Airline Delay Analysis with AWS EMR, Hadoop, Hive & Tableau

Air travel is one of the most complex industries in the world, and delays are among its most frustrating challenge—for both passengers and airlines. For businesses, flight delays don’t just mean late arrivals—they lead to increased operating costs, resource mismanagement, reduced customer satisfaction, and lost revenue.

With millions of flights occurring each year, the real challenge is not collecting delay data, but processing it at scale and turning it into actionable insights.

This project takes historical U.S. airline data (1987–2008) and applies big data tools on AWS to identify patterns in delays and cancellations. The end goal is to show how airlines and airports can use such analysis to optimize operations, reduce bottlenecks, and improve customer experiences.

Business Problem

Airlines and airports need answers to questions like:

• Which airports experience the highest delay times, and why?

• Which carriers contribute most to delays, and how does that impact competitiveness?

• Are departure delays or arrival delays more severe across the network?

Without these insights, airlines may continue inefficient scheduling, underestimating staffing needs, or failing to anticipate cascading effects of delays.

Technologies & Tools

• AWS EMR (Elastic MapReduce) – Managed Hadoop cluster for distributed data processing

• HDFS – Storage for gigabytes of historical airline data

• Apache Hive – SQL-like querying for large datasets

• WinSCP + SSH – Data transfer and remote access to the EMR cluster

• Tableau – Interactive dashboards for data visualization

Project Workflow

1. Data Extraction

   • Downloaded U.S. airline on-time performance dataset from Harvard Dataverse.

   • Worked with CSV files spanning multiple gigabytes.

2. Cluster Setup on AWS

   • Configured EMR cluster (1 master + 2 core nodes) with Hadoop & Hive.

   • Enabled distributed storage and querying.

3. Data Ingestion into HDFS

   • Used WinSCP to transfer data files into the master node.

   • Loaded CSVs into HDFS for scalable access.

4. Hive Table Creation & Querying

   • Created an external Hive table schema to structure flight data.

   • Wrote HiveQL queries to answer key business questions, such as:

     • Top 3 airports with the highest total delays

     • Top 3 carriers contributing most to delay hours

     • Comparison of average arrival vs. departure delays

5. Visualization with Tableau

   • Built dashboards to visualize airport-wise and carrier-wise delays.

   • Designed comparative charts to highlight delay sources across the network.

Insights & Findings

• Top 3 Delayed Airports: Certain airports consistently accounted for the largest portion of total delay times, highlighting critical congestion points in the network.

• Top 3 Delayed Carriers: Specific airlines were found to accumulate the highest total delay hours, providing benchmarks for operational efficiency.

• Arrival vs Departure Delays: On average, departure delays tended to cascade into arrival delays, reinforcing the need for better scheduling and resource management at origin airports.

Business Impact

By uncovering these insights, airlines and airports can:

• Optimize Staffing & Scheduling – Allocate more ground crew and gates at high-delay airports to reduce turnaround time.

• Carrier Benchmarking – Compare carrier performance and push for operational improvements.

• Delay Mitigation Strategies – Target root causes such as late aircraft or weather-related delays more effectively.

This type of analysis doesn’t just reduce delays—it improves on-time performance metrics, boosts passenger trust, and reduces financial losses associated with disruptions.

Next Steps

To extend the project’s value, the following enhancements could be implemented:

• Partitioned Hive Tables to analyze all years (1987–2008) efficiently.

• PySpark Integration for faster, in-memory computations.

• Automation with AWS Glue or Airflow to run the pipeline regularly.

• Interactive Dashboards with Tableau Public or AWS QuickSight for real-time monitoring.

• Integration with Weather Data to uncover external causes of delays.

Conclusion

This project demonstrates how cloud-based big data ecosystems (AWS EMR + Hadoop + Hive) can handle massive datasets and produce insights that directly tie to business outcomes. By combining scalable processing with interactive visualization, airlines can shift from reactive responses to proactive strategies, reducing delays and improving passenger satisfaction. Link to the project :- https://github.com/DatawithOmkar/Airline-Delay-Analysis-using-AWS-EMR-Hive-Tableau