From Pipeline to Insights: Accessing Your Data in Databricks (Part 3)
- Josh Adkins
- Aug 25
- 2 min read
We’ve come a long way:
Part 1 → built the end-to-end ETL pipeline (bronze to gold, SCDs, quality checks, schema handling).
Part 2 → stress-tested it against real-world data scenarios.
Now in Part 3, it’s time for the payoff: exploring the results.
Because the whole point of a pipeline isn’t just to move and clean data — it’s to make that data usable for analysis and decision-making.
Three Simple Ways to Access Your Data in Databricks
In this walkthrough, I show three practical ways to access processed data in Databricks. These methods let you (or your analysts, data scientists, and business partners) start working with the outputs immediately:
1️⃣ Directly from notebooks → Ideal for Python users and data scientists who want to transform, visualize, or model right away.
2️⃣ With SQL queries → Perfect for analysts who prefer working with SQL directly against curated tables.
3️⃣ Through the Databricks catalog → Centralized access that makes it easy to discover, govern, and share datasets across teams.
SQL or Python? The Choice is Yours
One of the key benefits here is flexibility. Some teams live in Python, others in SQL. With this setup, both are first-class options:
Run SQL commands to query tables, validate transformations, and create aggregates.
Or use Python to run the same logic programmatically and integrate results into notebooks or downstream ML workflows.
The output is the same: clean, trusted, production-ready data that’s accessible in the way that best fits your team.
Why This Stage Matters
The best pipelines don’t just land data—they empower people to use it. By standardizing outputs and making them easily accessible, you remove friction between data engineering and data consumption.
Analysts get the freedom to query.
Data scientists get clean inputs for models.
Leaders get faster insights with fewer delays.
This is where the pipeline becomes more than infrastructure—it becomes impact.
See It in Action
Final Thought
The lifecycle of a pipeline isn’t complete until the outputs are in the hands of the people who need them. In Part 3, we close the loop: turning raw data into insights that drive real outcomes.
💬 How does your team currently access and share pipeline outputs? Do you lean more toward notebooks, SQL, or catalogs?
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