Managed Workflows for Apache Airflow
MWAA allows running Apache Airflow workloads fully managed and securely architected following AWS best practices while optimizing reliability and costs. Managed Workflows for Apache Airflow on AWS enables workflow automation for data processing orchestration, lineage tracking, and operational monitoring across AWS services without infrastructure management requirements providing native integration with Amazon EMR, Redshift, AWS Glue, and related services.
Key Capabilities
- Fully managed Airflow control plane
- Airflow auto-scaling based on usage metrics
- Pay only for the capacity used
- Deep native AWS services integration
Benefits
- Airflow without operational heavy lifting
- Helps focus on pipeline logic rather than infra
- Automatic Airflow optimization by AWS
- Cost-efficient and elastic
Use Cases
- Redshift leverages MWAA’s auto-scaling to manage daily peak ETL loads accessing petabytes of weather simulation data.
- Doordash leverages MWAA to orchestrate data workflows – from order data ingestion to analytics.
- Intuit built its automated ML platform on MWAA, helping standardize workflows from experiment tracking to model monitoring.
Top 15 Automation Tools for Data Analytics
The exponential growth in data in recent times has made it imperative for organizations to leverage automation in their data analytics workflows. Data analytics helps uncover valuable insights from data that can drive critical business decisions. However, making sense of vast volumes of complex data requires scalable and reliable automation tools.
In this article, we will be discussing the Top 15 Automation Tools Data Analytics teams rely on to efficiently collect, process, analyze, and visualize data. We explore each tool’s core capabilities, benefits, and real-world use cases across organizations. Let’s get started!