Phase 2: Setting Up Your Learning Environment

2.2 Hardware Considerations

This guide is part of a larger roadmap to data engineering. Please refer back for context.

As your journey in data engineering and machine learning evolves, you’ll likely transition from browser-based tools to setting up your own local coding and development environment. This shift means ensuring your hardware is up to the task. Data engineering and machine learning can be resource-intensive, so it’s crucial to have a setup that can handle these demands efficiently.


Minimum and Recommended Hardware Requirements


Minimum requirements:

  • Processor: Intel i5 or Ryzen 5
  • RAM: 8GB
  • Storage: 256 SSD


  • Processor: Intel i7 or Ryzen 7 (higher budget: i9 or Ryzen 9)
  • RAM: 16GB (higher budget: 32GB)
  • Storage: 256GB+ NVMe M.2 SSD
  • Dedicated Graphics Card (NVIDIA GTX or RTX series)

Processor: Start with a solid foundation – a CPU that doesn’t flinch at heavy workloads. Look for an Intel i7 or AMD Ryzen 7 as a baseline. They’re like the trusty engines of your tech journey, ready to crunch data without breaking a sweat.


RAM: Think of RAM like your brain’s ability to multitask. 8GB is your entry ticket and will have you constantly close applications. If you’re serious, go with 16GB or 32GB. That is where the magic happens, especially when juggling multiple datasets and applications.


Storage: Speed is key. A Solid State Drive (SSD), preferably NVMe, with at least 256GB of space, will make loading data and booting up software feel like a breeze.


Graphics Card: If your path involves machine learning or complex visualizations, a dedicated graphics card (like an NVIDIA GTX or RTX series) can be a game-changer. It’s like having a personal assistant dedicated to doing the heavy lifting.



Additional Considerations

Portability vs. Power: Laptop or desktop? If you’re always on the move, a laptop’s mobility is unbeatable. For stationary workspaces, a desktop can offer more power for your buck while being able to upgrade them as your needs grow.


Battery Life: For laptops, longer battery life means more freedom to code in coffee shops, libraries, or wherever inspiration strikes.


Expandability: Desktops shine here, allowing you to upgrade components like RAM and storage as your needs grow. Laptops with Thunderbolt 3 ports can also offer some level of expandability.


External Monitor, Keyboard, Mouse: If you’ve opted for the laptop route, consider pairing it with an external monitor, keyboard, and mouse. This trio can transform your mobile workstation into a more ergonomic and spacious workspace, offering the comfort of a desktop while retaining the flexibility of a laptop.



High and Low Budget Laptop Recommendations


High Budget Option: MacBook Pro 16-inch

For those who can invest more, consider a laptop like MacBook Pro 16-inch. This machine offers robust performance, high-quality displays, and sufficient memory and processing power to smoothly run complex data engineering tasks.


Low Budget Option: Lenovo ThinkPad T Serie

If you’re on a tighter budget, look for older models like the Lenovo ThinkPad T Series on platforms like eBay. Thinkpad models are known for their durability and often have the necessary capabilities for basic data engineering tasks.



High Budget Option: Building Your Own Desktop

Cost: $2,000 – $3,000. 

For those who prefer a stationary setup and have a higher budget, building your own desktop can be a rewarding experience. Here’s a recommended configuration:

  • Processor: AMD Ryzen 9 – Powerful and efficient, ideal for handling heavy workloads.
  • Case: NZXT – Sleek and provides excellent airflow.
  • RAM: 64GB – Ample memory for running multiple applications and large datasets.
  • Cooling System: All-In-One (AIO) Cooling System – Keeps your system cool under heavy usage.
  • Storage: NVMe M.2 SSD Drives – Fast and reliable storage for quick data access.
  • Graphics Card: Nvidia RTX Series – Great for GPU-accelerated tasks, choose based on your specific needs and budget.

Low Budget Option: Second Hand Purchases

Cost: as low as $200.

Consider buying a second-hand laptop or desktop computer from eBay. Look for models that were high-end a few years ago, as they often still perform well for data engineering tasks. Once you’ve got your hands on one, installing an OS like Ubuntu can breathe new life into the hardware, making it a cost-effective option for learning and development. Installing Ubuntu is the secret cause here.

At the time of writing this, these are some good options on ebay.