> For the complete documentation index, see [llms.txt](https://mariadb.com/docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://mariadb.com/docs/gridgain/gridgain-9/gridgain9-get-started/learning-paths/data-pipeline-apis/transit-monitor.md).

# Build a Real-Time Transit Monitor

Build a real-time transit monitoring system that ingests GTFS feeds, stores time-series data in Apache Ignite 3, and runs SQL-based monitoring queries.

{% hint style="info" %}
This tutorial uses Apache Ignite 3. The companion repository was written for Apache Ignite 3.0.0. To run it against GridGain 9.1.28, make these changes after cloning it:

* In `pom.xml`, set `ignite.version` to `9.1.28`, change the `ignite-client` group ID from `org.apache.ignite` to `org.gridgain`, and add the GridGain repository (`https://www.gridgainsystems.com/nexus/content/repositories/external`).
* In `SchemaSetupExample`, `MonitorService` and `ReportingService`, cast the null transaction in all 13 `sql().execute(null, ...)` calls: `execute((Transaction) null, ...)`. A bare `null` is ambiguous because `IgniteSql` also has an `execute(String, Object...)` overload. Import `org.apache.ignite.tx.Transaction`.
* Start the cluster with the GridGain 9 Docker image from [How to Start a Cluster with Docker Compose](/docs/gridgain/gridgain-9/gridgain9-get-started/start-cluster-docker-compose.md), not the Apache Ignite one. In GridGain 9, `cluster init` requires a license (`--license <file>`).
  {% endhint %}

Transit data combines real-time streams, time-series storage, geospatial tracking, and complex queries, making it an ideal context for learning Ignite's distributed data capabilities. In this tutorial, you build a real-time transit monitoring system that exercises all of these patterns.

## What You Learn

* Connect to real-time GTFS transit data feeds
* Model time-series data with Ignite 3 schema annotations
* Build a batch ingestion pipeline with transaction support
* Write SQL analytics queries against distributed data
* Detect service anomalies using polling-based monitoring

## What You Build

By the end of this tutorial, the application produces a live console dashboard:

```
╔══════════════════════════════════════════════════════╗
║            TRANSIT MONITORING DASHBOARD              ║
╚══════════════════════════════════════════════════════╝
Current time: 2023-05-21 14:22:38

SUMMARY VIEW
────────────────────────────────────────────────────────────────
ACTIVE VEHICLES BY ROUTE (last 15 minutes)
• Route 14      : 32 vehicles ↑
• Route 5       : 30 vehicles =
• Route 38      : 28 vehicles ↓
• Route 1       : 26 vehicles =
• Route 8       : 24 vehicles ↑

VEHICLE STATUS DISTRIBUTION
• IN_TRANSIT_TO  : 342 vehicles ↑
• STOPPED_AT     : 198 vehicles ↓

DATA INGESTION STATUS
• Status: Running
• Total records fetched: 45,280
• Total records stored: 45,280
• Last fetch count: 540
• Last fetch time: 876ms
• Running time: 01:23:45
• Ingestion rate: 9.12 records/second

Views rotate automatically every 10 seconds
Press ENTER at any time to exit
```

The application connects to real-time transit data feeds, stores vehicle positions in a distributed Ignite database, analyzes movement patterns, and alerts operators to potential service disruptions. The system architecture includes:

* A clustered Ignite database for distributed storage
* A data ingestion pipeline for real-time updates
* SQL-based analytics for operational insights
* A monitoring system for service disruptions
* A real-time console dashboard for visualization

## Prerequisites

* **Java 11 or later** installed and properly configured
* **Maven 3.6+** for dependency management
* **Docker 20.10+** and Docker Compose for running the Ignite 3 cluster
* **IDE** such as IntelliJ IDEA or VS Code with Java extensions
* **Basic Java knowledge**, including familiarity with classes, interfaces, and collections
* **Some SQL experience** for understanding the query examples

## Tutorial Modules

This tutorial consists of 6 modules that build incrementally:

1. [**Project Setup**](/docs/gridgain/gridgain-9/gridgain9-get-started/learning-paths/data-pipeline-apis/transit-monitor/project-setup.md) - Configure environment and dependencies
2. [**Understanding GTFS**](/docs/gridgain/gridgain-9/gridgain9-get-started/learning-paths/data-pipeline-apis/transit-monitor/understanding-gtfs.md) - The transit data format and Ignite schema
3. [**GTFS Client**](/docs/gridgain/gridgain-9/gridgain9-get-started/learning-paths/data-pipeline-apis/transit-monitor/gtfs-client.md) - Build the data fetching component
4. [**Data Ingestion**](/docs/gridgain/gridgain-9/gridgain9-get-started/learning-paths/data-pipeline-apis/transit-monitor/data-ingestion.md) - Create the data pipeline
5. [**Service Monitoring**](/docs/gridgain/gridgain-9/gridgain9-get-started/learning-paths/data-pipeline-apis/transit-monitor/continuous-query.md) - Implement analytics and alerts
6. [**Putting It All Together**](/docs/gridgain/gridgain-9/gridgain9-get-started/learning-paths/data-pipeline-apis/transit-monitor/putting-together.md) - Complete application integration

## Source Code

The complete source code is available on GitHub:

**Repository:** <https://github.com/maglietti/transit-monitor>

```bash
git clone https://github.com/maglietti/transit-monitor.git
cd transit-monitor
```

Each module builds on the previous ones, with clear checkpoints to validate progress. Complete the modules in a single session or spread them across multiple days.

Start with [Module 1: Project Setup](/docs/gridgain/gridgain-9/gridgain9-get-started/learning-paths/data-pipeline-apis/transit-monitor/project-setup.md) to set up the project structure and configure the Ignite cluster.