Based on our record, Apache Airflow should be more popular than Azure Key Vault. It has been mentiond 67 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Utilize specialized tools like AWS Secrets Manager, HashiCorp Vault, or Azure Key Vault for secrets management in your serverless environments. These tools keep sensitive data out of function code and configurations and bring advanced features to the table:. - Source: dev.to / about 2 months ago
Azure Key Vault is a cloud-based service provided by Microsoft Azure that enables secure storage and management of secrets. It integrates well with Kubernetes, allowing organizations to centralize and control access to secrets within their Azure infrastructure. - Source: dev.to / 6 months ago
No Azure Key Vault[0]? Oh #1 is your product? #3 and #4 mention your product being better? It's your company? Shm [0]: https://azure.microsoft.com/en-us/products/key-vault/. - Source: Hacker News / 6 months ago
Ideally, all secrets should be stored and accessible by a secret manager (Azure Key Vault) and stored on repository only reference to right secret. On the other hand, the developer needs to use the secret's values on their configuration files (i.e. appSettings.json), so a fast way for retrieve them from Key Vault should be nice. - Source: dev.to / 11 months ago
From there, you should be able to use something like GCP HSM or Azure Key Vault (seem to be cheap enough): Https://cloud.google.com/kms/docs/hsm Https://azure.microsoft.com/en-us/products/key-vault/. Source: about 1 year ago
An integral part of an ML project is data acquisition and data transformation into the required format. This involves creating ETL (extract, transform, load) pipelines and running them periodically. Airflow is an open source platform that helps engineers create and manage complex data pipelines. Furthermore, the support for Python programming language makes it easy for ML teams to adopt Airflow. - Source: dev.to / 7 days ago
Level 1 of MLOps is when you've put each lifecycle stage and their intefaces in an automated pipeline. The pipeline could be a python or bash script, or it could be a directed acyclic graph run by some orchestration framework like Airflow, dagster or one of the cloud-provider offerings. AI- or data-specific platforms like MLflow, ClearML and dvc also feature pipeline capabilities. - Source: dev.to / about 1 month ago
For the third, examples here might be analytics plugins in specialized databases like Clickhouse, data-transformations in places like your ETL pipeline using Airflow or Fivetran, or special integrations in your authentication workflow with Auth0 hooks and rules. - Source: dev.to / 4 months ago
Apache Airflow is an open-source platform to programmatically author, schedule, and monitor workflows. The platform features a web-based user interface and a command-line interface for managing and triggering workflows. Source: 7 months ago
Airflow is the most widely used and well-known tool for orchestrating data workflows. It allows for efficient pipeline construction, scheduling, and monitoring. - Source: dev.to / 7 months ago
AWS CloudHSM - Data Security
ifttt - IFTTT puts the internet to work for you. Create simple connections between the products you use every day.
Egnyte - Enterprise File Sharing
Microsoft Power Automate - Microsoft Power Automate is an automation platform that integrates DPA, RPA, and process mining. It lets you automate your organization at scale using low-code and AI.
OpenSSH - OpenSSH is a free version of the SSH connectivity tools that technical users rely on.
Make.com - Tool for workflow automation (Former Integromat)