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T-SQL Tuesday #190–Mastering a New Technical Skill

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It’s time for T-SQL Tuesday again and this time Todd Kleinhans has a great invitation that is near and dear to my heart: mastering a new or existing technical skill. That’s been a lot of what I try to inspire people to do at SQL Server Central.

Make a plan and start learning. And respond to Todd’s invitation and write down your plan and share it. Start a blog, use Linked In, whatever. Spread the word on socials as well.

If you want to host, I’m always looking for hosts for T-SQL Tuesday. Ping me on Twitter/X, BlueSky, or LinkedIn.

Mastering a New Tech Skill

Like Todd, I’m interested in AI and I think it will dramatically change the world in the coming future. I also think it’s a bit of a technical skill that is important to learn. I wrote about this a bit in last month’s post.

How do I work with a GenAI model and improve my technical skills? The easy answer is more and more. I’ve been having more conversations with Claude, usually looking for ways to help me solve a problem or write code, and then ask the GenAI to explain things.

However, I wrote awhile ago about an experiment in helping someone else learn something: Can an AI Help Me Find a Job?. For me, I’ve been looking a bit more at DataBricks, as I hear this from clients all the time. I wanted to gain some skill here, so I decided to ask Claude to help me.

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I got a good outline of things to do across a few months. I then asked for references and got some:

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The next stage for me is to start embarking on this journey a few nights a week and learn some things that might help me both in my job, and potentially in a future position if I need one.

My Complete Outline from Claude

Here’s the end result, with links.

Databricks Learning Outline with Resources

Phase 1: Foundations (1-2 weeks)

Understanding the Basics

  • What is Databricks and why it’s used
  • Core concepts: clusters, notebooks, workspaces, and Apache Spark
  • Databricks architecture and modern data stack integration

Key Resources:

Phase 2: Getting Started (2-3 weeks)

Hands-on Basics

  • Navigating workspace interface
  • Creating and managing clusters
  • Working with notebooks
  • Basic data import methods

Key Resources:

Phase 3: Data Analysis Fundamentals (3-4 weeks)

Core Analytics Skills

  • Data exploration and cleaning
  • Working with different data formats
  • Basic SQL and PySpark operations

Key Resources:

Phase 4: Intermediate Techniques (4-5 weeks)

Advanced Analytics

  • Complex transformations and ETL
  • Streaming data and machine learning
  • Performance optimization

Key Resources:

Phase 5: Production and Best Practices (2-3 weeks)

Professional Development

  • Job scheduling and workflows
  • Security and monitoring
  • Integration patterns

Key Resources:

Additional Learning Platforms:
Certification Path:
Community and Support:
Getting Started Steps:
  1. Sign up for Databricks Free Edition: Visit the Databricks Free Edition signup page and pick your preferred signup method Databricks Free Edition | Databricks on AWS
  2. Access free training: Free unlimited access to training content on Databricks Academy provides on-demand learning experiences for all skill levels Databricks Launches Free Edition and Announces $100 Million Investment to Develop the Next Generation of Data and AI Talent – Databricks
  3. Join the community: Connect with other learners and experts
  4. Start with sample datasets: Practice with built-in datasets before using your own data

The great news is that all self-paced training across AI, data engineering, and more is now free for learners Databricks Training & Certification Programs | Databricks, making it easier than ever to get started with Databricks!

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