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PySparkSnowflakeDatabricks

Delta Live Tables (DLT Pipelines) — Hands-On Concepts

🚦 A Story to Begin — “What If Pipelines Built Themselves?”​

Imagine you’re a data engineer managing 15 different ETL jobs.

Some run every 10 minutes.
Some every hour.
Some depend on each other.
Some fail silently at 2 a.m.
Some corrupt data when schemas change.

You spend most of your day:

  • Fixing broken jobs
  • Restarting stuck tasks
  • Hunting for which notebook failed
  • Manually managing dependencies
  • Checking if data is fresh

Now imagine a system that:

  • Understands your pipeline
  • Orders the tasks automatically
  • Handles retries & failures
  • Tracks quality rules
  • Keeps data lineage
  • Manages Medallion flows
  • And just works

That system is Delta Live Tables (DLT).

🌟 What Is Delta Live Tables?​

Delta Live Tables (DLT) is Databricks’ framework for building reliable, automated, and production-ready data pipelines with minimal code.

It’s like telling Databricks:

“Here are my tables. Here’s how they relate.
You take care of everything else.”

DLT handles:

✔ Orchestration​

Automatically orders and schedules all transformations.

✔ Data Quality​

Built-in rules to validate and quarantine bad records.

✔ Dependency Graph​

DLT understands upstream → downstream flow.

✔ Auto-Scaling + Recovery​

If a step fails, DLT retries intelligently.

✔ Incremental Processing​

Uses Delta Lake efficiently without extra code.

✔ Lineage​

Visual graph of your pipeline — super helpful for debugging.

🧩 DLT in the Medallion Architecture​

DLT fits perfectly into:

  • Bronze ingestion
  • Silver cleaning
  • Gold aggregation

You write simple Python or SQL commands, and Databricks turns them into a production pipeline.

🧪 DLT: The Simplest Example (SQL)​

CREATE OR REFRESH LIVE TABLE bronze_orders
AS SELECT * FROM cloud_files("/mnt/raw/orders", "json");

Silver Table​

CREATE OR REFRESH LIVE TABLE silver_orders
AS SELECT
  CAST(orderId AS INT) AS order_id,
  CAST(amount AS DOUBLE) AS amount,
  orderDate
FROM LIVE.bronze_orders;

Gold Table​

CREATE OR REFRESH LIVE TABLE gold_daily_sales
AS SELECT
  DATE(orderDate) AS day,
  SUM(amount) AS daily_revenue
FROM LIVE.silver_orders
GROUP BY day;

That’s it. No job scheduling. No orchestration code. No workflow wiring.

DLT figures it all out.

🛠 How DLT Works Under the Hood​

When you create a pipeline:

  1. You choose your source code (SQL or Python)
  2. DLT reads every table definition
  3. DLT builds a dependency graph
  4. DLT executes in the correct order
  5. It applies schema checks and quality rules
  6. It writes results into Bronze/Silver/Gold tables
  7. It maintains run history + lineage automatically

📊 Data Quality with DLT (Expectations)​

DLT has built-in quality rules called Expectations.

Example:

CREATE OR REFRESH LIVE TABLE silver_customers
(
  CONSTRAINT valid_email EXPECT (email LIKE '%@%')
)
AS SELECT * FROM LIVE.bronze_customers;

You can choose what happens to invalid rows:

  • FAIL → Block the pipeline
  • DROP → Remove bad rows
  • QUARANTINE → Put them in a separate table

This makes data quality self-documenting and automatic.

⚙️ Incremental Processing (Automatically)​

No need to write “read only new files” logic.

DLT automatically understands:

  • What data has been processed
  • What new data has arrived
  • What needs to be reprocessed

You focus on transformations — DLT handles the state management.

🔁 Continuous vs Triggered Pipelines​

DLT supports two modes:

1️⃣ Triggered (Batch) Pipelines​

Runs only when triggered manually or on schedule.

2️⃣ Continuous Pipelines​

Runs like a stream.

Perfect for real-time dashboards or near-real-time ingestion.

🌈 Visual Lineage & Monitoring​

DLT generates a beautiful lineage graph:

bronze_orders → silver_orders → gold_daily_sales

You can click each table and see:

  • Code definition
  • Schema
  • History
  • Quality checks
  • Execution stats

This makes debugging dramatically easier.

🧠 When Should You Use DLT?​

Use DLT when you want:

✔ Automated pipeline management ✔ Easy data quality enforcement ✔ Clear lineage and visual tracking ✔ Less orchestration code ✔ Fewer failures ✔ Guaranteed reliability ✔ SQL or Python simplicity

Don't use DLT if:

✖ You want full custom orchestration control ✖ Your transformations must run outside Databricks ✖ You need extremely complex logic not suited for SQL/Python

📘 Summary​

  • DLT is Databricks’ framework for automated, reliable pipelines.
  • You write simple SQL/Python; DLT manages orchestration, quality, and dependencies.
  • It works perfectly with the Bronze/Silver/Gold model.
  • It ensures bad data is detected, quarantined, or rejected.
  • It automatically handles incremental updates, lineage, and execution tracking.
  • DLT dramatically reduces pipeline maintenance and failure headaches.

Delta Live Tables makes data pipelines simple, safe, and scalable — the way modern engineering should be.

👉 Next Topic

Materialized Views in Databricks (SQL + Pipelines)