Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
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VictoriaMetrics Community
Score 5.2 out of 10
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VictoriaMetrics is a high-performance monitoring solution and time series database
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Apache Spark
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Apache Spark
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Enterprise support prices are negotiated individually with every customer. The price depends on many factors such as:
* Costs for the existing monitoring solution
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Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.
Best suited, where your data is highly cardinal since it does a better job at maintaining it than other competitors. It is also well suited if you are using Prometheus and are looking for something that is less hungry for resources in comparison since the migration would be easier. But in case the company is small and wants a solution which is cheap and relies on built-in visualizations, it is not something that is suited. Although it takes fewer resources than Prometheus, it is still resource-intensive and attracts a high cost for maintenance.
If the team looking to use Apache Spark is not used to debug and tweak settings for jobs to ensure maximum optimizations, it can be frustrating. However, the documentation and the support of the community on the internet can help resolve most issues. Moreover, it is highly configurable and it integrates with different tools (eg: it can be used by dbt core), which increase the scenarios where it can be used
1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.
Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.
Prometheus only support PromQL and it is very complex with different exporter required for different requirement like Windowsexporter,linixexporter,sqlexporter etc but VictoriaMetrics is very simple comapred to it. VictoriaMetrics support both PromQL and MetricQL and can be integrated with Graphana easily. It is very easy to setup and learn compared to mutiple Prometheus exporters
