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103 Ratings
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Based on 103 reviews and ratings
Top Rated
241 Ratings
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Based on 241 reviews and ratings
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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.
Consultor Tecnico - Java Developer and Php Developer.
Consultec-TIComputer Software, 51-200 employees
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...
Manager Strategic Products
Intelliquip, LLCComputer Software, 11-50 employees
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

Verified User
Employee in Engineering
Hospital & Health Care Company, 501-1000 employees- 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
Senior Leader
GHXHospital & Health Care, 501-1000 employees
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

Verified User
Employee in Engineering
Hospital & Health Care Company, 501-1000 employees- It provides less flexibilty while writing complex queries.
- It should support multiple document level.
- This takes higher size to store data.
Software Developer
eClerxOutsourcing/Offshoring, 5001-10,000 employees
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.
Software Developer
eClerxOutsourcing/Offshoring, 5001-10,000 employees
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.
Developer
By the Book, Inc (Software For Churches/NonProfits)Religious Institutions, 1-10 employees
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.
User support technician
IBMComputer Software, 10,001+ employees
Implementation
No score
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.
Sr. Platform Developer / UX Designer / Optimization Engineer
FunnelEnvyMarketing and Advertising, 11-50 employees
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.
Owner, previous CEO
Econometric StudiosFinancial Services, 11-50 employees
I tried Cassandra, but the performance lags behind MongoDB
IT Consulting & WebDevelopment
selbstständigElectrical/Electronic Manufacturing, 5001-10,000 employees
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

Verified User
Employee in Engineering
Hospital & Health Care Company, 501-1000 employees- 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.
MongoDB Architect
Arigatoni, Inc. DBA MarcopoloniRetail, 1-10 employees
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