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  5. AWS Lambda vs Amazon EMR

AWS Lambda vs Amazon EMR

OverviewDecisionsComparisonAlternatives

Overview

Amazon EMR
Amazon EMR
Stacks543
Followers682
Votes54
AWS Lambda
AWS Lambda
Stacks26.0K
Followers18.8K
Votes432

AWS Lambda vs Amazon EMR: What are the differences?

AWS Lambda and Amazon EMR are both services provided by Amazon Web Services (AWS) that offer compute capabilities for different purposes. Let's explore the key differences between them.

  1. Use Case: AWS Lambda is a serverless compute service that allows you to run code without provisioning or managing servers. It is ideal for executing small, event-driven functions and building serverless applications. On the other hand, Amazon EMR is a managed cluster platform that enables you to process large amounts of data using frameworks like Apache Hadoop, Spark, and Presto. It is designed for big data processing and analytics.

  2. Scalability and Flexibility: AWS Lambda automatically scales the execution of functions in response to incoming events. It can handle a high number of concurrent requests and scale down to zero when there is no traffic. In contrast, Amazon EMR allows you to provision and manage a cluster of virtual servers to process large-scale data. You can scale the cluster up or down based on your processing needs.

  3. Execution Time and Latency: AWS Lambda functions have a maximum execution time of 15 minutes. They are optimized for short-running tasks and provide low-latency compute resources. On the other hand, Amazon EMR jobs can run for a longer period, ranging from minutes to hours or even days, depending on the complexity of the processing task. However, EMR jobs may have higher latency compared to Lambda functions due to the nature of distributed processing.

  4. Cost Model: AWS Lambda follows a pay-as-you-go pricing model where you are billed based on the number of invocations and the execution duration of functions. It is suitable for workloads with sporadic or unpredictable traffic patterns. Amazon EMR, on the other hand, follows a more traditional pricing model where you pay for the EC2 instances in the cluster, storage, and other associated services. It is designed for workloads that require continuous processing and have predictable resource requirements.

  5. Managed vs. Fully Managed: While both AWS Lambda and Amazon EMR are managed services, there is a difference in the level of management provided. AWS Lambda is a fully managed service where you only need to focus on writing and deploying your code. Amazon EMR is also a managed service, but you have more control and responsibility over the configuration and management of the underlying infrastructure.

  6. Supported Frameworks: AWS Lambda supports a variety of programming languages, including JavaScript, Python, Java, C#, and Go. It integrates well with other AWS services and can be easily used in serverless architectures. On the other hand, Amazon EMR supports popular big data frameworks like Apache Hadoop, Spark, and Presto. It provides the flexibility to use the tools and libraries that are commonly used in the big data ecosystem.

In summary, AWS Lambda is a serverless compute service that is ideal for small, event-driven functions and serverless applications. Amazon EMR, on the other hand, is a managed cluster platform designed for big data processing and analytics.

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Advice on Amazon EMR, AWS Lambda

Tim
Tim

CTO at Checkly Inc.

Sep 18, 2019

Needs adviceonHerokuHerokuAWS LambdaAWS 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

357k views357k
Comments

Detailed Comparison

Amazon EMR
Amazon EMR
AWS Lambda
AWS Lambda

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

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.

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.;Hadoop Tools- EMR supports powerful and proven Hadoop tools such as Hive, Pig, and HBase.
Extend other AWS services with custom logic;Build custom back-end services;Completely Automated Administration;Built-in Fault Tolerance;Automatic Scaling;Integrated Security Model;Bring Your Own Code;Pay Per Use;Flexible Resource Model
Statistics
Stacks
543
Stacks
26.0K
Followers
682
Followers
18.8K
Votes
54
Votes
432
Pros & Cons
Pros
  • 15
    On demand processing power
  • 12
    Don't need to maintain Hadoop Cluster yourself
  • 7
    Hadoop Tools
  • 6
    Elastic
  • 4
    Backed by Amazon
Pros
  • 129
    No infrastructure
  • 83
    Cheap
  • 70
    Quick
  • 59
    Stateless
  • 47
    No deploy, no server, great sleep
Cons
  • 7
    Cant execute ruby or go
  • 3
    Compute time limited
  • 1
    Can't execute PHP w/o significant effort

What are some alternatives to Amazon EMR, AWS Lambda?

Google BigQuery

Google BigQuery

Run super-fast, SQL-like queries against terabytes of data in seconds, using the processing power of Google's infrastructure. Load data with ease. Bulk load your data using Google Cloud Storage or stream it in. Easy access. Access BigQuery by using a browser tool, a command-line tool, or by making calls to the BigQuery REST API with client libraries such as Java, PHP or Python.

Amazon Redshift

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.

Qubole

Qubole

Qubole is a cloud based service that makes big data easy for analysts and data engineers.

Azure Functions

Azure Functions

Azure Functions is an event driven, compute-on-demand experience that extends the existing Azure application platform with capabilities to implement code triggered by events occurring in virtually any Azure or 3rd party service as well as on-premises systems.

Google Cloud Run

Google Cloud Run

A managed compute platform that enables you to run stateless containers that are invocable via HTTP requests. It's serverless by abstracting away all infrastructure management.

Serverless

Serverless

Build applications comprised of microservices that run in response to events, auto-scale for you, and only charge you when they run. This lowers the total cost of maintaining your apps, enabling you to build more logic, faster. The Framework uses new event-driven compute services, like AWS Lambda, Google CloudFunctions, and more.

Altiscale

Altiscale

we run Apache Hadoop for you. We not only deploy Hadoop, we monitor, manage, fix, and update it for you. Then we take it a step further: We monitor your jobs, notify you when something’s wrong with them, and can help with tuning.

Snowflake

Snowflake

Snowflake eliminates the administration and management demands of traditional data warehouses and big data platforms. Snowflake is a true data warehouse as a service running on Amazon Web Services (AWS)—no infrastructure to manage and no knobs to turn.

Google Cloud Functions

Google Cloud Functions

Construct applications from bite-sized business logic billed to the nearest 100 milliseconds, only while your code is running

Knative

Knative

Knative provides a set of middleware components that are essential to build modern, source-centric, and container-based applications that can run anywhere: on premises, in the cloud, or even in a third-party data center

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