Essential Libraries for Data Engineering in Fintech

Data engineering in fintech requires efficient data processing, cloud integration, and real-time analytics. Below are some key Python libraries that help streamline ETL workflows, database interactions, and large-scale data transformations.

1. pandas

• The most widely used library for data manipulation and transformation.
• Provides fast DataFrame operations, making it easy to clean and preprocess transaction data.
• Integrates seamlessly with PostgreSQL and DuckDB.
• Example:

  import pandas as pd
  df = pd.read_csv("transactions.csv")
  df["amount"] = df["amount"].astype(float)

2. duckdb

• An in-memory OLAP database optimized for analytics.
• Handles large-scale financial datasets efficiently.
• Supports SQL queries on Pandas DataFrames and Parquet files.
• Example:

  import duckdb
  df = duckdb.query("SELECT user_id, SUM(amount) FROM transactions GROUP BY user_id").df()

3. psycopg2

• The standard PostgreSQL adapter for Python.
• Used for executing queries and managing transactions in Azure PostgreSQL.
• Example:

  import psycopg2
  conn = psycopg2.connect("dbname=fintech user=admin password=secure")
  cur = conn.cursor()
  cur.execute("SELECT COUNT(*) FROM transactions")
  print(cur.fetchone())

4. sqlalchemy

• Provides a high-level ORM and database connection management.
• Works well with PostgreSQL on Azure.
• Enables query optimization and connection pooling.
• Example:

  from sqlalchemy import create_engine
  engine = create_engine("postgresql://user:pass@host:5432/db")

5. azure-storage-blob

• Used for storing large datasets, logs, and backups in Azure Blob Storage.
• Essential for cloud-based data pipelines.
• Example:

  from azure.storage.blob import BlobServiceClient
  blob_service = BlobServiceClient.from_connection_string("your_conn_string")
  container_client = blob_service.get_container_client("data-container")

6. pyarrow

• Optimized for handling Parquet and Arrow file formats.
• Speeds up data transfer between PostgreSQL and DuckDB.
• Example:

  import pyarrow.parquet as pq
  table = pq.read_table("transactions.parquet")

7. dask

• A parallel computing library for scalable ETL processing.
• Handles massive transaction datasets efficiently.
• Example:

  import dask.dataframe as dd
  df = dd.read_csv("large_transactions.csv")

8. dbt (Data Build Tool)

• Used for transforming data in PostgreSQL.
• Helps define and manage SQL transformations.
• Example dbt model:

  SELECT user_id, COUNT(*) AS txn_count, SUM(amount) AS total_spent
  FROM transactions
  GROUP BY user_id

9. fastapi

• Low-latency, async-friendly API framework for fintech applications.
• Useful for exposing real-time data engineering services.
• Example:

  from fastapi import FastAPI
  app = FastAPI()
   
  @app.get("/health")
  def health_check():
      return {"status": "running"}

Conclusion

These libraries form the foundation of a scalable, cloud-integrated fintech data engineering pipeline. By leveraging pandas, DuckDB, PostgreSQL, and Azure services, fintech companies can optimize transaction processing, real-time fraud detection, and financial analytics.

🚀 Optimising Data Engineering in Fintech!