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.

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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.

Apache Airflow
  1. End-to-end

    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.

  2. 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.

  3. Visualization
    of pipelines

    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.

Mark Gergess

Mark Gergess

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.

Gautam Doulani

Gautam Doulani

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.

Alaeddine Maaoui

Alaeddine Maaoui

Product Owner at Societe Generale