Argo vs Avo

Overview

Argo

Stacks763

Followers471

Votes6

Avo

Stacks29

Followers22

Votes0

Argo vs Avo: What are the differences?

# Introduction
When comparing Argo and Avo, it's important to understand the key differences between these two popular tools for data governance and metadata management.

1. **Integration Capabilities**: Argo offers seamless integration with popular cloud storage providers like AWS S3 and Google Cloud Storage, while Avo has a more versatile approach with integrations with BI tools like Looker and Tableau.
   
2. **User Interface**: Avo boasts a user-friendly interface with drag-and-drop functionality for easy metadata management, whereas Argo provides a more technical interface that caters to users comfortable with code and scripts.

3. **Data Lineage Visualization**: Argo visualizes data lineage in a hierarchical tree structure, making it easier for users to track the flow of data, while Avo focuses on providing lineage information in a more detailed, granular format with interactive features for drilling down into specific details.

4. **Collaboration Features**: Avo emphasizes collaboration with features like commenting and sharing metadata assets with team members, fostering better communication and teamwork, whereas Argo focuses more on individual data management tasks without extensive collaboration tools.

5. **Metadata Versioning**: Argo supports metadata versioning, allowing users to track changes over time and revert to previous versions if needed, while Avo lacks this feature, making it potentially more challenging to manage metadata changes and history.

6. **Governance Policies**: Argo provides robust governance policies for enforcing data quality standards and access controls, ensuring data integrity and compliance, whereas Avo offers basic governance functionalities with room for customization based on user needs.

In Summary, the key differences between Argo and Avo lie in their integration capabilities, user interface, data lineage visualization, collaboration features, metadata versioning, and governance policies.

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CLI (Node.js)

Manual

Detailed Comparison

Argo	Avo
Argo is an open source container-native workflow engine for getting work done on Kubernetes. Argo is implemented as a Kubernetes CRD (Custom Resource Definition).	A code-generated, type-safe tracking library to accurately implement analytics events that are defined and maintained in a single-source-of-truth web app. Built to optimize the experience of maintaining and version controlling complicated event schemas.
DAG or Steps based declaration of workflows;Artifact support (S3, Artifactory, HTTP, Git, raw);Step level input & outputs (artifacts/parameters);Loops;Parameterization;Conditionals;Timeouts (step & workflow level);Retry (step & workflow level);Resubmit (memoized);Suspend & Resume;Cancellation;K8s resource orchestration;Exit Hooks (notifications, cleanup);Garbage collection of completed workflow;Scheduling (affinity/tolerations/node selectors);Volumes (ephemeral/existing);Parallelism limits;Daemoned steps;DinD (docker-in-docker);Script steps	-
Statistics
Stacks 763	Stacks 29
Followers 471	Followers 22
Votes 6	Votes 0
Pros & Cons
Pros 3 Open Source 2 Autosinchronize the changes to deploy 1 Online service, no need to install anything	No community feedback yet
Integrations
Kubernetes Docker	Firebase FullStory Intercom Segment Amplitude Mixpanel

What are some alternatives to Argo, Avo?

Kubernetes

Kubernetes is an open source orchestration system for Docker containers. It handles scheduling onto nodes in a compute cluster and actively manages workloads to ensure that their state matches the users declared intentions.

Rancher

Rancher is an open source container management platform that includes full distributions of Kubernetes, Apache Mesos and Docker Swarm, and makes it simple to operate container clusters on any cloud or infrastructure platform.

Docker Compose

With Compose, you define a multi-container application in a single file, then spin your application up in a single command which does everything that needs to be done to get it running.

Docker Swarm

Swarm serves the standard Docker API, so any tool which already communicates with a Docker daemon can use Swarm to transparently scale to multiple hosts: Dokku, Compose, Krane, Deis, DockerUI, Shipyard, Drone, Jenkins... and, of course, the Docker client itself.

Segment

Segment is a single hub for customer data. Collect your data in one place, then send it to more than 100 third-party tools, internal systems, or Amazon Redshift with the flip of a switch.

Tutum

Tutum lets developers easily manage and run lightweight, portable, self-sufficient containers from any application. AWS-like control, Heroku-like ease. The same container that a developer builds and tests on a laptop can run at scale in Tutum.

Portainer

It is a universal container management tool. It works with Kubernetes, Docker, Docker Swarm and Azure ACI. It allows you to manage containers without needing to know platform-specific code.

Codefresh

Automate and parallelize testing. Codefresh allows teams to spin up on-demand compositions to run unit and integration tests as part of the continuous integration process. Jenkins integration allows more complex pipelines.

CAST.AI

It is an AI-driven cloud optimization platform for Kubernetes. Instantly cut your cloud bill, prevent downtime, and 10X the power of DevOps.

k3s

Certified Kubernetes distribution designed for production workloads in unattended, resource-constrained, remote locations or inside IoT appliances. Supports something as small as a Raspberry Pi or as large as an AWS a1.4xlarge 32GiB server.

Related Comparisons

Argo vs Avo: What are the differences?

# Introduction
When comparing Argo and Avo, it's important to understand the key differences between these two popular tools for data governance and metadata management.

1. **Integration Capabilities**: Argo offers seamless integration with popular cloud storage providers like AWS S3 and Google Cloud Storage, while Avo has a more versatile approach with integrations with BI tools like Looker and Tableau.
   
2. **User Interface**: Avo boasts a user-friendly interface with drag-and-drop functionality for easy metadata management, whereas Argo provides a more technical interface that caters to users comfortable with code and scripts.

3. **Data Lineage Visualization**: Argo visualizes data lineage in a hierarchical tree structure, making it easier for users to track the flow of data, while Avo focuses on providing lineage information in a more detailed, granular format with interactive features for drilling down into specific details.

4. **Collaboration Features**: Avo emphasizes collaboration with features like commenting and sharing metadata assets with team members, fostering better communication and teamwork, whereas Argo focuses more on individual data management tasks without extensive collaboration tools.

5. **Metadata Versioning**: Argo supports metadata versioning, allowing users to track changes over time and revert to previous versions if needed, while Avo lacks this feature, making it potentially more challenging to manage metadata changes and history.

6. **Governance Policies**: Argo provides robust governance policies for enforcing data quality standards and access controls, ensuring data integrity and compliance, whereas Avo offers basic governance functionalities with room for customization based on user needs.

Argo vs Avo

Overview