Delta Live Tables (DLT) in Databricks

Delta Live Tables (DLT) in Databricks is a framework for building reliable, scalable, and simple data pipelines. It is built on top of Delta Lake and simplifies creating, managing, and monitoring data pipelines.

Key Features of Delta Live Tables

• Declarative Pipeline Development: Define transformations in a declarative way. Databricks manages dependencies and execution optimizations.

  
  from pyspark.sql.functions import col
  
  @dlt.table
  def clean_data():
      return spark.read("path/to/raw_data").filter(col("age") > 18)
              

• Managed Pipelines: DLT handles the entire lifecycle of a pipeline, including monitoring, failure handling, and data lineage tracking.
• Incremental Data Processing: Supports incremental processing of new data, improving efficiency, especially for streaming workloads.
• Automatic Data Quality Checks: Define data quality constraints to ensure that only valid data enters the pipeline.

  
  @dlt.expect("valid_age", "age > 0")
  def clean_data():
      return spark.read("path/to/raw_data").filter(col("age") > 18)
              

• Delta Lake Integration: Since DLT is built on Delta Lake, it benefits from Delta's ACID transactions, time travel, and schema enforcement.
• Pipeline Monitoring and Observability: Built-in tools provide visibility into the health and performance of the pipeline.
• Batch and Streaming Support: DLT supports both batch and streaming data sources, providing flexibility in how data is processed.
• Ease of Use: The declarative syntax simplifies the creation of ETL pipelines by abstracting away much of the complex orchestration.

Example Pipeline Workflow

import dlt
from pyspark.sql.functions import *

# Ingest raw data
@dlt.table
def raw_data():
    return spark.read("path/to/raw_data")

# Clean data by filtering invalid rows
@dlt.table
def clean_data():
    return dlt.read("raw_data").filter(col("age") > 18)

# Aggregate the cleaned data
@dlt.table
def aggregated_data():
    return dlt.read("clean_data").groupBy("country").agg(count("*").alias("user_count"))
    

Use Cases for Delta Live Tables

• ETL Pipelines: Automate data ingestion, transformation, and output for analytics or machine learning.
• Data Quality Enforcement: Enforce rules to ensure only clean and valid data is processed.
• Real-Time Streaming: Handle real-time data ingestion for immediate processing and analytics.
• Simplified Data Pipelines: Reduce manual operational overhead with automatic dependency management and optimizations.

© Kevin Luzbetak