Apache Airflow VS Azure Synapse Analytics

Apache Airflow Reviews

Azure Synapse Analytics Reviews

Apache Airflow mentions (66)

• AI Strategy Guide: How to Scale AI Across Your Business

  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 / 11 days ago

• The 2024 Web Hosting Report

  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 / 3 months ago

• Best ETL Tools And Why To Choose

  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

• Simplifying Data Transformation in Redshift: An Approach with DBT and Airflow

  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

• Share Your favorite python related software!

  AIRFLOW This is more of a library in my opinion, but Airflow has become an essential tool for scheduling in my work. All our ML training pipelines are ordered and scheduled with Airflow and it works seamlessly. The dashboard provided is also fantastic! Source: 8 months ago

Azure Synapse Analytics mentions (3)

• Deploying a Data Warehouse with Pulumi and Amazon Redshift

  A data warehouse is a specialized database that's purpose built for gathering and analyzing data. Unlike general-purpose databases like MySQL or PostgreSQL, which are designed to meet the real-time performance and transactional needs of applications, a data warehouse is designed to collect and process the data produced by those applications, collectively and over time, to help you gain insight from it. Examples of... - Source: dev.to / over 1 year ago

• [WSJ] Facebook Parent Meta Expected to Post Slowest Revenue Growth Since IPO

  You don't run into these kinds of problems with other tools, like the ones I mentioned. I've never tried the Azure ones, but my gut says they may have some scaling issues (synapse analytics looks promising but I have no experience with it). Source: about 2 years ago

• The Difference Between Data Warehouses, Data Lakes, and Data Lakehouses.

  Popular managed cloud data warehouse solutions include Azure Synapse Analytics, Azure SQL Database, and Amazon Redshift. - Source: dev.to / about 2 years ago