Introduction

In today's fast-paced financial markets, the ability to process and analyze stock market data in real-time provides a significant competitive advantage. This article describes our journey building a comprehensive Stock Market Analytics Platform on Google Cloud Platform (GCP), combining batch processing, real-time streaming, and advanced analytics components to deliver actionable insights from market data.

We'll walk through the architecture, implementation details, and key technical decisions that went into creating this end-to-end data engineering solution.

Project Overview

Our Stock Market Analytics Platform processes data from both historical stock feeds and real-time market updates. It transforms raw market data into meaningful analytics through a combination of batch and streaming pipelines, stores the results in BigQuery, and visualizes them through interactive dashboards.

The system handles millions of records daily, processes complex transformations through dbt, and delivers insights ranging from basic price trends to sophisticated technical indicators.

Architecture

Here's the high-level architecture of our stock market analytics platform:

                                                  +-------------------+
                                                  |                   |
                                                  |  Looker Studio    |
                                                  |  Visualizations   |
                                                  |                   |
                                                  +--------^----------+
                                                           |
                       Batch Pipeline                      |
+---------------+    +----------------+    +---------------v-----------+
|               |    |                |    |                           |
| Stock Market  +--->+ Dataproc       +--->+                           |
| Data Files    |    | (Spark Jobs)   |    |                           |
|               |    |                |    |                           |
+---------------+    +-------^--------+    |                           |
                             |             |                           |
                             |             |      BigQuery             |
+---------------+    +-------+--------+    |     (Data Storage)        |
|               |    |                |    |                           |
| Stock Market  +--->+ Kafka VM       +--->+                           |
| Data Stream   |    | (Producer/     |    |                           |
|               |    |  Consumer)     |    |                           |
+---------------+    +-------+--------+    +-------------^-------------+
                             |                           |
                             |                           |
                     +-------v---------+       +---------+---------+
                     |                 |       |                   |
                     | Airflow + DBT   +------>+ Transformed Data  |
                     | (Analytics)     |       | Models            |
                     |                 |       |                   |
                     +-----------------+       +-------------------+
                           |
                           | (triggers batch processing once daily)
                           |
                           v

This architecture combines:

  1. Batch Processing: Using Dataproc (managed Spark) for processing historical data
  2. Real-time Streaming: Using Kafka for streaming real-time stock market data
  3. Data Storage: BigQuery for storing raw and processed data
  4. Analytics: Airflow and DBT for orchestration and data transformation
  5. Visualization: Connected to Looker Studio for data visualization

Infrastructure as Code (Terraform)

For our cloud infrastructure, we implemented Infrastructure as Code (IaC) using Terraform. This allowed us to define, version, and deploy our GCP resources programmatically.

Our main Terraform configuration provisions: