Overview

AWS Data Pipeline

Stacks95

Followers398

Votes1

Azure Pipelines

Stacks1.2K

Followers457

Votes14

AWS Data Pipeline vs Azure Pipelines: What are the differences?

Introduction

AWS Data Pipeline and Azure Pipelines are two cloud-based services that provide solutions for orchestrating and automating data workflows. While they serve similar purposes, there are key differences between them that differentiate their functionalities and features.

Pricing and Cost Structure: AWS Data Pipeline follows a pay-as-you-go model, charging users based on the number of pipeline runs, resources used, and the data volume processed. On the other hand, Azure Pipelines offers a variety of pricing options, including a free tier for open-source projects and flexible pricing plans based on minutes consumed for builds and releases.
Platform Compatibility: AWS Data Pipeline primarily focuses on integrating and orchestrating data workflows within the AWS ecosystem. It seamlessly integrates with various AWS services such as Amazon S3, Amazon Redshift, and Amazon EMR. In contrast, Azure Pipelines is designed to support multi-platform environments, including both cloud-native and on-premises solutions. It provides seamless integration with Azure services as well as third-party tools and repositories.
Data Transformation Capabilities: AWS Data Pipeline offers a wide range of pre-built activities and data transformers to manipulate and transform data throughout the pipeline. It allows users to create custom data transformation scripts using Shell commands or AWS CLI. Azure Pipelines, on the other hand, provides a flexible and extensible platform that supports custom data transformation using a variety of programming languages, scripting tools, and task-based operations.
Ease of Use and User Interface: AWS Data Pipeline provides a user-friendly web-based console that allows users to easily create, configure, and monitor pipelines. It offers a drag-and-drop interface for visually designing workflows. Azure Pipelines also provides a web-based interface, along with a YAML-based declarative language for defining pipelines as code. This allows users to version control and maintain their pipeline configurations and easily reproduce and deploy pipelines.
Integration with Source Code Repositories: AWS Data Pipeline supports integration with AWS CodeCommit, allowing users to version control and manage pipeline definitions alongside their application code. Additionally, it integrates with AWS CloudFormation for infrastructure management. Azure Pipelines offers seamless integration with popular source code repositories such as Azure Repos, GitHub, and Bitbucket. It allows users to trigger pipeline runs based on code commits and pull requests, enabling continuous integration and delivery workflows.
Extensibility and Ecosystem: AWS Data Pipeline provides a robust ecosystem of AWS services and third-party connectors, allowing users to leverage a wide range of data processing, storage, and analytics tools within their pipelines. Azure Pipelines also offers an extensive marketplace of extensions and integrations, allowing users to extend pipeline capabilities and integrate with external tools and services.

In summary, AWS Data Pipeline and Azure Pipelines differ in terms of pricing models, platform compatibility, data transformation capabilities, user interface, source code repository integration, and ecosystem support. While AWS Data Pipeline is more focused on AWS-centric workflows, Azure Pipelines offers a broader range of platform compatibility and flexibility in terms of pipeline design and customization.

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Advice on AWS Data Pipeline, Azure Pipelines

Balaramesh

Apr 20, 2020

Needs adviceon

Azure Pipelines

.NET

Jenkins

We are currently using Azure Pipelines for continous integration. Our applications are developed witn .NET framework. But when we look at the online Jenkins is the most widely used tool for continous integration. Can you please give me the advice which one is best to use for my case Azure pipeline or jenkins.

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Comments

Detailed Comparison

AWS Data Pipeline	Azure Pipelines
AWS Data Pipeline is a web service that provides a simple management system for data-driven workflows. Using AWS Data Pipeline, you define a pipeline composed of the “data sources” that contain your data, the “activities” or business logic such as EMR jobs or SQL queries, and the “schedule” on which your business logic executes. For example, you could define a job that, every hour, runs an Amazon Elastic MapReduce (Amazon EMR)–based analysis on that hour’s Amazon Simple Storage Service (Amazon S3) log data, loads the results into a relational database for future lookup, and then automatically sends you a daily summary email.	Fast builds with parallel jobs and test execution. Use container jobs to create consistent and reliable builds with the exact tools you need. Create new containers with ease and push them to any registry.
You can find (and use) a variety of popular AWS Data Pipeline tasks in the AWS Management Console’s template section.;Hourly analysis of Amazon S3‐based log data;Daily replication of AmazonDynamoDB data to Amazon S3;Periodic replication of on-premise JDBC database tables into RDS	Any language, any platform; Containers and Kubernetes; Extensible; Deploy to any cloud; Open source; Advanced workflows and features
Statistics
Stacks 95	Stacks 1.2K
Followers 398	Followers 457
Votes 1	Votes 14
Pros & Cons
Pros 1 Easy to create DAG and execute it	Pros 4 Easy to get started 3 Built by Microsoft 3 Unlimited CI/CD minutes 2 Docker support 2 Yaml support
Integrations
No integrations available	.NET Core Slack Python Ruby Kubernetes .NET Node.js Linux Microsoft Azure RxJava

What are some alternatives to AWS Data Pipeline, Azure Pipelines?

Jenkins

In a nutshell Jenkins CI is the leading open-source continuous integration server. Built with Java, it provides over 300 plugins to support building and testing virtually any project.

Travis CI

Free for open source projects, our CI environment provides multiple runtimes (e.g. Node.js or PHP versions), data stores and so on. Because of this, hosting your project on travis-ci.com means you can effortlessly test your library or applications against multiple runtimes and data stores without even having all of them installed locally.

Codeship

Codeship runs your automated tests and configured deployment when you push to your repository. It takes care of managing and scaling the infrastructure so that you are able to test and release more frequently and get faster feedback for building the product your users need.

CircleCI

Continuous integration and delivery platform helps software teams rapidly release code with confidence by automating the build, test, and deploy process. Offers a modern software development platform that lets teams ramp.

TeamCity

TeamCity is a user-friendly continuous integration (CI) server for professional developers, build engineers, and DevOps. It is trivial to setup and absolutely free for small teams and open source projects.

Drone.io

Drone is a hosted continuous integration service. It enables you to conveniently set up projects to automatically build, test, and deploy as you make changes to your code. Drone integrates seamlessly with Github, Bitbucket and Google Code as well as third party services such as Heroku, Dotcloud, Google AppEngine and more.

wercker

Wercker is a CI/CD developer automation platform designed for Microservices & Container Architecture.

GoCD

GoCD is an open source continuous delivery server created by ThoughtWorks. GoCD offers business a first-class build and deployment engine for complete control and visibility.

Shippable

Shippable is a SaaS platform that lets you easily add Continuous Integration/Deployment to your Github and BitBucket repositories. It is lightweight, super simple to setup, and runs your builds and tests faster than any other service.

Buildkite

CI and build automation tool that combines the power of your own build infrastructure with the convenience of a managed, centralized web UI. Used by Shopify, Basecamp, Digital Ocean, Venmo, Cochlear, Bugsnag and more.