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Building End-to-End ETL Pipelines in Databricks with Delta Live Tables (DLT)

In the evolving landscape of data engineering, Delta Live Tables (DLT) in Databricks has emerged as a game-changer for building robust, scalable, and reliable ETL pipelines. DLT abstracts much of the complexity behind data pipeline management, allowing developers to focus more on business logic than infrastructure.

In this blog, we’ll guide you step-by-step on how to build an end-to-end ETL pipeline using DLT, from basics to advanced features, including data quality, orchestration, error handling, and CDC.

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🚀 Table of Contents

1. What is Delta Live Tables (DLT)?
2. Why Use DLT for ETL Pipelines?
3. Prerequisites
4. Step-by-Step Guide to Building DLT Pipelines
   • a. Define the source
   • b. Create your DLT notebook
   • c. Use decorators: @dlt.table, @dlt.expect
   • d. Handle streaming vs batch
   • e. Apply transformations
   • f. Add data quality checks
   • g. Materialize views/tables
5. Scheduling & Orchestration
6. Advanced Concepts
   • a. Change Data Capture (CDC)
   • b. Incremental processing
   • c. Autoloader integration
   • d. Monitoring & logging
7. Best Practices
8. Conclusion

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🔍 What is Delta Live Tables (DLT)?

Delta Live Tables is a framework by Databricks that allows you to declaratively define data pipelines using Python or SQL. DLT handles orchestration, error handling, monitoring, and optimizations out of the box. It supports batch and streaming data ingestion seamlessly.

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✅ Why Use DLT for ETL Pipelines?

• Simplifies orchestration with a declarative approach
• Built-in data quality enforcement with expectations
• Supports schema evolution
• Works well with streaming + batch (Lambda/Delta architecture)
• Native integration with Unity Catalog, Autoloader, and Delta Lake
• Automated lineage tracking and monitoring

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📘 Prerequisites

Before you begin, ensure:

• You have access to Databricks workspace with DLT enabled
• Workspace is Unity Catalog-enabled (recommended)
• Familiarity with Python or SQL
• Basic knowledge of Delta Lake

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🔧 Step-by-Step Guide to Building DLT Pipelines

1️⃣ Define the Source

Create a source directory or configure your Autoloader path (e.g., files landing in /mnt/raw/events/).

source_path = "/mnt/raw/events"

2️⃣ Create a DLT Notebook

In Databricks:

• Go to Workflows > Delta Live Tables
• Create a Pipeline
• Link it to a notebook where your ETL logic will reside

3️⃣ Start with a Table using @dlt.table

import dlt
from pyspark.sql.functions import *

@dlt.table
def raw_events():
    return (
        spark.readStream.format("cloudFiles")
        .option("cloudFiles.format", "json")
        .load("/mnt/raw/events")
    )

4️⃣ Add Transformations

Define intermediate steps using additional @dlt.table.

@dlt.table
def clean_events():
    df = dlt.read("raw_events")
    return df.filter("event_type IS NOT NULL").withColumn("ingestion_date", current_date())

5️⃣ Add Data Quality with @dlt.expect

@dlt.table
@dlt.expect("valid_user_id", "user_id IS NOT NULL")
def transformed_events():
    return dlt.read("clean_events").withColumn("event_id", expr("uuid()"))

DLT will automatically track failed expectations and surface them in the UI.

6️⃣ Handle Streaming vs Batch

DLT intelligently handles batch and streaming, but you can specify explicitly:

@dlt.table
def batch_table():
    return spark.read.format("parquet").load("/mnt/batch/source")

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⏰ Scheduling & Orchestration

In the DLT pipeline UI, you can:

• Set trigger interval (e.g., every 15 mins)
• Enable continuous mode for real-time streaming
• Add email alerts for failures

DLT integrates with Jobs API for CI/CD and Airflow orchestration.

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⚙️ Advanced Concepts

a. Change Data Capture (CDC)

DLT supports CDC via MERGE semantics.

@dlt.table
def dim_customers():
    source = dlt.read_stream("cdc_raw")
    return source.dropDuplicates(["customer_id"]).withColumn("updated_at", current_timestamp())

b. Incremental Processing with Checkpoints

DLT internally manages checkpoints when using streaming input sources.

c. Autoloader Integration

@dlt.table
def autoloaded_data():
    return spark.readStream.format("cloudFiles") \
        .option("cloudFiles.format", "csv") \
        .option("cloudFiles.schemaLocation", "/mnt/schemas") \
        .load("/mnt/autoload-path")

d. Monitoring & Logging

• Use SHOW LIVE TABLES to get table status
• Use LIVE TABLE EVENT LOG for audit trail
• Integration with Databricks SQL for real-time monitoring dashboards

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💡 Best Practices

Area	Best Practice
Naming	Use consistent naming conventions for DLT functions
Testing	Test transformations locally before deploying to DLT
Modularity	Split ETL logic into multiple stages
Expectations	Define data quality at each stage
Documentation	Use notebook markdown to explain each step
Security	Integrate with Unity Catalog for RBAC

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✅ Conclusion

Delta Live Tables radically transforms how modern data teams build ETL pipelines. From ingestion to transformation to delivery, DLT:

• Abstracts complexity,
• Improves reliability,
• Supports real-time & batch data,
• And brings governance and monitoring out-of-the-box.

Whether you’re a beginner starting with your first ETL flow or an enterprise scaling data pipelines across hundreds of sources—DLT is your ideal companion in the Databricks ecosystem.

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