ETL/ELT Data Pipelines
You need incoming, relevant data to act on, whether your final aim is training machine learning models, BI and analytics, building a data lake, or data science experimentation. Apache Airflow allows you to create, scale, and manage ETL pipelines more effectively, delivering standardized, quality-assured, and current data that can be reused across your organization.Schedule a Demo
ETL - unifying the workflow from data ingestion to data consumer
ETL combines, checks, and prepares data for consumption across your company by integrating data from a variety of internal and external sources. When your ETL solution comprises multiple integrated systems, it gets harder to scale and more complex to navigate.
How will you manage upstream and downstream dependencies? Passing parameters between jobs? Reprocessing data for the past X days? Retrying jobs that fail? Parallel processing multiple pipelines? Airflow has the answers.
In the age of Big Data, data engineers need faster, repeatable and easily manageable workflow management systems to deal with all their scheduled ETL. Airflow solves this problem, addressing the complex challenges of data pipelines: scale, performance, reliability, security and manageability.
Apache Airflow—end to end orchestration for ETL & ELT
Airflow is purpose-built to orchestrate the data pipelines that provide ELT at scale for a modern data platform. Since its founding, Airflow has evolved with the needs of Cloud, Big Data, BI and machine learning to provide the scale, developer productivity, flexibility, community and ecosystem that make it the technology of choice for building and operating data pipelines as code using Python and SQL.
Express all your pipelines using the flexibility of Python and SQL. Data engineers can quickly assemble pipelines using the library of providers, sample modules and template pipelines in the Astronomer Registry.
Advanced pipeline management features
Apache Airflow includes real-world pipeline capabilities such as XCom for inter-task communication, backfilling to reprocess historical data, and concurrency, improving performance while reducing execution time.
Airflow’s out-of-the-box intuitive visualizations are an invaluable tool for monitoring and debugging pipelines and individual tasks.
I love how my data science team has become self-sufficient and effective. Airflow made it very easy for them to get the data they need and manage it in a way that allows them to do their job quickly and efficiently.
VP of Data & Analytics at Herman Miller
After 6-7 months with Apache Airflow, we’ve built more than ninety DAGs. The tool made the experience so much easier.
Data Engineering Lead at CRED
An open source project, such as Apache Airflow, works great in the production environment, even for the sensitive use cases of the banking industry.
Product Owner at Societe Generale