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From SQL scripts to a production data platform on AWS
In this session, we’ll explore how modern data orchestration solves the challenges created by scattered scripts and growing collections of cron jobs, and why Apache Airflow has become the industry standard for building scalable data workflows.
Agenda
- Why data orchestration matters
- Introduction to Apache Airflow
- Running Airflow on AWS with MWAA
- Connecting Airflow and dbt with Cosmos
- Real-world architecture: from demo to enterprise
Key topics
- what data orchestration is and why scattered scripts and cron jobs stop working past a certain scale;
- how Apache Airflow became the industry standard for orchestration, and what makes it a different category of tool than simple schedulers;
- how Amazon MWAA removes the operational burden of running Airflow yourself, so teams can focus on pipelines instead of infrastructure;
- how dbt and Cosmos turn raw SQL transformations into a fully-orchestrated, dependency-aware pipeline with no boilerplate code;
- how the same architecture scales from a small demo to a production data platform - and why this stack pays off long-term for hiring and warehouse flexibility.
Speaker
Amazon MWAAAmazon MWAA
Amazon Managed Workflows for Apache AirflowApache Airflow is the industry standard for orchestrating data pipelines — and Amazon MWAA lets you run it fully managed on AWS, without the operational overhead of provisioning, scaling, or patching the infrastructure yourself.
For production-grade data platforms, MWAA gives your team a reliable, scalable foundation to focus on pipelines instead of infrastructure.
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