Apache Spark vs. SnapLogic

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

Snaplogic is unique from other IPASS tools if you're very sensitive about data security as they have an on-premise option where your data never needs to leave your data center. And data pipelines can be quickly created if Snaplogic has the requisite connector to your data sources. On the downside, if you're transforming a large amount of data for example in training machine learning models, a tool with elastic compute capability is more appropriate.

• 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

• Easy access to any type of source system. Data could be in any format.
• Very beautiful visual representation of transforms that makes it super easy to use it by any non developer.
• It can be run in cloud or on-premise. helping you choose your comfort of security.
• Has pretty good customer support and have recently started their community forum as well.

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

• They need to have a way to connect to GitHub to allow the users to maintain their version control in GitHub. This is a missing functionality.
• As pipelines become complex, it's difficult to have all the snaps stitched together - just like to see it done differently.
• They do not have a way to start/stop a preview. This is hard to use, especially if you have to stop an accidental preview invocation.

Incentivized

Capacity of computing data in cluster and fast speed.

This has been hands down the BEST software company I have ever used and dealt with. I am a 25 year IT veteran at this college. They go above and beyond in soliciting our feedback/input and proactively follow up about bugs, issues, etc. I have given multiple potential clients my thoughts and after seeing the SL demo they all sign up. I appreciate their support model, it's REFRESHING!

Incentivized

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

It is very powerful but has a steep learning curve

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 can be prompt but they have not been as useful as I've wanted. We had a bug that affected many of our customers through an API connection between SnapLogic and our platform. Eventually they were able to figure it out, but it took a long time of negotiating between our engineering team and theirs. Additionally, we installed the SnapLogic groundplex for our customers and we've run into a bunch of problems of connectivity. If SnapLogic offered to be on those calls with our clients to troubleshoot how to fix these problems, I would give them a better grade here.

Incentivized

The groundplex in our VPC is very nice for security reasons and the SnapLogic team was extremely helpful during our implementation

Incentivized

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 opted for SnapLogic due its ease of use and the flexibility it offers, it was the platform that was strongest in both application integration and data integration and both were use cases we wanted to be able to cover.

• 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

• We have cut development time down by at least 70%
• The software was more on the expensive side at renewal which required some further approvals to be sought for the spend
• More developers are able to build and use Snaplogic pipelines in their projects

Incentivized