Advanced Data Engineering with Python, Azure, PostgreSQL, and DuckDB in Fintech

Introduction

Data engineering in fintech requires low-latency analytics, real-time fraud detection, and cost-efficient data processing. This guide explores a niche concept: Intraday Transaction Analysis using DuckDB with PostgreSQL on Azure.

Intraday Transaction Analysis in Fintech

Intraday analysis involves processing millions of transactions in real-time to detect anomalies, predict risks, and improve financial decision-making. Traditional databases struggle with high-volume aggregations, making DuckDB a powerful in-memory OLAP engine for fintech analytics.

Why Use DuckDB for Fintech Analytics?

• High-Speed In-Memory Queries: Faster than PostgreSQL for analytical workloads.
• Embedded OLAP Processing: No need for a separate data warehouse.
• Efficient Handling of Parquet and CSV Files: Ideal for streaming transaction data.

Setting Up PostgreSQL and DuckDB for Intraday Analysis

Step 1: Storing Transactions in Azure PostgreSQL

CREATE TABLE transactions (
    id SERIAL PRIMARY KEY,
    user_id INT,
    amount DECIMAL(10,2),
    currency VARCHAR(3),
    timestamp TIMESTAMPTZ DEFAULT NOW(),
    status VARCHAR(20)
);

Step 2: Streaming PostgreSQL Data into DuckDB

Using Python and DuckDB, we can efficiently process real-time data.

import duckdb
import psycopg2
import pandas as pd
 
def load_transactions():
    conn_pg = psycopg2.connect(
        dbname='your_db', user='your_user', password='your_pass', host='your_azure_host', port='5432'
    )
    df = pd.read_sql("SELECT * FROM transactions WHERE timestamp > NOW() - INTERVAL '1 HOUR'", conn_pg)
    conn_pg.close()
 
    conn_duck = duckdb.connect(database=':memory:')
    conn_duck.register('transactions', df)
    return conn_duck

Step 3: Real-Time Fraud Detection Using DuckDB

Using DuckDB’s fast SQL engine, we detect fraud patterns:

conn = load_transactions()
fraudulent_txns = conn.execute('''
    SELECT user_id, COUNT(*) AS txn_count, SUM(amount) AS total_spent
    FROM transactions
    WHERE status = 'approved'
    GROUP BY user_id
    HAVING COUNT(*) > 50 AND SUM(amount) > 10000
''').fetchdf()
print(fraudulent_txns)

Step 4: Storing Anomalies in Azure Data Lake

Once anomalies are detected, we store them in Azure Data Lake for further analysis:

from azure.storage.blob import BlobServiceClient
import json
 
def store_anomalies(anomalies_df):
    blob_service = BlobServiceClient.from_connection_string("your_adls_conn_string")
    blob_client = blob_service.get_blob_client(container="fraud-detection", blob="anomalies.json")
    blob_client.upload_blob(anomalies_df.to_json(orient='records'), overwrite=True)

Step 5: Automating with Azure Functions

• Deploy the fraud detection DuckDB pipeline as an Azure Function.
• Trigger functions on new transactions using Azure Event Hub.
• Use Power BI or Streamlit for visualization.

Notable Libraries for Data Engineering in Fintech

As a data engineer working with Python, Azure, PostgreSQL, and DuckDB, these libraries are essential:

1. pandas

• Used for data manipulation and transformation.
• Efficiently loads PostgreSQL and DuckDB query results into DataFrames.

2. duckdb

• In-memory OLAP database for analytical queries.
• Optimized for handling large datasets efficiently within Python.

3. psycopg2

• The standard PostgreSQL adapter for Python.
• Used for executing SQL queries and managing database transactions.

4. sqlalchemy

• Provides an ORM and database connection management.
• Supports PostgreSQL and integrates with Azure SQL services.

5. azure-storage-blob

• Enables interaction with Azure Blob Storage.
• Used to store large datasets, logs, and backups.

6. pyarrow

• Optimized for working with Parquet and Arrow formats.
• Enhances DuckDB’s ability to process structured data efficiently.

7. dask

• Scalable parallel computing framework.
• Ideal for handling large-scale ETL operations.

8. dbt (Data Build Tool)

• Used for analytics engineering and transforming data in PostgreSQL.
• Helps define and manage SQL transformations.

9. fastapi

• Useful for building REST APIs to expose data engineering services.
• Low-latency and async-friendly for fintech applications.

Performance Considerations

• DuckDB in-memory processing reduces query times significantly.
• PostgreSQL partitioning optimizes historical data storage.
• Azure Functions + Event Hub enables real-time detection pipelines.

Conclusion

By integrating DuckDB, PostgreSQL, and Azure, fintech companies can build high-performance intraday analytics pipelines to detect fraud in real-time. Additionally, utilizing powerful libraries like pandas, duckdb, psycopg2, sqlalchemy, and dbt enhances data engineering workflows, making them efficient and scalable.

For further exploration, consider integrating machine learning models within DuckDB for predictive fraud detection.

🚀 Revolutionising Fintech Data Engineering!