Apache Spark vs MongoDB

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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Carla Borges

Consultor Tecnico - Java Developer and Php Developer.

Consultec-TIComputer Software, 51-200 employees

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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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Jon Kern

Manager Strategic Products

Intelliquip, LLCComputer Software, 11-50 employees

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Feature Rating Comparison

Apache Spark

—

MongoDB

8.8

Performance

—

8.9

Availability

—

8.8

Concurrency

—

8.5

Security

—

8.4

Scalability

—

8.8

Data model flexibility

—

9.0

Deployment model flexibility

—

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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Verified User

Employee in Engineering

Hospital & Health Care Company, 501-1000 employees

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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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Jeff Sherard

Senior Leader

GHXHospital & Health Care, 501-1000 employees

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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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Verified User

Employee in Engineering

Hospital & Health Care Company, 501-1000 employees

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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.

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Nikita kumari

Software Developer

eClerxOutsourcing/Offshoring, 5001-10,000 employees

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Likelihood to Renew

No score

No answers yet

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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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Nikita kumari

Software Developer

eClerxOutsourcing/Offshoring, 5001-10,000 employees

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Usability

No score

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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.

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Joshua Weaver

Developer

By the Book, Inc (Software For Churches/NonProfits)Religious Institutions, 1-10 employees

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Support

No score

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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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Fernando Malave

User support technician

IBMComputer Software, 10,001+ employees

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Implementation

No score

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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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Tom Maiaroto

Sr. Platform Developer / UX Designer / Optimization Engineer

FunnelEnvyMarketing and Advertising, 11-50 employees

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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.

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Thomas Young

Owner, previous CEO

Econometric StudiosFinancial Services, 11-50 employees

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I tried Cassandra, but the performance lags behind MongoDB

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Michael Höller

IT Consulting & WebDevelopment

selbstständigElectrical/Electronic Manufacturing, 5001-10,000 employees

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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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Verified User

Employee in Engineering

Hospital & Health Care Company, 501-1000 employees

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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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