Apache Airflow
Airflow helps data teams programmatically author, orchestrate, monitor, and version complex analytical workflows. Its fault-tolerant architecture handles large workloads reliably. Airflow is an open-source workflow orchestration platform used to programmatically author, schedule, monitor, and coordinate complex programmed data pipelines represented as directed acyclic graphs, enabling process automation, visualization, and lineage tracking of workflow logic and integrated with familiar data sources, data services, and execution orchestration engines.
Key Capabilities
- Workflow authoring, scheduling, and monitoring
- Graphical pipeline design with Python code
- Inbuilt dependency management
- High availability, scale, and performance
Benefits
- Infrastructure-as-code allows version control.
- Centralized control pane to manage pipelines
- Enhanced pipeline SLA monitoring
- Automation support across services, databases, tools
Use Cases
- Lyft orchestrates critical workflows leveraging Airflow to ensure optimal fleet efficiency and availability.
- Intuit built an automated ML platform on AWS leveraging Apache Airflow to standardize workflows from experiment tracking to model monitoring.
- Walmart uses Airflow automation to collect hundreds of terabytes of store sales data daily from over a million cash registers for near real-time analytics.
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!