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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OpenSearch
Score 8.2 out of 10
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OpenSearch is an open-source software suite for search, analytics, and observability applications licensed under Apache 2.0. Powered by Apache Lucene and driven by the OpenSearch Project community, OpenSearch offers a vendor-agnostic toolset that can be used to build applications, or as an end-to-end solution, or connected with preferred open-source tools or partner projects.
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.
OpenSearch Service presents a cost-effective pricing model, allowing users to pay solely for their usage without being burdened by minimum fees or upfront commitments. Its seamless integration with various AWS services enhances its adaptability for a wide range of data analysis requirements and also I love using it isn't it enough.
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.
Well as I said, Elastic is behind paywall now and managing OpenSearch through AWS is so seamless that we just love it. It gets updates faster we don't have to manage separate infra and many other settings to work with elastic search and some of the tools that it provides are better then them.
