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113 Ratings
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296 Ratings
113 Ratings
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Score 8.4 out of 101

MongoDB

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Score 8.2 out of 101

Likelihood to Recommend

Apache Spark

The software appears to run more efficiently than other big data tools, such as Hadoop. Given that, Apache Spark is well-suited for querying and trying to make sense of very, very large data sets. The software offers many advanced machine learning and econometrics tools, although these tools are used only partially because very large data sets require too much time when the data sets get too large. The software is not well-suited for projects that are not big data in size. The graphics and analytical output are subpar compared to other tools.
Thomas Young profile photo

MongoDB

Mongo DB is better placed in large projects, with great scalability. It also allows you to work quite comfortably with projects based on programming languages such as javascript angular typescript C #. I believe that its performance is much better with the type of technologies that handle very logical, similar terms of programming. If we use languages like java php, for example, it is better to work with relational databases like postgres or mySql. Since this type of technology allows you to work better with database management frameworks much more agile for these environments, such as JPA, HIBERNATE, Oracle, I think they are much better with this type of architecture and programming languages.
Ronald Melendez profile photo

Feature Rating Comparison

NoSQL Databases

Apache Spark
MongoDB
8.7
Performance
Apache Spark
MongoDB
8.8
Availability
Apache Spark
MongoDB
8.8
Concurrency
Apache Spark
MongoDB
8.4
Security
Apache Spark
MongoDB
8.3
Scalability
Apache Spark
MongoDB
8.8
Data model flexibility
Apache Spark
MongoDB
9.3
Deployment model flexibility
Apache Spark
MongoDB
8.8

Pros

Apache Spark

  • 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
Nitin Pasumarthy profile photo

MongoDB

  • Easy to learn. When I picked up MongoDB for the first time, I had little background in database management or modeling. If you have a background in javascript (and JSON)... then you can figure out how to use MongoDB pretty fast.
  • Fast performance.
  • It's relatively easy to set up in certain environments because there are lots of ready-made solutions out there.
  • There's a lot of support in the existing ecosystem for it —, especially in the node.js realm.
  • Query syntax is pretty simple to grasp and utilize.
  • Aggregate functions are powerful.
  • Scaling options.
  • Documentation is quite good and versioned for each release.
Joshua Weaver profile photo

Cons

Apache Spark

  • 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
Anson Abraham profile photo

MongoDB

  • I love the idea of Map-Reduce native support in MongoDB. Admittedly I have not used it as much as I would like -- it always seems to trip me up.
  • Recent additions to the aggregation queries have helped reduce (no pun intended) my need to better wield the weapon that is Map-Reduce.
Jon Kern profile photo

Likelihood to Renew

Apache Spark

No score
No answers yet
No answers on this topic

MongoDB

MongoDB 8.6
Based on 58 answers
This is a very convenient "go to" database for application CRUD operations. So many applications need to create, read, update, and delete records. Here's the trick though - as a product changes, the data does as well. Having a database that makes this process easy and avoids the need to manage a schema and migrations is extremely valuable.MongoDB may not be the tool for every need but it is often always a tool that gets used for some need or another.
Tom Maiaroto profile photo

Usability

Apache Spark

No score
No answers yet
No answers on this topic

MongoDB

MongoDB 8.0
Based on 3 answers
I'm not a database expert by any means. But MongoDB has helped lower the barrier to entry in the world of full stack development. It has an expressive and easy to understand syntax and API. Additionally, their documentation is really quite detailed and easy to follow. Anyone with javascript experience should be able to work with it.
Joshua Weaver profile photo

Support

Apache Spark

No score
No answers yet
No answers on this topic

MongoDB

MongoDB 8.0
Based on 2 answers
I never had problems with the application. It complies with all the characteristics that the company specifies with this product.
Fernando Malave profile photo

Implementation

Apache Spark

No score
No answers yet
No answers on this topic

MongoDB

MongoDB 8.4
Based on 2 answers
While the setup and configuration of MongoDB is pretty straight forward, having a vendor that performs automatic backups and scales the cluster automatically is very convenient. If you do not have a system administrator or DBA familiar with MongoDB on hand, it's a very good idea to use a 3rd party vendor that specializes in MongoDB hosting. The value is very well worth it over hosting it yourself since the cost is often reasonable among providers.
Tom Maiaroto profile photo

Alternatives Considered

Apache Spark

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.
No photo available

MongoDB

MongoDB was the most full-featured NoSQL database we evaluated - that offered atomic transactions at a document level, built-in HA & DR, open source, robust queries, and enterprise level support.Other platforms had specific parts of what we were looking for - MongoDB had it all.
Jeff Sherard profile photo

Return on Investment

Apache Spark

  • It has had a very positive impact, as it helps reduce the data processing time and thus helps us achieve our goals much faster.
  • Being easy to use, it allows us to adapt to the tool much faster than with others, which in turn allows us to access various data sources such as Hadoop, Apache Mesos, Kubernetes, independently or in the cloud. This makes it very useful.
  • It was very easy for me to use Apache Spark and learn it since I come from a background of Java and SQL, and it shares those basic principles and uses a very similar logic.
Carla Borges profile photo

MongoDB

  • Much faster development time.
  • Price is fantastic compared to MSSQL when you consider OS costs and the entire package.
  • Only negative to me is the lack of DBA skills for it, due to it being a new player in the field. I feel like that will get better as time moves on however.
Joshua Austill profile photo

Screenshots

Apache Spark

Pricing Details

Apache Spark

General

Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No

MongoDB

General

Free Trial
Yes
Free/Freemium Version
Yes
Premium Consulting/Integration Services
Entry-level set up fee?
No

Rating Summary

Likelihood to Recommend

Apache Spark
8.5
MongoDB
8.5

Likelihood to Renew

Apache Spark
MongoDB
8.6

Usability

Apache Spark
MongoDB
8.0

Reliability and Availability

Apache Spark
MongoDB
9.0

Support

Apache Spark
MongoDB
8.0

Implementation

Apache Spark
MongoDB
8.4

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