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).

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🌟 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.

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🧩 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.

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🧪 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.

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🛠 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

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📊 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.

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⚙️ 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.

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🔁 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.

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🌈 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.

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🧠 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

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📘 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.

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👉 Next Topic

Materialized Views in Databricks (SQL + Pipelines)