Apache Spark vs. Perforce Delphix

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.

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REST API calls are available for all Delphix activities. Delphix makes it simple to move data between production to other test or QA environments. Through the replacement with a dummy, yet realistic data of the customer or patient data, you reduce the danger of data breaches for any and all copies of data you produce.

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• Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues
• Faster in execution times compare to Hadoop and PIG Latin
• Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner
• Interoperability between SQL and Scala / Python style of munging data

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• Fast refresh of production data to non-production environments.
• Easy to automate tasks in Delphix (Bookmark, Rewind, Refresh, etc.) and build pipelines in tools like Jenkins. Enables CI/CD push button build of new virtual database and also the ability to automate regression testing scenarios as well as provide a simple way to expose self-service to developers & testers.
• The staff at Delphix are knowledgeable and take a genuine interest in helping their customers succeed. The support organization is among the best I've seen.
• Delphix is good at notifying customers of bugs & features striking a good balance on sending the right amount and type of communication.
• Delphix is always improving the product and fixing any bugs quickly and transparently.
• Delphix Data Masking product works as described and allows for multiple streams of masking concurrently to complete masking faster.

• Memory management. Very weak on that.
• PySpark not as robust as scala with spark.
• spark master HA is needed. Not as HA as it should be.
• Locality should not be a necessity, but does help improvement. But would prefer no locality

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• Delphix is a hefty solution for virtualization, but it is not strong enough for the creation of data, so we have to use another platform when it comes to creating new data.
• Moreover, Delphix web pages do not support AD security groups which eventually results in hassle if you have a large number of users to create and maintain passwords.

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Capacity of computing data in cluster and fast speed.

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

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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.

Delphix support could be a little better. It seems sometimes we bounce through support engineers until we get one that understands our issues and know how to fix it.

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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.

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The other competitors in the market are Solix and Data Global and SAP. The pricing and the UI are much simpler and easy to put Delphix in the top spot.

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• Business leaders are able to take data driven decisions
• Business users are able access to data in near real time now . Before using spark, they had to wait for at least 24 hours for data to be available
• Business is able come up with new product ideas

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• Positive ROI on the first year.
• Low TCO as really easy to use and manage.
• Avoid technology vendor lock in.
• Avoid technology difficulties to manage data, one ingested Delphix is on charge.