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BI & Analytics

Drive successful analytics and business intelligence with valuable data, thanks to reliable and automated pipelines. Discover the value of Apache Airflow in your business!

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Success with Analytics depends on freshness of data

Data is only as valuable as it is fresh, accurate, and actionable. In an ideal world, business insights should be limited only by the ability to frame the right questions. When delivered into the right hands in a timely and usable manner, analytics and business intelligence can completely transform how organizations make decisions. On the flip side, a lack of data or incomplete or inaccurate data can have negative consequences, from misleading reports to incorrect conclusions to slower decision-making.

The data ingestion pipeline is the keystone to successful big data analytics and delivering business insights. It’s impossible to get reliable, fresh data with traditional serial processing of data pipelines and one-and-done data analysis efforts. Pipelines need to be running reliably and in parallel any time fresh data can make a difference to business decisions.

Apache Airflow—run your data pipelines and tasks automatically

Apache Airflow is a way to programmatically author, schedule, and monitor workflows. With its proven core functionality, extensible and scalable framework, and support from a large community, Airflow is considered an industry standard for data pipelines automation. Now you can deliver fresh analytics more frequently, with less work.

Apache Airflow
  1. End-to-end pipelines-as-code

    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. Rapid
    data innovation

    Combine multiple existing pipelines to solve new business problems and provide new analytical insights.

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