Apache Spark vs. Roambi (discontinued)

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.

Incentivized

Getting Roambi up to speed in your company certainly is a quick win. After just a few days, several of our reports were mobilized, interactive and automatically updated. Building Roambi reports is not hard to do; the intuitive interface allows virtually any type of user to create his or her own reports.

• 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

Incentivized

• Report templates that are easy to learn, use, update and maintain.
• Great looking reports that stay up-to-date with what Apple is producing. When Apple rolled out the new look and feel to their iOS7 release, Roambi created a brand new set of reports within two weeks to go along with the iOS7 changes.
• Seamless integration with our existing BI environment, SAP Business Objects XI, 3.1. This drastically reduced the time from purchase to client-ready roll out from months to weeks.

• 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

Incentivized

• cardex type of presentation is already innovated in roambi in 2012, even iOS safari browser idea was created from this cardex style
• blink view is only available for sap and ms sql
• roambi publisher is very user-friendly and understandable for non technical person as well

Incentivized

Capacity of computing data in cluster and fast speed.

I would definitely use Roambi again because of its ease-of-use, but its lack of customization ability makes it difficult to recommend to businesses as business needs are always changing. People are picky, and on the Roambi project, we were not able to fully live up to the customer's expectations, limited by Roambi's preset limitations.

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

Incentivized

VERY easy to use. Only limited by how the graphs and data is initially set up.

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.

Incentivized

We have compared Roambi against Business Objects, Reporting Services (on tablet) and Cubeware Cockpit. All those solutions lack in support of touch capabilities and responsiveness. Roambi "feels" just right. There is no training required and users love to explore the application. Compared to Roambi all other solutions feel very technical and hard to understand.

• 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

Incentivized

• Increased insights on customer success metrics (using a Layers report).
• Higher motivation of employees, better communication towards employees, tighter bonds with the company - because of Roambi Flow. Periodic Flow magazines with analytic capabilities are sent out to the employees, as we value frequent and meaningful contact - both ways.