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Amazon EMR

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Amazon EMR vs AWS Lambda: What are the differences?

Developers describe Amazon EMR as "Distribute your data and processing across a Amazon EC2 instances using Hadoop". Amazon EMR is used in a variety of applications, including log analysis, web indexing, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics. Customers launch millions of Amazon EMR clusters every year. On the other hand, AWS Lambda is detailed as "Automatically run code in response to modifications to objects in Amazon S3 buckets, messages in Kinesis streams, or updates in DynamoDB". AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security.

Amazon EMR can be classified as a tool in the "Big Data as a Service" category, while AWS Lambda is grouped under "Serverless / Task Processing".

Some of the features offered by Amazon EMR are:

  • Elastic- Amazon EMR enables you to quickly and easily provision as much capacity as you need and add or remove capacity at any time. Deploy multiple clusters or resize a running cluster
  • Low Cost- Amazon EMR is designed to reduce the cost of processing large amounts of data. Some of the features that make it low cost include low hourly pricing, Amazon EC2 Spot integration, Amazon EC2 Reserved Instance integration, elasticity, and Amazon S3 integration.
  • Flexible Data Stores- With Amazon EMR, you can leverage multiple data stores, including Amazon S3, the Hadoop Distributed File System (HDFS), and Amazon DynamoDB.

On the other hand, AWS Lambda provides the following key features:

  • Extend other AWS services with custom logic
  • Build custom back-end services
  • Completely Automated Administration

"On demand processing power" is the primary reason why developers consider Amazon EMR over the competitors, whereas "No infrastructure" was stated as the key factor in picking AWS Lambda.

According to the StackShare community, AWS Lambda has a broader approval, being mentioned in 1023 company stacks & 613 developers stacks; compared to Amazon EMR, which is listed in 95 company stacks and 18 developer stacks.

Decisions about Amazon EMR and AWS Lambda

When adding a new feature to Checkly rearchitecting some older piece, I tend to pick Heroku for rolling it out. But not always, because sometimes I pick AWS Lambda . The short story:

  • Developer Experience trumps everything.
  • AWS Lambda is cheap. Up to a limit though. This impact not only your wallet.
  • If you need geographic spread, AWS is lonely at the top.
The setup

Recently, I was doing a brainstorm at a startup here in Berlin on the future of their infrastructure. They were ready to move on from their initial, almost 100% Ec2 + Chef based setup. Everything was on the table. But we crossed out a lot quite quickly:

  • Pure, uncut, self hosted Kubernetes — way too much complexity
  • Managed Kubernetes in various flavors — still too much complexity
  • Zeit — Maybe, but no Docker support
  • Elastic Beanstalk — Maybe, bit old but does the job
  • Heroku
  • Lambda

It became clear a mix of PaaS and FaaS was the way to go. What a surprise! That is exactly what I use for Checkly! But when do you pick which model?

I chopped that question up into the following categories:

  • Developer Experience / DX 🤓
  • Ops Experience / OX 🐂 (?)
  • Cost 💵
  • Lock in 🔐

Read the full post linked below for all details

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Pros of Amazon EMR
Pros of AWS Lambda
  • 15
    On demand processing power
  • 12
    Don't need to maintain Hadoop Cluster yourself
  • 7
    Hadoop Tools
  • 6
    Elastic
  • 4
    Backed by Amazon
  • 3
    Flexible
  • 3
    Economic - pay as you go, easy to use CLI and SDKs
  • 2
    Don't need a dedicated Ops group
  • 1
    Great support
  • 1
    Massive data handling
  • 127
    No infrastructure
  • 82
    Cheap
  • 69
    Quick
  • 57
    Stateless
  • 47
    No deploy, no server, great sleep
  • 9
    AWS Lambda went down taking many sites with it
  • 6
    Event Driven Governance
  • 5
    Auto scale and cost effective
  • 5
    Extensive API
  • 5
    Easy to deploy
  • 4
    VPC Support
  • 2
    Integrated with various AWS services

Sign up to add or upvote prosMake informed product decisions

Cons of Amazon EMR
Cons of AWS Lambda
    Be the first to leave a con
    • 5
      Cant execute ruby or go
    • 0
      Can't execute PHP w/o significant effort

    Sign up to add or upvote consMake informed product decisions

    What is Amazon EMR?

    It is used in a variety of applications, including log analysis, data warehousing, machine learning, financial analysis, scientific simulation, and bioinformatics.

    What is AWS Lambda?

    AWS Lambda is a compute service that runs your code in response to events and automatically manages the underlying compute resources for you. You can use AWS Lambda to extend other AWS services with custom logic, or create your own back-end services that operate at AWS scale, performance, and security.

    Need advice about which tool to choose?Ask the StackShare community!

    What companies use Amazon EMR?
    What companies use AWS Lambda?
    See which teams inside your own company are using Amazon EMR or AWS Lambda.
    Sign up for Private StackShareLearn More

    Sign up to get full access to all the companiesMake informed product decisions

    What tools integrate with Amazon EMR?
    What tools integrate with AWS Lambda?

    Sign up to get full access to all the tool integrationsMake informed product decisions

    Blog Posts

    Aug 28 2019 at 3:10AM

    Segment

    PythonJavaAmazon S3+16
    5
    2144
    GitHubPythonNode.js+47
    50
    69438
    GitHubDockerAmazon EC2+23
    12
    6360
    JavaScriptGitHubPython+42
    52
    19908
    GitHubSlackMySQL+44
    109
    49985
    What are some alternatives to Amazon EMR and AWS Lambda?
    Amazon EC2
    It is a web service that provides resizable compute capacity in the cloud. It is designed to make web-scale computing easier for developers.
    Hadoop
    The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage.
    Amazon DynamoDB
    With it , you can offload the administrative burden of operating and scaling a highly available distributed database cluster, while paying a low price for only what you use.
    Amazon Redshift
    It is optimized for data sets ranging from a few hundred gigabytes to a petabyte or more and costs less than $1,000 per terabyte per year, a tenth the cost of most traditional data warehousing solutions.
    Azure HDInsight
    It is a cloud-based service from Microsoft for big data analytics that helps organizations process large amounts of streaming or historical data.
    See all alternatives