Apache Spark vs. SnapLogic

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.6 out of 10
N/A
N/AN/A
SnapLogic
Score 7.6 out of 10
N/A
SnapLogic is a cloud integration platform with a self-service capacity supported by over 450 prebuilt modifiable connectors. SnapLogic also offers real-time and batch integration processes for interfacing with external data sources, a drag-and-drop interface, and use of the vendors’ Iris AI.N/A
Pricing
Apache SparkSnapLogic
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache SparkSnapLogic
Free Trial
NoYes
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoYes
Entry-level Setup FeeNo setup feeOptional
Additional Details
More Pricing Information
Features
Apache SparkSnapLogic
Cloud Data Integration
Comparison of Cloud Data Integration features of Product A and Product B
Apache Spark
-
Ratings
SnapLogic
7.7
24 Ratings
6% below category average
Pre-built connectors00 Ratings8.222 Ratings
Connector modification00 Ratings6.919 Ratings
Support for real-time and batch integration00 Ratings7.424 Ratings
Data quality services00 Ratings7.820 Ratings
Data security features00 Ratings7.522 Ratings
Monitoring console00 Ratings8.124 Ratings
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Apache SparkSnapLogic
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User Ratings
Apache SparkSnapLogic
Likelihood to Recommend
9.9
(24 ratings)
8.1
(24 ratings)
Likelihood to Renew
10.0
(1 ratings)
9.0
(2 ratings)
Usability
10.0
(3 ratings)
7.0
(1 ratings)
Support Rating
8.7
(4 ratings)
8.5
(4 ratings)
Implementation Rating
-
(0 ratings)
9.0
(1 ratings)
User Testimonials
Apache SparkSnapLogic
Likelihood to Recommend
Apache
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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Snaplogic
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.
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Pros
Apache
  • Apache Spark makes processing very large data sets possible. It handles these data sets in a fairly quick manner.
  • Apache Spark does a fairly good job implementing machine learning models for larger data sets.
  • Apache Spark seems to be a rapidly advancing software, with the new features making the software ever more straight-forward to use.
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Snaplogic
  • 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.
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Cons
Apache
  • 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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Snaplogic
  • 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.
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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Snaplogic
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!
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Usability
Apache
The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.
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Snaplogic
It is very powerful but has a steep learning curve
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Support Rating
Apache
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.
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Snaplogic
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.
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Implementation Rating
Apache
No answers on this topic
Snaplogic
The groundplex in our VPC is very nice for security reasons and the SnapLogic team was extremely helpful during our implementation
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Alternatives Considered
Apache
All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional type programming easily based on the situation. Also it doesn't need special data ingestion or indexing pre-processing like Presto. Combining it with Jupyter Notebooks (https://github.com/jupyter-incubator/sparkmagic), one can develop the Spark code in an interactive manner in Scala or Python
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Snaplogic
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.
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Return on Investment
Apache
  • Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark.
  • Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy.
  • Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs.
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Snaplogic
  • 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
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ScreenShots

SnapLogic Screenshots

Screenshot of Here you can see the Designer of SnapLogic's Enterprise Integration Cloud, which showcases our clicks-not-code approach to creating integration pipelines. Notice the machine-learning powered integration assistant, Iris, to the right suggests which Snaps (our term for connectors) to use from our catalogue of nearly 450+ pre-built connectors.Screenshot of Here is the Enterprise Integration Cloud Dashboard that empowers you to monitor the status and health your pipelines and Snaplexes. From here you can further optimize your pipelines to your needs.Screenshot of Here is the Manager tab of the Enterprise Integration Cloud Dashboard, where you can manage users, groups, project spaces, pipelines and security. You can also view account and Snap statistics here.