Apache Spark vs. Jitterbit

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

This is a great tool for bringing data out of your locked, internal systems and getting it into the cloud. It meshes well with Salesforce and is fairly easy to use, helping the transition from other older, more complex tools into a more modern environment. It has lots of competition in this space and some are better than others, but if your data is straight forward and you know it well, Jitterbit will get the job done. If you are not as close or comfortable with your data and need to do some wildly complex migrations, there might be better packages out there for you.

Incentivized

• 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

• UI is super easy to understand with a low learning curve so admins can figure it out and maintain it without breaking anything.
• It's FREE! There's a paid version too but I like that you can use most of the features for free and they're not pushy with buying.
• There's a great user community that you can google and ask any questions. Most problems I've encountered have been posted and answered already.

Incentivized

• 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

• Migrating operations from QA to Production work well for initial deployment, however, when migrating an update to an existing job to production, sometimes certain project items are duplicated. This is not the end of the world... the duplicates can be removed, but would be nice if it was not required.
• I have not found a way to trap under-the-covers SOAP errors (for example, when a query you are running against Salesforce takes too long). You get a warning error in the operation log that the job only pulled a "partial" file, but it does not fail.

Incentivized

Capacity of computing data in cluster and fast speed.

I have been evaluating other tools as a continuous improvement practice. I would like something that would be easier to use for a non-technical user. I work for a small organization and have no back-up for Jitterbit if something happens to me. We don't have the technically savvy employees to understand it.

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

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.

They have some of the best support of any software vendor that we use. They always get our issues resolved quickly

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

Evaluated Dell Boomi and Celigo as alternatives prior to purchasing Jitterbit. We went with Jitterbit at that time because we could handle all changes ourselves without any assistance from Jitterbit, and we liked their size and nimbleness. Dell Boomi was too big for us, and Celigo at that time did not have a self-service model. Every change had to go through them (although that has since changed). We were not in a position to be able to wait for someone to make changes for us given the rate of change within the business.

Incentivized

• 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

• The time it takes to connect systems has reduced by orders of magnitude. Previously, we would custom-develop connectors between various systems and they would all be managed by different vendors. With Jitterbit speed-to-deploy and the efficiency gained by managing all connections in one dashboard has been the greatest piece of the ROI.

Incentivized