AWS Batch vs Apache Storm comparison

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363 views|303 comparisons
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7,086 views|6,771 comparisons
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Executive Summary

We performed a comparison between Apache Storm and AWS Batch based on real PeerSpot user reviews.

Find out what your peers are saying about Amazon Web Services (AWS), Apache, Zadara and others in Compute Service.
To learn more, read our detailed Compute Service Report (Updated: April 2024).
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Pricing and Cost Advice
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  • "AWS Batch's pricing is good."
  • "The pricing is very fair."
  • "AWS Batch is a cheap solution."
  • More AWS Batch Pricing and Cost Advice →

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    Top Answer:AWS Lambda is a serverless solution. It doesn’t require any infrastructure, which allows for cost savings. There is no setup process to deal with, as the entire solution is in the cloud. If you use… more »
    Top Answer:AWS Batch manages the execution of computing workload, including job scheduling, provisioning, and scaling.
    Ranking
    14th
    out of 16 in Compute Service
    Views
    363
    Comparisons
    303
    Reviews
    0
    Average Words per Review
    0
    Rating
    N/A
    3rd
    out of 16 in Compute Service
    Views
    7,086
    Comparisons
    6,771
    Reviews
    4
    Average Words per Review
    973
    Rating
    9.0
    Comparisons
    Also Known As
    Amazon Batch
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    Overview
    Apache Storm is a free and open source distributed realtime computation system. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm is simple and can be used with any programming language.

    AWS Batch enables developers, scientists, and engineers to easily and efficiently run hundreds of thousands of batch computing jobs on AWS. AWS Batch dynamically provisions the optimal quantity and type of compute resources (e.g., CPU or memory optimized instances) based on the volume and specific resource requirements of the batch jobs submitted. With AWS Batch, there is no need to install and manage batch computing software or server clusters that you use to run your jobs, allowing you to focus on analyzing results and solving problems. AWS Batch plans, schedules, and executes your batch computing workloads across the full range of AWS compute services and features, such as Amazon EC2 and Spot Instances.

    Sample Customers
    Groupon, Spotify, The Weather Channel, Twitter, FullContact
    Hess, Expedia, Kelloggs, Philips, HyperTrack
    Top Industries
    VISITORS READING REVIEWS
    Financial Services Firm24%
    Computer Software Company16%
    University10%
    Retailer7%
    VISITORS READING REVIEWS
    Financial Services Firm25%
    Computer Software Company13%
    Manufacturing Company6%
    Insurance Company5%
    Company Size
    VISITORS READING REVIEWS
    Small Business13%
    Midsize Enterprise9%
    Large Enterprise79%
    VISITORS READING REVIEWS
    Small Business16%
    Midsize Enterprise12%
    Large Enterprise73%
    Buyer's Guide
    Compute Service
    April 2024
    Find out what your peers are saying about Amazon Web Services (AWS), Apache, Zadara and others in Compute Service. Updated: April 2024.
    768,740 professionals have used our research since 2012.

    Apache Storm is ranked 14th in Compute Service while AWS Batch is ranked 3rd in Compute Service with 4 reviews. Apache Storm is rated 0.0, while AWS Batch is rated 9.0. On the other hand, the top reviewer of AWS Batch writes "User-friendly, good customization and offers exceptional scalability, allowing users to run jobs ranging from 32 cores to over 2,000 cores". Apache Storm is most compared with Apache NiFi, Azure Stream Analytics and AWS Lambda, whereas AWS Batch is most compared with AWS Lambda, Apache Spark, AWS Fargate, Oracle Compute Cloud Service and Amazon EC2 Auto Scaling.

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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.