Hello World of Modern Data Engineering

This guide introduces the concept of a "Hello World" project for modern data engineering, helping newcomers learn the basics through a practical example.

Hello World of Modern Data Engineering

A beginner's guide to building your first data pipeline with modern tools and practices

Get Started

Why Start Here?

Just as "Hello World" is the traditional first program in software development, this data engineering starter project introduces core concepts while delivering tangible results. It's designed to be simple yet powerful, using modern tools that scale from laptop to enterprise.

Quick to Build

Create a working pipeline in under an hour

Modern Stack

Learn industry-standard tools in a simple context

Scalable Pattern

Start small, but with a foundation that can grow

Core Concepts

Practice fundamental skills used in all data pipelines

The Pathway

Follow these steps to create your first modern data engineering pipeline

Step 1

Set Up Your Environment

Create a reproducible environment with all the tools you need using Docker or virtual environments.

DockerPythonvenv

Step 2

Install Core Libraries

Add the essential data engineering libraries that provide powerful functionality with minimal code.

SQLAlchemyDuckDBPolarsDaft

Step 3

Extract Data

Read data from common sources like CSV or Excel files, preparing them for processing.

CSVExcelFile Formats

Step 4

Convert to Parquet

Transform your data into the efficient Parquet columnar format for better performance.

ParquetCompressionPerformance

Step 5

Load into DuckDB

Store your data in a powerful analytical database that runs locally with minimal setup.

DuckDBSQLAnalytics

Step 6

Transform with Polars/Daft

Manipulate and analyze your data using high-performance dataframe libraries.

DataframesTransformationsFiltering

Step 7

Write Results

Save your processed data back to persistent storage in optimized formats.

OutputPersistenceStorage

Step 8

Schedule Execution

Automate your pipeline to run on a schedule, making it truly operational.

CronMakefileAutomation

Sample Code

pipeline.pyPython

# Hello World of Modern Data Engineering

# 1. Import libraries
import polars as pl
import duckdb

# 2. Read CSV file
df = pl.read_csv("data.csv")
print(f"Loaded {len(df)} rows from CSV")

# 3. Convert to Parquet
df.write_parquet("data.parquet")
print("Converted to Parquet format")

# 4. Connect to DuckDB
con = duckdb.connect("my_database.db")
con.execute("CREATE TABLE IF NOT EXISTS my_data AS SELECT * FROM 'data.parquet'")
print("Loaded data into DuckDB")

# 5. Transform data with Polars
result = pl.from_arrow(
    con.execute("SELECT * FROM my_data WHERE value > 100").fetch_arrow_table()
)
print(f"Filtered to {len(result)} rows")

# 6. Add new calculated column
result = result.with_columns(
    pl.col("value").mul(1.5).alias("adjusted_value")
)
print("Added calculated column")

# 7. Write results back
result.write_parquet("results.parquet")
con.execute("CREATE OR REPLACE TABLE results AS SELECT * FROM 'results.parquet'")
print("Saved transformed data")

# To schedule: Add this to crontab or create a Makefile

Why These Technologies?

DuckDB

An analytical database that runs in-process with your application. It's like SQLite but optimized for analytics rather than transactions, delivering exceptional performance for data processing tasks.

Polars

A lightning-fast DataFrame library written in Rust. It offers a pandas-like API but with dramatic performance improvements and better memory efficiency for larger datasets.

Parquet

A columnar storage file format that provides efficient data compression and encoding schemes. It significantly reduces storage requirements while improving query performance.

Containers

Technologies like Docker ensure your environment is reproducible and consistent across development and production, eliminating "it works on my machine" issues.

Where to Go Next

After mastering this "Hello World" pipeline, you can expand your data engineering skills by:

Scale Up

Move to distributed processing with Apache Spark or Dask for handling larger datasets

Add Orchestration

Implement proper workflow management with tools like Apache Airflow or Dagster

Streaming Data

Adapt your pipeline for real-time data processing using Kafka and Flink

Add Data Quality

Implement validation, testing, and monitoring with Great Expectations

Version Control

Track data and schema changes with dbt or other data versioning tools

Cloud Integration

Move your pipeline to cloud data platforms like Snowflake, BigQuery, or Redshift