You want to improve your data warehousing skills. What’s the best way to get started?

To be good at data warehousing you must read the book - “The Data Warehouse Toolkit” It will help you not only with basics but also teach you how to build one.

Data warehouses are used for analytics purposes. Think of an analytical problem and collect data from different sources using data warehousing tool like Snowflake. Use the warehouse to perform SQL queries and understand the data trends and then using visualizations tools like Tableau and Looker to display them.

These are some fundamentals to learn the basics! Understand Core Concepts: Grasp the fundamental differences between data warehousing and data lakes, including their respective benefits and challenges. Learn Architectures and Models: Familiarize yourself with common data warehouse architectures, models (e.g., star schema, snowflake schema), and the data warehouse lifecycle. Master Key Terms: Dive into key terms like dimensional modeling, fact tables, dimension tables, ETL processes, OLAP operations, and data quality measures. Utilize Resources: Leverage online courses, books, and blogs that offer in-depth coverage of these areas, including practical examples and exercises to apply what you learn.

There are a lot of good books out there, you need good foundations to sharpen your data warehouse skills, some recommendations: - Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems - The Data Warehouse Toolkit - Data Warehouse Design: Modern Principles and Methodologies

Start by solidifying your understanding of foundational data warehousing concepts like data modeling, ETL (Extract, Transform, Load) processes, data quality management, and data security. Numerous online courses, tutorials, and books are available to cover these topics comprehensively. Consider platforms like Coursera, edX, or textbooks by Ralph Kimball or William Inmon.

Improving data warehousing skills involves hands-on learning with tools - for me it was DatabricksSQL, among others: Master SQL for data manipulation and analysis. Get to know ETL tools for extracting, transforming and loading data. Understand data modelling tools for designing and optimizing schemas. Use BI tools to create insightful dashboards and reports. Explore Databricks features such as optimized SQL processing and scalable ETL pipelines. Create interactive BI dashboards with Databricks, Tableau or PowerBI for enhanced data visualization and analysis.

Here is a step by step procedure I followed to practice with tools: Learn SQL: Gain proficiency in SQL for data manipulation and analysis, as it's fundamental to interacting with data warehouses Explore ETL Tools: Familiarize yourself with ETL (Extract, Transform, Load) tools to manage data flow from various sources into the data warehouse. Tools include but are not limited to Power Query, SSIS, ADF, Azure Databricks, etc. Master Data Modeling Tools: Use data modeling tools to design, implement, and optimize data warehouse schemas, including understanding star and snowflake schemas. Utilize BI Tools: Develop skills in Business Intelligence (BI) tools to create insightful dashboards and reports, turning data into actionable intelligence.

Master tools like relational database management systems (RDBMS), cloud-based data warehouse platforms, data integration tools, data modeling tools, and business intelligence tools. Learn SQL for data manipulation and analysis. Gain proficiency in ETL (Extract, Transform, Load) tools for extracting, transforming, and loading data from different sources. Understand how to use data modeling tools to design and optimize data warehouse schemas. Familiarize yourself with BI (Business Intelligence) tools for creating dashboards and reports.

The most foundation piece of a data warehouse is SQL, learn well SQL, understand how to answer business problems with it, start with some simple examples, BigQuery has public datasets and a generous free tier where you can play with it around. Then understand data modelling, learn how to optimise your workloads, improve costs, the efficient and maintainability of it.

To truly enhance your data warehousing skills, engaging in real-world projects is key: Tackle Real-World Problems: Choose projects that allow you to solve actual business questions. Build Your Own Data Warehouse: Design and implement a data warehouse from scratch, focusing on a particular industry or interest to apply and test your skills comprehensively. Participate in Competitions: Engage with online communities and platforms like Kaggle, DataCamp, or Coursera that host data warehousing projects and competitions, providing a platform to learn and compete. Showcase Your Work: Use these projects as a portfolio to demonstrate your capabilities to potential employers or clients.

Pick one tool and explore the best practices that the tool has to offer instead of learning many tools, Understand the benefits of cloud data warehousing,the optimisation and costs.Try exploring daas options

Apply your learning through practical projects. Set up a personal data warehouse using open-source tools like PostgreSQL or MySQL, and practice data modeling, ETL implementation using tools like Apache Airflow, and data analysis using SQL or Python.

Real-world projects: Working on real-world projects is key to enhancing data warehousing skills. Look for challenging projects that allow you to apply your knowledge effectively. Whether it's using public datasets or your own data, the goal is to build a data warehouse that addresses specific business needs or provides valuable insights. Creating projects: Don't hesitate to create your own projects tailored to your interests and requirements. This allows for greater flexibility and customization in addressing particular challenges or exploring specific domains or industries.

The data warehousing landscape is constantly evolving. Keep yourself updated with the latest trends, technologies, and best practices by attending online webinars, conferences, or following industry publications and blogs.