Real-time Train Location Data Processing and Analysis Pipeline

You, Fri Jun 23 2023 • data pipeline data analysis kafka ksqldb spark

Personal Initiative:

This is one of my recent personal project and implemented entirely during my free time. It represents my interest to show continuous learning and curiosity to experiment with state-of-the-art technologies and apply them to solve real-world problems.

Project Overview:

This project is a detailed end-to-end data pipeline, designed to receive, process, refine, store, and analyze real-time train location data. The system leverages leading technologies such as Python, Kafka, ksqlDB, Apache Spark, and datalake to provide essential insights about train journeys.

Key Features and Architecture:

Figure 1: High Level Architecture

Real-time Data Ingestion: The initial part of the pipeline (Data Ingestor) is a Python script designed to receive real-time train location data.
Data Streaming with Kafka: The incoming location data is streamed into a Kafka topic, providing efficient, fault-tolerant streaming, and processing of live data.
Data Refinement with ksqlDB: The Kafka-streamed location data is further processed using ksqlDB, refining the train location data and joining it with stations and trains table data. This refinement phase enriches the raw data with additional contextual information.
Data Storage and Backup: The refined data is inserted into a relational database for easy access to the real-time train locations. Simultaneously, the raw refined data is written to data lake for secure and scalable data storage.
Data Analysis with Spark: The final phase involves a Spark script to process the stored data from the data lake, extracting information about the journey of each train, calculating delays etc.

Figure 2: Geographic distribution of train stations and all train journeys on a single day

Figure 3: Geographic distribution of a single train journey on a single day

Figure 4: Overview of a single train journey on a single day

Figure 5: Geographic distribution of live train locations on a map

Technologies Used:

Python: Scripting data reception and overall pipeline orchestration.

Apache Kafka: Real-time data streaming.

ksqlDB: Refining and joining raw data with existing tables.

Database: Storing refined data.

S3 compatible Object Storage: Serving as a data lake for raw, refined data storage.

Apache Spark: Data processing and analytics.

Confluent Cloud: For kafka and ksqlDB cluster.

This project is a testament to the efficient use of various data processing and analytics tools in a real-time context. It demonstrates the design and implementation of a comprehensive data pipeline that can handle large volumes of streaming data, apply complex transformations, and perform large-scale data analysis.