How to Build a DLT Pipeline in Databricks Step by Step

Delta Live Tables (DLT) represents Databricks’ declarative framework for building reliable, maintainable data pipelines. Unlike traditional ETL approaches that require extensive boilerplate code and manual orchestration, DLT allows you to focus on transformation logic while the framework handles dependencies, error handling, data quality, and infrastructure management automatically. This paradigm shift from imperative to declarative pipeline development dramatically reduces complexity and accelerates time-to-production for data engineering teams. Whether you’re ingesting streaming data, building medallion architectures, or implementing complex transformations, understanding how to construct DLT pipelines unlocks the full potential of modern data engineering on Databricks.

Understanding Delta Live Tables Fundamentals

Delta Live Tables operates on a fundamentally different philosophy than traditional Spark-based pipelines. Instead of writing procedural code that executes step-by-step, you declare what your data should look like at each stage, and DLT figures out how to make it happen. This declarative approach means you define tables and views using SQL or Python, specify dependencies through queries that reference other tables, and DLT constructs the execution graph automatically.

The framework introduces two primary abstractions: tables and views. Tables are materialized datasets persisted to storage, forming durable stages in your pipeline. Views are temporary datasets that exist only during pipeline execution, useful for intermediate transformations that don’t need persistence. Both support the same transformation logic, but tables provide queryable results after pipeline completion while views save storage costs for transient data.

DLT manages the complete lifecycle of your data assets. When you define a table, DLT creates it, manages its schema evolution, handles updates according to the processing mode you specify (streaming or batch), and maintains lineage metadata automatically. Quality constraints, when specified, enforce data quality rules and can quarantine bad records or fail the pipeline based on severity. This comprehensive management eliminates much of the operational overhead traditionally associated with data pipelines.

The framework’s dependency resolution capability represents one of its most powerful features. When you write a query that references other DLT tables, the framework automatically understands those dependencies and schedules execution in the correct order. This eliminates the manual dependency management and complex orchestration logic that plagues traditional pipeline implementations.

Setting Up Your Development Environment

Before building your first DLT pipeline, proper environment setup ensures smooth development. Start by creating a dedicated notebook in your Databricks workspace. Navigate to the Workspace section, create a new folder for your DLT project, and create a Python or SQL notebook within it. The notebook will contain your table definitions and transformation logic, serving as the source code for your pipeline.

DLT notebooks differ from standard Databricks notebooks in important ways. They cannot be executed interactively cell-by-cell like regular notebooks—DLT notebooks only run within the context of a pipeline. This constraint enforces the declarative paradigm and prevents the common pitfall of notebooks that only work when cells execute in a specific order. Your DLT notebook should be self-contained, with all table definitions independent of execution order.

Import the necessary libraries at the top of your notebook. For Python-based DLT pipelines:

import dlt
from pyspark.sql.functions import *
from pyspark.sql.types import *

For SQL-based pipelines, no imports are necessary—SQL DLT syntax is built into the Databricks SQL engine. Choose Python when you need complex transformation logic, custom functions, or programmatic pipeline generation. Choose SQL for straightforward transformations where SQL’s expressiveness suffices. Many teams mix both, using SQL for simple transformations and Python for complex logic within the same pipeline.

Configure your storage location for pipeline outputs. DLT stores table data, checkpoints, and metadata in the location you specify during pipeline creation. Best practice involves using a dedicated storage path following your organization’s data lake structure. Consider using separate paths for development, staging, and production environments to prevent accidental overwrites and maintain clear boundaries between environments.

Building Bronze Layer: Raw Data Ingestion

The bronze layer forms your pipeline’s foundation, ingesting raw data in its native format with minimal transformation. Bronze tables preserve data exactly as received, providing a reliable source of truth for downstream processing. This layer typically handles streaming sources like Kafka, event hubs, or cloud storage locations where files arrive continuously.

Creating a streaming bronze table for JSON files demonstrates the basic pattern:

@dlt.table(
    name="raw_events_bronze",
    comment="Raw event data from cloud storage",
    table_properties={
        "quality": "bronze"
    }
)
def raw_events_bronze():
    return (
        spark.readStream
            .format("cloudFiles")
            .option("cloudFiles.format", "json")
            .option("cloudFiles.schemaLocation", "/mnt/schemas/events")
            .load("/mnt/raw/events/")
    )

The @dlt.table decorator declares this function defines a DLT table. The function name becomes the table name unless overridden by the name parameter. The function returns a DataFrame representing the table’s contents—DLT handles all persistence, schema management, and incremental processing. Auto Loader (cloudFiles format) provides efficient incremental ingestion, automatically discovering new files and tracking which files have been processed.

For batch processing scenarios, the pattern adjusts slightly:

@dlt.table(
    name="historical_sales_bronze"
)
def historical_sales_bronze():
    return spark.read.format("parquet").load("/mnt/historical/sales/")

Bronze layer best practices include preserving all source columns, adding metadata columns for lineage tracking, and using permissive schema inference that captures unexpected fields. Avoid filtering or transformation at this layer—capture everything for maximum flexibility in downstream processing.

Implementing Silver Layer: Data Cleansing and Validation

The silver layer transforms raw bronze data into clean, validated datasets ready for analysis. This layer applies business rules, enforces data quality constraints, handles schema evolution, and structures data for efficient querying. Silver tables represent the “single source of truth” for validated data within your organization.

Data quality constraints form the cornerstone of silver layer processing. DLT provides expectations—declarative data quality rules that validate incoming data. Here’s a silver table with comprehensive quality constraints:

@dlt.table(
    name="events_silver",
    comment="Validated and cleaned event data"
)
@dlt.expect_or_drop("valid_timestamp", "timestamp IS NOT NULL")
@dlt.expect_or_drop("valid_user_id", "user_id IS NOT NULL AND length(user_id) = 36")
@dlt.expect("reasonable_amount", "amount >= 0 AND amount < 1000000")
def events_silver():
    return (
        dlt.read_stream("raw_events_bronze")
            .select(
                col("timestamp").cast("timestamp").alias("event_timestamp"),
                col("user_id"),
                col("event_type"),
                col("amount").cast("decimal(10,2)"),
                col("metadata"),
                current_timestamp().alias("processed_at")
            )
            .withColumn("event_date", to_date(col("event_timestamp")))
    )

The expect_or_drop expectations drop rows that fail validation, ensuring only quality data reaches downstream consumers. The expect expectation logs violations but allows rows to pass through—useful for tracking issues without blocking pipeline execution. DLT tracks all expectation violations in pipeline metrics, enabling monitoring of data quality trends over time.

This example demonstrates several silver layer patterns: explicit schema definition through select and cast operations, derived columns for partitioning (event_date), and audit columns tracking processing time. The dlt.read_stream() function creates the dependency on the bronze table, and DLT handles incremental processing automatically—only new bronze records are processed on subsequent runs.

Handling schema evolution gracefully prevents pipeline breaks when source schemas change. DLT’s schema inference and evolution capabilities automatically accommodate new columns in source data. Configure schema evolution behavior through table properties:

@dlt.table(
    name="adaptive_events_silver",
    table_properties={
        "delta.autoOptimize.optimizeWrite": "true",
        "delta.columnMapping.mode": "name"
    }
)
def adaptive_events_silver():
    return dlt.read_stream("raw_events_bronze").select("*")

Column mapping mode “name” allows renaming and dropping columns without breaking downstream dependencies, providing flexibility for schema changes without pipeline modifications.

Creating Gold Layer: Business-Level Aggregations

Gold tables provide business-ready datasets optimized for specific analytical use cases. This layer implements aggregations, joins across multiple sources, and denormalization that accelerates query performance for dashboards and reports. Gold tables typically represent dimension and fact tables in a star schema, curated datasets for machine learning, or pre-computed metrics for operational dashboards.

Building aggregated metrics demonstrates gold layer patterns:

@dlt.table(
    name="daily_user_metrics_gold",
    comment="Daily aggregated user activity metrics"
)
def daily_user_metrics_gold():
    return (
        dlt.read("events_silver")
            .groupBy("user_id", "event_date")
            .agg(
                count("*").alias("event_count"),
                sum("amount").alias("total_amount"),
                countDistinct("event_type").alias("unique_event_types"),
                min("event_timestamp").alias("first_event"),
                max("event_timestamp").alias("last_event")
            )
    )

Notice this gold table uses dlt.read() rather than dlt.read_stream(). Gold aggregations typically work better with batch processing since they need to see complete sets of data for accurate aggregation. DLT handles this mode difference seamlessly, buffering streaming silver data appropriately for batch gold processing.

Joining multiple sources enriches gold tables with comprehensive business context:

@dlt.table(
    name="enriched_transactions_gold"
)
def enriched_transactions_gold():
    transactions = dlt.read("transactions_silver")
    users = dlt.read("users_silver")
    products = dlt.read("products_silver")
    
    return (
        transactions
            .join(users, "user_id")
            .join(products, "product_id")
            .select(
                col("transaction_id"),
                col("transaction_date"),
                col("user_id"),
                col("user_name"),
                col("user_segment"),
                col("product_id"),
                col("product_name"),
                col("product_category"),
                col("amount"),
                col("quantity")
            )
    )

This denormalized structure eliminates joins in downstream queries, dramatically improving dashboard and report performance. Gold tables trade storage for query speed, materializing the results of complex joins so analysts access pre-computed results.

Configuring and Running Your DLT Pipeline

With your notebook complete, create the DLT pipeline through the Databricks UI or API. Navigate to Workflows in the left sidebar, click “Delta Live Tables,” then “Create Pipeline.” The configuration form requests several critical settings that determine pipeline behavior.

Pipeline configuration includes:

• Pipeline name: Descriptive identifier for the pipeline
• Product edition: Choose between Core, Pro, and Advanced based on features needed
• Pipeline mode: Triggered (runs on schedule or manual trigger) or Continuous (runs constantly processing new data)
• Notebook libraries: Path to your DLT notebook(s)
• Storage location: Where DLT persists tables and metadata
• Target schema: Database where tables are created
• Cluster configuration: Compute resources for pipeline execution

Advanced configuration options control pipeline behavior in detail. Development mode accelerates testing by reusing clusters and disabling retries—perfect for iterative development but inappropriate for production. Channel selection determines whether you use the current release or preview features. Photon acceleration significantly improves performance for SQL-heavy pipelines.

Start your pipeline through the UI or programmatically via the API. The pipeline interface displays real-time execution progress, showing dependencies as a graph, tables as nodes, and data flow as edges. Click any table to view its metrics, schema, and sample data. The event log provides detailed execution history including timestamps, records processed, and any errors encountered.

Monitor pipeline health through built-in observability features. DLT tracks metrics for every table: rows processed, expectation violations, processing time, and data quality scores. Access these metrics through the pipeline UI or query them programmatically from the DLT metadata tables for custom monitoring dashboards.

Handling Common Scenarios and Advanced Patterns

Change Data Capture (CDC) represents a common advanced pattern where DLT excels. Apply CDC changes from source systems to maintain up-to-date copies of operational data:

@dlt.table(
    name="customers_silver"
)
def customers_silver():
    return dlt.read_stream("customers_cdc_bronze")

dlt.apply_changes(
    target="customers_silver",
    source="customers_cdc_bronze",
    keys=["customer_id"],
    sequence_by="update_timestamp",
    apply_as_deletes="operation = 'DELETE'",
    except_column_list=["operation", "update_timestamp"]
)

The apply_changes function handles inserts, updates, and deletes automatically, maintaining the current state of each customer based on the CDC sequence. This pattern eliminates complex merge logic you’d otherwise write manually.

Slowly Changing Dimensions (SCD Type 2) track historical changes by creating new rows for each change:

dlt.apply_changes(
    target="products_scd",
    source="products_cdc_bronze",
    keys=["product_id"],
    sequence_by="update_timestamp",
    stored_as_scd_type="2"
)

DLT adds effective date columns automatically, maintains current flags, and handles all the complexity of SCD implementation.

Parameterization enables reusable pipeline patterns. Access pipeline parameters in your notebook through configuration values:

source_path = spark.conf.get("source.path")
environment = spark.conf.get("environment", "dev")

@dlt.table(name=f"events_{environment}_bronze")
def events_bronze():
    return spark.readStream.format("cloudFiles").load(source_path)

Set parameters in the pipeline configuration or pass them at runtime, enabling the same notebook to power multiple pipelines for different environments or data sources.

Conclusion

Building DLT pipelines in Databricks represents a paradigm shift from traditional ETL development to declarative data engineering. By focusing on what your data should be rather than how to process it, DLT eliminates enormous complexity from pipeline development and operations. The framework’s automatic dependency management, built-in data quality enforcement, and comprehensive observability create robust pipelines with less code and maintenance burden than traditional approaches.

Success with DLT comes from embracing its declarative philosophy and organizing pipelines around the medallion architecture pattern. Start simple with bronze ingestion, add silver transformations with quality constraints, and create gold aggregations that serve business needs directly. The framework handles the rest—dependencies, orchestration, incremental processing, and error recovery—allowing you to focus on delivering business value through high-quality data products.