Apache Spark vs. AWS Elastic Beanstalk

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.6 out of 10
N/A
N/AN/A
AWS Elastic Beanstalk
Score 8.9 out of 10
N/A
AWS Elastic Beanstalk is the platform-as-a-service offering provided by Amazon and designed to leverage AWS services such as Amazon Elastic Cloud Compute (Amazon EC2), Amazon Simple Storage Service (Amazon S3).
$35
per month
Pricing
Apache SparkAWS Elastic Beanstalk
Editions & Modules
No answers on this topic
No Charge
$0
Users pay for AWS resources (e.g. EC2, S3 buckets, etc.) used to store and run the application.
Offerings
Pricing Offerings
Apache SparkAWS Elastic Beanstalk
Free Trial
NoNo
Free/Freemium Version
NoYes
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Apache SparkAWS Elastic Beanstalk
Top Pros
Top Cons
Features
Apache SparkAWS Elastic Beanstalk
Platform-as-a-Service
Comparison of Platform-as-a-Service features of Product A and Product B
Apache Spark
-
Ratings
AWS Elastic Beanstalk
9.5
28 Ratings
15% above category average
Ease of building user interfaces00 Ratings10.018 Ratings
Scalability00 Ratings9.928 Ratings
Platform management overhead00 Ratings9.727 Ratings
Workflow engine capability00 Ratings9.522 Ratings
Platform access control00 Ratings9.327 Ratings
Services-enabled integration00 Ratings9.727 Ratings
Development environment creation00 Ratings9.527 Ratings
Development environment replication00 Ratings9.528 Ratings
Issue monitoring and notification00 Ratings9.127 Ratings
Issue recovery00 Ratings9.525 Ratings
Upgrades and platform fixes00 Ratings9.426 Ratings
Best Alternatives
Apache SparkAWS Elastic Beanstalk
Small Businesses

No answers on this topic

AWS Lambda
AWS Lambda
Score 8.8 out of 10
Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
IBM Cloud Private
IBM Cloud Private
Score 9.5 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 8.8 out of 10
IBM Cloud Private
IBM Cloud Private
Score 9.5 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SparkAWS Elastic Beanstalk
Likelihood to Recommend
9.9
(24 ratings)
9.8
(28 ratings)
Likelihood to Renew
10.0
(1 ratings)
7.9
(2 ratings)
Usability
10.0
(3 ratings)
7.7
(9 ratings)
Support Rating
8.7
(4 ratings)
8.0
(12 ratings)
Implementation Rating
-
(0 ratings)
7.0
(2 ratings)
User Testimonials
Apache SparkAWS Elastic Beanstalk
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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Amazon AWS
I have been using AWS Elastic Beanstalk for more than 5 years, and it has made our life so easy and hassle-free. Here are some scenarios where it excels -
  • I have been using different AWS services like EC2, S3, Cloudfront, Serverless, etc. And Elastic Beanstalk makes our lives easier by tieing each service together and making the deployment a smooth process.
  • N number of integrations with different CI/CD pipelines make this most engineer's favourite service.
  • Scalability & Security comes with the service, which makes it the absolute perfect product for your business.
Personally, I haven't found any situations where it's not appropriate for the use cases it can be used. The pricing is also very cost-effective.
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Pros
Apache
  • Apache Spark makes processing very large data sets possible. It handles these data sets in a fairly quick manner.
  • Apache Spark does a fairly good job implementing machine learning models for larger data sets.
  • Apache Spark seems to be a rapidly advancing software, with the new features making the software ever more straight-forward to use.
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Amazon AWS
  • Getting a project set up using the console or CLI is easy compared to other [computing] platforms.
  • AWS Elastic Beanstalk supports a variety of programming languages so teams can experiment with different frameworks but still use the same compute platform for rapid prototyping.
  • Common application architectures can be referenced as patterns during project [setup].
  • Multiple environments can be deployed for an application giving more flexibility for experimentation.
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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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Amazon AWS
  • Limited to the frameworks and configurations that AWS supports. There is no native way to use Elastic Beanstalk to deploy a Go application behind Nginx, for example.
  • It's not always clear what's changed on an underlying system when AWS updates an EB stack; the new version is announced, but AWS does not say what specifically changed in the underlying configuration. This can have unintended consequences and result in additional work in order to figure out what changes were made.
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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Amazon AWS
As our technology grows, it makes more sense to individually provision each server rather than have it done via beanstalk. There are several reasons to do so, which I cannot explain without further diving into the architecture itself, but I can tell you this. With automation, you also loose the flexibility to morph the system for your specific needs. So if you expect that in future you need more customization to your deployment process, then there is a good chance that you might try to do things individually rather than use an automation like beanstalk.
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Usability
Apache
The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.
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Amazon AWS
It is a great tool to manage your applications. You just need to write the codes, and after that with one click, your app will be online and accessible from the internet. That is a huge help for people who do not know about infrastructure or do not want to spend money on maintaining infrastructure.
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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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Amazon AWS
As I described earlier it has been really cost effective and really easy for fellow developers who don't want to waste weeks and weeks into learning and manually deploying stuff which basically takes month to create and go live with the Minimal viable product (MVP). With AWS Beanstalk within a week a developer can go live with the Minimal viable product easily.
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Implementation Rating
Apache
No answers on this topic
Amazon AWS
- Do as many experiments as you can before you commit on using beanstalk or other AWS features. - Keep future state in mind. Think through what comes next, and if that is technically possible to do so. - Always factor in cost in terms of scaling. - We learned a valuable lesson when we wanted to go multi-region, because then we realized many things needs to change in code. So if you plan on using this a lot, factor multiple regions.
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Alternatives Considered
Apache
All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional type programming easily based on the situation. Also it doesn't need special data ingestion or indexing pre-processing like Presto. Combining it with Jupyter Notebooks (https://github.com/jupyter-incubator/sparkmagic), one can develop the Spark code in an interactive manner in Scala or Python
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Amazon AWS
We also use Heroku and it is a great platform for smaller projects and light Node.js services, but we have found that in terms of cost, the Elastic Beanstalk option is more affordable for the projects that we undertake. The fact that it sits inside of the greater AWS Cloud offering also compels us to use it, since integration is simpler. We have also evaluated Microsoft Azure and gave up trying to get an extremely basic implementation up and running after a few days of struggling with its mediocre user interface and constant issues with documentation being outdated. The authentication model is also badly broken and trying to manage resources is a pain. One cannot compare Azure with anything that Amazon has created in the cloud space since Azure really isn't a mature platform and we are always left wanting when we have to interface with it.
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Return on Investment
Apache
  • Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark.
  • Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy.
  • Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs.
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Amazon AWS
  • till now we had not Calculated ROI as the project is still evolving and we had to keep on changing the environment implementation
  • it meets our purpose of quick deployment as compared to on-premises deployment
  • till now we look good as we also controlled our expenses which increased suddenly in the middle of deployment activity
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ScreenShots