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  1. Stackups
  2. Application & Data
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  4. Big Data As A Service
  5. Airflow vs Amazon EMR

Airflow vs Amazon EMR

OverviewDecisionsComparisonAlternatives

Overview

Amazon EMR
Amazon EMR
Stacks543
Followers682
Votes54
Airflow
Airflow
Stacks1.7K
Followers2.8K
Votes128

Airflow vs Amazon EMR: What are the differences?

Introduction

Airflow and Amazon EMR are two popular tools used for data processing and workflow management. While they have some similarities, they also have key differences that set them apart from each other.

  1. Architecture: The architecture of Airflow and Amazon EMR is a major difference between the two. Airflow is a task scheduler and workflow management system that allows users to define, schedule, and monitor complex workflows. It uses a Directed Acyclic Graph (DAG) to represent workflows and executes tasks in parallel or sequentially based on dependencies. On the other hand, Amazon EMR is a cloud-based big data processing service that uses a cluster-based architecture for data processing and analysis. It allows users to run distributed frameworks such as Hadoop, Spark, and Hive on a managed cluster of Amazon EC2 instances.

  2. Supported Use Cases: Airflow is primarily focused on workflow management and scheduling, making it suitable for data processing pipelines, ETL (Extract, Transform, Load) jobs, and other task automation scenarios. It provides a rich set of capabilities for workflow orchestration, including error handling, retries, and monitoring. Amazon EMR, on the other hand, is designed for big data processing and analysis. It is well-suited for processing large volumes of data using distributed compute engines like Hadoop and Spark. It provides pre-configured clusters with optimized performance for specific use cases, such as log analysis, machine learning, and batch processing.

  3. Scalability and Elasticity: The scalability and elasticity of Airflow and Amazon EMR differ significantly. Airflow can scale horizontally by adding more workers to handle increased task load, but it does not have built-in elasticity features. On the other hand, Amazon EMR provides automatic scaling capabilities, allowing users to dynamically add or remove cluster nodes based on the workload. This makes Amazon EMR more suitable for handling variable and unpredictable workloads, while Airflow may require manual scaling and resource management.

  4. Infrastructure Management: Airflow does not have built-in infrastructure management capabilities and requires users to provision and manage their own compute resources. In contrast, Amazon EMR handles the infrastructure management aspect by providing managed compute clusters. It abstracts the complexities of managing underlying infrastructure, such as provisioning and configuring EC2 instances, and allows users to focus on their data processing tasks.

  5. Integration with Cloud Services: Airflow has a wide range of integrations with various services and platforms, including cloud providers like AWS, Google Cloud, and Microsoft Azure. It provides operators and hooks for interacting with these services, making it easy to incorporate cloud-based services into workflows. Amazon EMR, being an AWS service, integrates seamlessly with other AWS services such as S3, Redshift, and DynamoDB. It provides direct access to these services for data storage, querying, and analysis.

  6. Cost Model: Airflow is an open-source software and does not have direct usage-based pricing. However, users have to bear the cost of infrastructure resources needed to run and scale Airflow, along with any additional costs of integrating and using third-party services. On the other hand, Amazon EMR has a pay-as-you-go pricing model, where users are billed based on the number and type of instances used in the cluster, along with any additional AWS service charges. This allows users to have more control over cost optimization and pay only for the resources they need.

In Summary, Airflow is a workflow management system focused on task scheduling and automation, while Amazon EMR is a cloud-based big data processing service. Airflow provides flexibility and ease of integration with various platforms, while Amazon EMR offers built-in scalability, infrastructure management, and seamless integration with other AWS services. The choice between the two depends on the specific use case and requirements of the data processing workflow.

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Advice on Amazon EMR, Airflow

Anonymous
Anonymous

Jan 19, 2020

Needs advice

I am so confused. I need a tool that will allow me to go to about 10 different URLs to get a list of objects. Those object lists will be hundreds or thousands in length. I then need to get detailed data lists about each object. Those detailed data lists can have hundreds of elements that could be map/reduced somehow. My batch process dies sometimes halfway through which means hours of processing gone, i.e. time wasted. I need something like a directed graph that will keep results of successful data collection and allow me either pragmatically or manually to retry the failed ones some way (0 - forever) times. I want it to then process all the ones that have succeeded or been effectively ignored and load the data store with the aggregation of some couple thousand data-points. I know hitting this many endpoints is not a good practice but I can't put collectors on all the endpoints or anything like that. It is pretty much the only way to get the data.

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Comments

Detailed Comparison

Amazon EMR
Amazon EMR
Airflow
Airflow

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

Use Airflow to author workflows as directed acyclic graphs (DAGs) of tasks. The Airflow scheduler executes your tasks on an array of workers while following the specified dependencies. Rich command lines utilities makes performing complex surgeries on DAGs a snap. The rich user interface makes it easy to visualize pipelines running in production, monitor progress and troubleshoot issues when needed.

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.
Dynamic: Airflow pipelines are configuration as code (Python), allowing for dynamic pipeline generation. This allows for writting code that instantiate pipelines dynamically.;Extensible: Easily define your own operators, executors and extend the library so that it fits the level of abstraction that suits your environment.;Elegant: Airflow pipelines are lean and explicit. Parameterizing your scripts is built in the core of Airflow using powerful Jinja templating engine.;Scalable: Airflow has a modular architecture and uses a message queue to talk to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.
Statistics
Stacks
543
Stacks
1.7K
Followers
682
Followers
2.8K
Votes
54
Votes
128
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
  • 53
    Features
  • 14
    Task Dependency Management
  • 12
    Cluster of workers
  • 12
    Beautiful UI
  • 10
    Extensibility
Cons
  • 2
    Open source - provides minimum or no support
  • 2
    Running it on kubernetes cluster relatively complex
  • 2
    Observability is not great when the DAGs exceed 250
  • 1
    Logical separation of DAGs is not straight forward

What are some alternatives to Amazon EMR, Airflow?

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.

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.

GitHub Actions

GitHub Actions

It makes it easy to automate all your software workflows, now with world-class CI/CD. Build, test, and deploy your code right from GitHub. Make code reviews, branch management, and issue triaging work the way you want.

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.

Apache Beam

Apache Beam

It implements batch and streaming data processing jobs that run on any execution engine. It executes pipelines on multiple execution environments.

Stitch

Stitch

Stitch is a simple, powerful ETL service built for software developers. Stitch evolved out of RJMetrics, a widely used business intelligence platform. When RJMetrics was acquired by Magento in 2016, Stitch was launched as its own company.

Zenaton

Zenaton

Developer framework to orchestrate multiple services and APIs into your software application using logic triggered by events and time. Build ETL processes, A/B testing, real-time alerts and personalized user experiences with custom logic.

Azure Synapse

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

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