From Databricks to Dashboard

A Streamlined & Collaborative Workflow for Data Professionals

🚀The Modern Data Workflow in 3 Phases

🔎1. Discovery & Collaboration

Leverage existing assets. Don't reinvent the wheel. Find queries, tables, and business logic that your team has already built and vetted.

📝2. Development & Saving

Build robust, reusable queries with clear documentation. Save your work as a View to create a reliable data source for reporting.

📊3. Connection & Visualization

Bring your data to life in Power BI with live, interactive queries that tap directly into the power of the Databricks engine.

1️⃣Phase 1: Discovery & Collaboration

Before writing a single line of new code, a smart analyst investigates what already exists. This ensures consistency across the organization and dramatically speeds up development time. Your primary tools are built directly into the Databricks workspace.

A guide on how to manage and organize your data assets using Catalogs and Schemas within Databricks Unity Catalog.

Learn how to use the Query History feature to analyze past queries, understand performance, and see what your teammates are working on.

A beginner-friendly introduction to the Databricks SQL Editor, showing you how to start writing, saving, and managing your queries.

2️⃣Phase 2: Development & Saving

Once you've gathered existing logic, it's time to build. The goal is to create a query that is not only accurate but also easy for others (and your future self) to understand. The best practice is to save your final query as a View, making it a reusable, logical layer for reporting.

Best Practice: Create a View

A View acts like a virtual table, simplifying access for Power BI and other tools.

CREATE OR REPLACE VIEW analytics.sales.vw_quarterly_sales_summary AS

-- CTE to aggregate daily sales data
WITH daily_sales AS (
  SELECT
    transaction_date,
    product_id,
    SUM(sale_amount) AS total_sales
  FROM prod.commerce.transactions
  GROUP BY 1, 2
)
-- Final SELECT statement for the view
SELECT
  ds.transaction_date,
  p.product_name,
  ds.total_sales
FROM daily_sales ds
LEFT JOIN prod.commerce.products p
  ON ds.product_id = p.id;

This tutorial covers the fundamentals of creating and managing standard Views in Databricks, a best practice for reusable logic.

An advanced look at how you can leverage Large Language Models (LLMs) directly within your SQL queries for powerful text analysis.

A practical guide on executing Spark SQL within a Databricks notebook to query data from various sources in the Lakehouse.

3️⃣Phase 3: Connection & Visualization

With your view created in Databricks, the final step is to connect it to Power BI. This allows you to build interactive reports and dashboards, leveraging the high-performance Databricks engine for all calculations.

See the seamless integration between Databricks and Power BI, enabling powerful, governed analytics on your lakehouse data.

Learn the best practices for authentication, performance, and security when connecting Power BI to your Databricks environment.

A step-by-step walkthrough of using the Partner Connect feature in Databricks to quickly and easily set up a connection to Power BI.