Designing real-time aviation intelligence at scale

I built cloud-native streaming systems across AWS and GCP to predict flight delays, optimize routes, and enable personalized customer offers. With Spark, TensorFlow, and SageMaker, we modernized how data drove flight ops, customer experience, and pricing strategy.

Project Overview

Our goal was to develop a scalable, multi-cloud data pipeline that ingests real-time flight telemetry, weather, and booking data, performs streaming analytics, and deploys ML models for delay prediction and demand forecasting. The system integrates workflow orchestration and visualization tools to empower operational teams with actionable insights, improving efficiency and customer targeting in airline operations.

Components

Data Ingestion (Kafka / Kinesis):
Ingests real-time flight telemetry, weather data, gate information, and customer bookings from airport systems and web/mobile platforms.

Processing (Apache Spark Streaming):
Performs real-time transformations, joins, and windowed aggregations to detect patterns in flight performance and operational delays.

Machine Learning (TensorFlow + SageMaker):
Applies deep learning models to predict flight delays, forecast demand, and score passenger loyalty segments. Models are retrained nightly on historical and real-time features.

Workflow Orchestration (Airflow):
Coordinates ETL, batch training jobs, and SageMaker deployments. Also manages daily dashboard refresh pipelines and anomaly detection alerts.

Data Storage (S3 + BigQuery):
Data is stored across AWS S3 (for raw and transformed data) and BigQuery (for analytical querying and BI dashboards).

Visualization (Looker / QuickSight):
Flight operation teams, route planners, and marketing analysts use dashboards to monitor live KPIs: delay predictions, crew efficiency, load factors, and high-value passenger targeting.

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Key Achievements

• Achieved sub-30 second latency for real-time flight delay predictions using Spark Structured Streaming.
• Deployed TensorFlow models on SageMaker for dynamic pricing, boosting yield per seat by 12%.
• Integrated weather APIs and ATC feeds for proactive rerouting recommendations.
• Enabled high-precision segmentation for loyalty programs using ML clustering on customer behavior data.
• Reduced route planning cycle time by 40% through automated data pipelines.

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Example Airflow DAG (Python Snippet)

from airflow import DAG
from airflow.providers.amazon.aws.operators.sagemaker import SageMakerTrainingOperator
from airflow.providers.apache.spark.operators.spark_submit import SparkSubmitOperator
from airflow.operators.python import PythonOperator
from datetime import datetime, timedelta

def update_dashboards():
    print("Refreshing aviation dashboards...")

default_args = {
    'owner': 'aviation_team',
    'depends_on_past': False,
    'start_date': datetime(2025, 1, 1),
    'retries': 1,
    'retry_delay': timedelta(minutes=10),
}

with DAG('aviation_intelligence_pipeline',
         default_args=default_args,
         schedule_interval='@hourly',
         catchup=False) as dag:

    stream_ingestion = SparkSubmitOperator(
        task_id='ingest_flight_streams',
        application='/opt/spark_jobs/flight_stream_ingest.py',
        conn_id='spark_default',
    )

    ml_training = SageMakerTrainingOperator(
        task_id='train_flight_delay_model',
        config={
            # Simplified config
            'TrainingJobName': 'delay-predictor',
            'AlgorithmSpecification': {
                'TrainingImage': 'tensorflow:latest',
                'TrainingInputMode': 'File',
            },
            'InputDataConfig': [],
            'OutputDataConfig': {'S3OutputPath': 's3://models/flight-delay/'},
            'ResourceConfig': {
                'InstanceType': 'ml.m5.xlarge',
                'InstanceCount': 1,
                'VolumeSizeInGB': 50,
            },
            'RoleArn': 'arn:aws:iam::123456789:role/SageMakerExecution',
            'StoppingCondition': {'MaxRuntimeInSeconds': 600},
        },
        aws_conn_id='aws_default',
        wait_for_completion=True,
    )

    refresh_dashboards = PythonOperator(
        task_id='refresh_ops_dashboards',
        python_callable=update_dashboards,
    )

    stream_ingestion >> ml_training >> refresh_dashboards

Summary

This project showcases a cutting-edge aviation data pipeline integrating real-time ingestion, ML model deployment, and actionable dashboards. Spark, TensorFlow, SageMaker, and Airflow worked together to power smarter flight operations, improved customer targeting, and adaptive pricing — all while maintaining operational agility across AWS and GCP environments.