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103 Ratings
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241 Ratings
103 Ratings
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Score 8.5 out of 101
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Score 8.2 out of 101

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Likelihood to Recommend

Apache Spark

It is suitable for processing large amounts of data, as it is very easy to use and its syntax is simple and understandable. I also find it useful to use in a variety of applications without the need to integrate many other processing technologies, and it is very fast and has many machine learning algorithms that can be used for data problems. I find it less appropriate for data that is not so large, as it uses too many resources.
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MongoDB

For most every basic web app that I have developed, MongoDB is well suited. I find it hard to imagine scenarios where it would not be...For apps where we have dynamic, user-controlled attributes, MongoDB makes this really easy. I would imagine MongoDB might be least appropriate for teams not interested in trying to learn a NoSQL approach. Try a skunkworks project then...
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Feature Rating Comparison

NoSQL Databases

Apache Spark
MongoDB
8.8
Performance
Apache Spark
MongoDB
8.9
Availability
Apache Spark
MongoDB
8.8
Concurrency
Apache Spark
MongoDB
8.5
Security
Apache Spark
MongoDB
8.4
Scalability
Apache Spark
MongoDB
8.8
Data model flexibility
Apache Spark
MongoDB
9.0
Deployment model flexibility
Apache Spark
MongoDB
9.0

Pros

  • We used to make our batch processing faster. Spark is faster in batch processing than MapReduce with it in memory computing
  • Spark will run along with other tools in the Hadoop ecosystem including Hive and Pig
  • Spark supports both batch and real-time processing
  • Apache Spark has Machine Learning Algorithms support
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  • Schemaless - make data changes on the fly
  • Document Based (aligns closely with object-oriented programming)
  • Built in DR and HA, scalable
  • Rich query language and aggregation tools
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Cons

  • Consumes more memory
  • Difficult to address issues around memory utilization
  • Expensive - In-memory processing is expensive when we look for a cost-efficient processing of big data
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  • It provides less flexibilty while writing complex queries.
  • It should support multiple document level.
  • This takes higher size to store data.
Nikita kumari profile photo

Likelihood to Renew

No score
No answers yet
No answers on this topic
MongoDB8.6
Based on 47 answers
This reduces the overall reads and writes CPU time. As per my humble opinion, it is best where we have to read a lot of data through our applications. There is no need to update the database object references it automatically handles this.
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Usability

No score
No answers yet
No answers on this topic
MongoDB8.0
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

No score
No answers yet
No answers on this topic
MongoDB8.0
Based on 2 answers
I never had problems with the application. It complies with all the characteristics that the company specifies with this product.
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Implementation

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No answers yet
No answers on this topic
MongoDB8.4
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.
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Alternatives Considered

How does Apache Spark perform against competing tools? I think Apache Spark does well in processing large volumes of data. The machine learning models also seem to be easier to program and interpret. With that said, the programming side of Apache Spark seems more difficult to implement good models than Kinesis or other tools. You really have to have lots of data and very valuable questions to answer to justify the investment in Apache Spark.
Thomas Young profile photo
I tried Cassandra, but the performance lags behind MongoDB
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Return on Investment

  • We were able to make batch job faster by 20 times as compared to MapReduce
  • With the language support like Scala, Java, and Python, easily manageable
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  • The ease with which applications can transform data in far greater than before. Developer productivity and happiness has gone up.
  • The integration of applications with third party services has become super easy.
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Screenshots

Apache Spark

Pricing Details

Apache Spark

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

MongoDB

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