so there are no licensing costs. 

It's important to be familiar with Spark Streaming and Spark libraries, because familiarity with those scripts and coding languages makes it easier to work with the Spark code ecosystem to get the integrations of Spark Streaming or any Spark cluster creations.

I rate this solution eight out of 10. 

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.