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AWS Fargate vs Apache Spark comparison

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Buyer's Guide
AWS Fargate vs. Apache Spark
July 2022
Find out what your peers are saying about AWS Fargate vs. Apache Spark and other solutions. Updated: July 2022.
620,319 professionals have used our research since 2012.
Quotes From Members
We asked business professionals to review the solutions they use.
Here are some excerpts of what they said:
Pros
"The most valuable feature of Apache Spark is its ease of use.""It is useful for handling large amounts of data. It is very useful for scientific purposes.""The solution has been very stable.""One of Apache Spark's most valuable features is that it supports in-memory processing, the execution of jobs compared to traditional tools is very fast.""Spark helps us reduce startup time for our customers and gives a very high ROI in the medium term.""AI libraries are the most valuable. They provide extensibility and usability. Spark has a lot of connectors, which is a very important and useful feature for AI. You need to connect a lot of points for AI, and you have to get data from those systems. Connectors are very wide in Spark. With a Spark cluster, you can get fast results, especially for AI.""I like that it can handle multiple tasks parallelly. I also like the automation feature. JavaScript also helps with the parallel streaming of the library.""Spark can handle small to huge data and is suitable for any size of company."

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"If you create your deployment with a good set of rules for how to scale in, you can just set it and forget it.""I like their containerization service. You can use Docker or something similar and deploy quickly without the know-how related to, for example, Kubernetes. If you use AKS or Kubernetes, then you have to have the know-how. But for Fargate, you don't need to have the know-how there. You just deploy the container or the image, and then you have the container, and you can use it as AWS takes care of the rest. This makes it easier for those getting started or if you don't have a strong DevOps team inside your organization."

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Cons
"It's not easy to install.""Apache Spark could improve the connectors that it supports. There are a lot of open-source databases in the market. For example, cloud databases, such as Redshift, Snowflake, and Synapse. Apache Spark should have connectors present to connect to these databases. There are a lot of workarounds required to connect to those databases, but it should have inbuilt connectors.""The initial setup was not easy.""Stream processing needs to be developed more in Spark. I have used Flink previously. Flink is better than Spark at stream processing.""Spark could be improved by adding support for other open-source storage layers than Delta Lake.""When you are working with large, complex tasks, the garbage collection process is slow and affects performance.""The logging for the observability platform could be better.""Apache Spark can improve the use case scenarios from the website. There is not any information on how you can use the solution across the relational databases toward multiple databases."

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"I heard from my team that it's not easy to predict the cost. That is the only issue we have with AWS Fargate, but I think that's acceptable. AWS Fargate isn't user-friendly. Anything related to Software as a Service or microservice architecture is not easy to implement. You're required to have DevOps from your side to implement the solution. AWS Fargate is just a temporary solution for us. When we grow to a certain level, we may use AKS for better control.""The main area for improvement is the cost, which could be lowered to be more competitive with other major cloud providers."

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Pricing and Cost Advice
  • "Since we are using the Apache Spark version, not the data bricks version, it is an Apache license version, the support and resolution of the bug are actually late or delayed. The Apache license is free."
  • "Spark is an open-source solution, so there are no licensing costs."
  • "Apache Spark is open-source. You have to pay only when you use any bundled product, such as Cloudera."
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    Questions from the Community
    Top Answer:I don't think using Apache Spark without Hadoop has any major drawbacks or issues. I have used Apache Spark quite successfully with AWS S3 on many projects which are batch based. Yes for very high… more »
    Top Answer:The most valuable feature of Apache Spark is its ease of use.
    Top Answer:Spark is an open-source solution, so there are no licensing costs.
    Top Answer:I like their containerization service. You can use Docker or something similar and deploy quickly without the know-how related to, for example, Kubernetes. If you use AKS or Kubernetes, then you have… more »
    Top Answer:I heard from my team that it's not easy to predict the cost. That is the only issue we have with AWS Fargate, but I think that's acceptable. AWS Fargate isn't user-friendly. Anything related to… more »
    Top Answer:What's suitable for potential users depends on their company's IT capability. For a small company, it's best to engage a consultant service for help. For example, we could consult others on the… more »
    Ranking
    2nd
    out of 14 in Compute Service
    Views
    10,395
    Comparisons
    8,264
    Reviews
    11
    Average Words per Review
    405
    Rating
    8.0
    8th
    out of 14 in Compute Service
    Views
    2,978
    Comparisons
    2,533
    Reviews
    2
    Average Words per Review
    327
    Rating
    9.0
    Comparisons
    Learn More
    Overview

    Spark provides programmers with an application programming interface centered on a data structure called the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. It was developed in response to limitations in the MapReduce cluster computing paradigm, which forces a particular linear dataflowstructure on distributed programs: MapReduce programs read input data from disk, map a function across the data, reduce the results of the map, and store reduction results on disk. Spark's RDDs function as a working set for distributed programs that offers a (deliberately) restricted form of distributed shared memory

    A new compute engine that enables you to use containers as a fundamental compute primitive without having to manage the underlying instances. With Fargate, you don’t need to provision, configure, or scale virtual machines in your clusters to run containers. Fargate can be used with Amazon ECS today, with plans to support Amazon Elastic Container Service for Kubernetes (Amazon EKS) in the future.

    Fargate has flexible configuration options so you can closely match your application needs and granular, per-second billing.

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    Learn more about Apache Spark
    Learn more about AWS Fargate
    Sample Customers
    NASA JPL, UC Berkeley AMPLab, Amazon, eBay, Yahoo!, UC Santa Cruz, TripAdvisor, Taboola, Agile Lab, Art.com, Baidu, Alibaba Taobao, EURECOM, Hitachi Solutions
    Expedia, Intuit, Royal Dutch Shell, Brooks Brothers
    Top Industries
    REVIEWERS
    Financial Services Firm33%
    Computer Software Company25%
    Marketing Services Firm8%
    Non Profit8%
    VISITORS READING REVIEWS
    Computer Software Company20%
    Comms Service Provider19%
    Financial Services Firm14%
    Media Company6%
    VISITORS READING REVIEWS
    Media Company23%
    Computer Software Company20%
    Comms Service Provider18%
    Financial Services Firm8%
    Company Size
    REVIEWERS
    Small Business43%
    Midsize Enterprise21%
    Large Enterprise36%
    VISITORS READING REVIEWS
    Small Business15%
    Midsize Enterprise15%
    Large Enterprise70%
    VISITORS READING REVIEWS
    Small Business15%
    Midsize Enterprise13%
    Large Enterprise72%
    Buyer's Guide
    AWS Fargate vs. Apache Spark
    July 2022
    Find out what your peers are saying about AWS Fargate vs. Apache Spark and other solutions. Updated: July 2022.
    620,319 professionals have used our research since 2012.

    Apache Spark is ranked 2nd in Compute Service with 11 reviews while AWS Fargate is ranked 8th in Compute Service with 2 reviews. Apache Spark is rated 8.0, while AWS Fargate is rated 9.0. The top reviewer of Apache Spark writes "Provides fast aggregations, AI libraries, and a lot of connectors". On the other hand, the top reviewer of AWS Fargate writes "A serverless, pay-as-you-go compute engine that you can deploy quickly". Apache Spark is most compared with Spring Boot, Azure Stream Analytics, AWS Lambda, AWS Batch and Spring MVC, whereas AWS Fargate is most compared with Amazon EC2 Auto Scaling, AWS Batch, Amazon EC2, Apache NiFi and Amazon Elastic Inference. See our AWS Fargate vs. Apache Spark report.

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    We monitor all Compute Service reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.