Apache Spark vs. Boomi

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.9 out of 10
N/A
N/AN/A
Boomi
Score 8.2 out of 10
N/A
Boomi is a cloud-based, on-premise, or hybrid integration platform. It offers a low-code/no-code interface with the capacity for API and EDI connections for integrating with external organizations and systems, as well as compliance with data protection regulations.
$550
per month
Pricing
Apache SparkBoomi
Editions & Modules
No answers on this topic
Boomi
$550
per month
Offerings
Pricing Offerings
Apache SparkBoomi
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Features
Apache SparkBoomi
Cloud Data Integration
Comparison of Cloud Data Integration features of Product A and Product B
Apache Spark
-
Ratings
Boomi
8.1
33 Ratings
1% above category average
Pre-built connectors00 Ratings9.532 Ratings
Connector modification00 Ratings6.029 Ratings
Support for real-time and batch integration00 Ratings9.631 Ratings
Data quality services00 Ratings9.629 Ratings
Data security features00 Ratings6.831 Ratings
Monitoring console00 Ratings7.033 Ratings
Best Alternatives
Apache SparkBoomi
Small Businesses

No answers on this topic

Make
Make
Score 9.2 out of 10
Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.9 out of 10
Make
Make
Score 9.2 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 7.8 out of 10
IBM App Connect
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Score 8.9 out of 10
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User Ratings
Apache SparkBoomi
Likelihood to Recommend
9.4
(24 ratings)
9.0
(41 ratings)
Likelihood to Renew
10.0
(1 ratings)
10.0
(9 ratings)
Usability
8.7
(4 ratings)
8.0
(6 ratings)
Performance
-
(0 ratings)
7.9
(5 ratings)
Support Rating
8.7
(4 ratings)
6.0
(2 ratings)
User Testimonials
Apache SparkBoomi
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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Boomi
Legacy systems often need to be replaced or integrated with new applications in order to modernize businesses. A strong API strategy that avoids custom coding and third-party programs is essential to enable this integration. Boomi's new-age connectivity and integration solutions ensure safe, secure, and robust integration. In the age of information, businesses are under more pressure than ever to be able to collect and manage large amounts of data. This data comes in from a variety of sources, including personalized devices such as voice assistants and wearable tech. While this data can be immensely valuable to businesses, they often lack the infrastructure necessary to handle it effectively. This can lead to data build-up in databases or silos, and can eventually lead to problems with integration and security.
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Pros
Apache
  • 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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Boomi
  • Pre-built connectors to almost anything which greatly reduce time to release of your integration needs.
  • Easy to use development platform with drag&drop features. Non-technical people could build simple integration processes in no time.
  • Great user community where you can get quick help if needed. Boomi people hang around there a lot.
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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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Boomi
  • More from a development perspective. It is always difficult to use the properties features. It takes a while to understand how the data/variables can be used across an integration.
  • Dell Boomi should also invest more on API Management and not just seen as a ETL,ESB tool.
  • Should roll out features more often based on users reviews.
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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Boomi
Dell Boomi has provided us with the ability to connect our campus together using our various existing platforms. There are many supported features and have yet to run into something that we cannot do. Its user interface is very intuitive which would allow users to begin developing fairly easily. There is a myriad of resources available
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Usability
Apache
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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Boomi
My IT and Finance teams have noted that setting up the tool is a breeze. Dell Boomi has never caused an issue during a system implementation that I am aware of. We are pleased with the tool and recommend others consider it.
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Performance
Apache
No answers on this topic
Boomi
The atom sphere takes a time to load, when I open a process or when I open a log. One more slow processing is when I import objects from NetSuite.
About the performance of processing, it looks like Boomi takes a time to initialize some things such as connectors before starting the process. This is also performance we have.
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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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Boomi
Boomi support was responsive and knowledgable, however being a closed cloud service, it doesn't have good community support. We found the learning curve to be steep and there aren't avenues like google, forums, or blogs that provide community driven insight into the product or how to go about designing solutions using the tool
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Alternatives Considered
Apache
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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Boomi
We decided to go with Dell Boomi because another department in our company was already using the software. We did not research competitor applications to use as our business solution. Dell Boomi was very easy and quick to set up, so once we decided to use Dell Boomi for systems integration, we had it set up and running within a few working days.
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Return on Investment
Apache
  • 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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Boomi
  • It has allowed us to scale significantly without having to add headcount, specifically those geared towards data entry. We went from a $10m ARR business to $200m ARR business with the same amount of Order Processors and 12x amount of transactions by leveraging Boomi to perform a lot of the work, and then having the Order Processing team to simply review that the transaction was processed successfully.
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