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  5. Amazon EMR vs Serverless

Amazon EMR vs Serverless

OverviewDecisionsComparisonAlternatives

Overview

Amazon EMR
Amazon EMR
Stacks543
Followers682
Votes54
Serverless
Serverless
Stacks2.2K
Followers1.2K
Votes28
GitHub Stars46.9K
Forks5.7K

Amazon EMR vs Serverless: What are the differences?

Amazon EMR and Serverless serve different purposes in the cloud computing landscape. Here are six key differences between them:

  1. Computing Paradigm: Amazon EMR follows a traditional, cluster-based computing paradigm. EMR provides a fully managed Hadoop and Spark framework, allowing users to process large datasets using clusters of virtual servers. Serverless is associated with serverless computing, where the focus is on executing individual functions in response to events without the need to manage server infrastructure.

  2. Use Case: Amazon EMR is primarily designed for big data processing and analytics tasks. It is suitable for distributed processing of large datasets using frameworks like Apache Spark, Apache Hadoop, etc. Serverless is often used for event-driven computing, microservices architecture, and functions as a service (FaaS). It's suitable for executing small, stateless functions in response to events.

  3. Resource Management: Amazon EMR involves managing and configuring clusters of virtual machines, allowing for fine-tuning of computing resources based on the workload. Serverless abstracts away server management entirely. Users define and deploy functions, and the underlying infrastructure is automatically provisioned by the serverless platform.

  4. Granularity of Scaling: Amazon EMR scales by adding or removing entire clusters of virtual machines. It's a more coarse-grained scaling approach. Serverless scales at a fine-grained level, with each function being individually scalable. It automatically adjusts the number of function instances based on demand.

  5. Cost Model: Amazon EMR typically involves paying for the provisioned cluster capacity, even if the resources are not fully utilized. Costs are more predictable but may be less efficient for sporadic workloads. Serverless follows a pay-as-you-go model where users are billed based on the actual compute resources consumed by functions. It can be more cost-effective for intermittent or low-traffic workloads.

  6. Ease of Management: Amazon EMR provides more control over cluster configuration and management, suitable for users who require specific optimizations or customizations. Serverless abstracts away much of the infrastructure management complexity, making it easier for developers to focus on writing code without dealing with server provisioning and scaling concerns.

In summary, Amazon EMR is a fully managed big data processing service, ideal for distributed analytics tasks using clusters. In contrast, Serverless is associated with serverless computing, offering a fine-grained, event-driven paradigm where users deploy individual functions without managing underlying infrastructure, making it well-suited for microservices and event-driven architectures.

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Advice on Amazon EMR, Serverless

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
Serverless
Serverless

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

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.

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.
-
Statistics
GitHub Stars
-
GitHub Stars
46.9K
GitHub Forks
-
GitHub Forks
5.7K
Stacks
543
Stacks
2.2K
Followers
682
Followers
1.2K
Votes
54
Votes
28
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
  • 14
    API integration
  • 7
    Supports cloud functions for Google, Azure, and IBM
  • 3
    Lower cost
  • 1
    Auto scale
  • 1
    3. Simplified Management for developers to focus on cod
Integrations
No integrations available
Azure Functions
Azure Functions
AWS Lambda
AWS Lambda
Amazon API Gateway
Amazon API Gateway

What are some alternatives to Amazon EMR, Serverless?

AWS Lambda

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.

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.

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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