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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Zadara Cloud Platform
Score 7.0 out of 10
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The Zadara Cloud Platform, from Zadara in Irvine, provides enterprise storage as a service (STaaS), providing block storage, object storage, or file (NAS) storage in the cloud. Zadara's features include snapshots, replication, multi-zone high availability, online volume migration, thin provisioning, and all in a cloud-based package.
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.
I only used it as a proof of concept for some products. For me, it seemed to be focused on the European market, but it´s a pretty new cloud provider in comparison to other major competitors like Amazon and Azure which are much more developed, so they are still missing some functionality and features.
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.
Zadara is a new cloud provider, so it does not have all functionalities and features as its major competitors like Amazon Cloud and MS Azure. It is up to you to test it, but personally, I still prefer MS Azure as a cloud provider.
