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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Dell ECS
Score 8.3 out of 10
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Dell ECS (formerly Atmos) is an object-based cloud storage platform. The vendor states that it has been engineered to support both traditional and next-generation workloads alike. Deployable in a software-defined model or as a turnkey appliance, the vendor boasts that ECS provides unmatched scalability, manageability, resilience, and economics to meet the demands of modern business.
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.
Dell ECS is well-suited for organizations that can't afford to store data in the public cloud. It also provides a much cheaper solution to store archived data that is not frequently accessed. However, it's not suitable for small-scale storage requirements as it will not be cost-effective. Also, it can't be used for low-latency databases as it will cause performance issues.
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
Dell ECS is working well for our organisation. On the one hand, we can leverage cloud technology; on the other, we can keep it in our own data centre, thus ensuring full security for sensitive data. With Dell ECS, we are saving a lot on the monthly bill that we used to pay for the cloud storage solution.
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.
Because of Dell's proven reliability and stability, it's well-suited to large enterprises compared to its competitors. Its Geo-distribution technology protects data across sites in a very cost-effective manner. It supports storage for both modern and legacy applications. Its archive storage costs are much lower than those of its competitors. It saves monthly bills as we own the hardware.
