Are you navigating the evolving landscape of modern data engineering? Curious about leveraging the strengths of different programming languages within a single, cohesive data pipeline? This article dives into a practical, lightweight project that demonstrates exactly that: a polyglot data pipeline integrating Python for robust data preparation and Go for blazing-fast data ingestion into ClickHouse.

This project offers a hands-on look at how to blend Python’s versatility with Go’s raw speed, all orchestrated locally using Docker Compose. We’ll explore the architecture, the tools involved, and the valuable lessons learned along the way.

What This Polyglot Pipeline Achieves

Our containerized mini-project showcases a streamlined workflow:

• Python (🐍): Handles the generation and meticulous preparation of sample data.
• Parquet (📁): The prepared data is efficiently converted into a columnar Parquet file format.
• Go (⚡): Reads the Parquet file and performs high-speed data inserts directly into ClickHouse.
• Docker Compose (🐳): Provides a simple, local environment for spinning up ClickHouse and running the entire pipeline seamlessly.

The Essential Tech Stack

This pipeline leverages a powerful combination of technologies:

• Python: Chosen for its extensive libraries and flexibility in data manipulation and Parquet file generation.
• Go: Selected for its exceptional performance, making it ideal for rapid data ingestion into databases like ClickHouse.
• ClickHouse: An incredibly fast, open-source columnar OLAP database, perfect for analytical workloads and handling high-volume inserts.
• Docker Compose: Simplifies the setup and management of ClickHouse locally, ensuring easy reproducibility.
• Parquet: An industry-standard, efficient columnar storage format that optimizes data access and storage.

Get Your Hands Dirty: Running It Locally

Ready to see it in action? Follow these simple steps to set up and run the pipeline on your machine:

1. Clone the Repository:

git clone https://github.com/mohhddhassan/go-clickhouse-parquet.git
cd go-clickhouse-parquet

1. Generate Sample Parquet Data with Python:

cd python
python3 generate_parquet.py

1. Start ClickHouse with Docker Compose:

docker compose up -d

1. Execute the Go Application for Data Ingestion:

cd go
go run main.go

Project Structure at a Glance

Understanding the layout:

go-clickhouse-parquet/
├── docker-compose.yml         # Defines the ClickHouse service
├── parquet-files/
│   └── sample.parquet         # Automatically generated Parquet data file
├── python/
│   └── generate_parquet.py    # Python script for data creation and Parquet conversion
└── go/
    ├── go.mod
    ├── go.sum
    └── main.go                # Go application for reading Parquet and inserting into ClickHouse

Valuable Lessons Learned

Building this project offered key insights into:

• Programmatically generating and working with Parquet files using Python.
• Establishing robust connections and performing efficient data inserts into ClickHouse using Go.
• Rapidly deploying and managing ClickHouse instances with Docker Compose.
• The profound benefits and practical application of polyglot pipelines, where different languages are chosen for their specific strengths.

Why This Project is a Must-Try

If you’re delving into data engineering or systems programming, this project provides an excellent opportunity to:

• Gain practical experience integrating Python and Go for real-world data movement tasks.
• Work directly with Parquet files, an essential format in modern analytics.
• Observe ClickHouse‘s capabilities in handling fast data inserts and queries.
• Develop skills in wiring together diverse components to form a functional data pipeline.

What’s Next for This Pipeline?

The journey doesn’t end here! Consider these exciting enhancements:

• Developing an interactive dashboard on top of ClickHouse to visualize the ingested data.
• Exploring methods for streaming Parquet data into ClickHouse in real-time.
• Experimenting with more intricate data schemas and transformations.
• Conducting detailed benchmarks to compare the performance of Python versus Go within different stages of the pipeline.

This project, conceived by Mohamed Hussain S, an Associate Data Engineer, is a testament to the continuous learning approach – building one mini-project at a time to master the craft of data engineering. You can connect with Mohamed on LinkedIn and explore more of his work on GitHub.