Delta Live Tables in DataBricks Series — Part 1 — An Introductory Overview

Introduction

Delta Live Tables (DLT) in Databricks provides a simplified and scalable approach to building data pipelines. It enables data engineers to create reliable and efficient data workflows while ensuring data quality and governance. This overview will guide you through the key features, benefits, and practical implementation of Delta Live Tables.

What is Delta Live Tables?

Delta Live Tables is a framework in Databricks that facilitates the creation and management of data pipelines. It leverages Delta Lake, providing a declarative approach to defining data transformations and ensuring data quality. By abstracting the complexities of managing data flows, DLT simplifies the process of building and maintaining robust data pipelines.

Key Features

1. Declarative ETL

DLT allows you to define your data transformations in a declarative manner, focusing on the desired outcomes rather than the procedural steps. This approach enhances readability and maintainability of your ETL code.

Example:

CREATE STREAMING LIVE TABLE sales_orders_raw
COMMENT "The raw sales orders, ingested from /databricks-datasets."
TBLPROPERTIES ("quality" = "bronze")
AS
SELECT * FROM cloud_files("/databricks-datasets/retail-org/sales_orders/", "json", map("cloudFiles.i

2. Data Quality Enforcement

DLT includes built-in capabilities to enforce data quality rules, ensuring that only clean and valid data progresses through the pipeline. This is achieved using constraints and expectations.

Example:

CREATE STREAMING LIVE TABLE sales_orders_cleaned(
  CONSTRAINT valid_order_number EXPECT (order_number IS NOT NULL) ON VIOLATION DROP ROW
)
PARTITIONED BY (order_date)
COMMENT "The cleaned sales orders with valid order_number(s) and partitioned by order_date."
TBLPROPERTIES ("quality" = "silver")
AS
SELECT * FROM STREAM(LIVE.sales_orders_raw);

3. Automated Data Lineage

DLT provides automated tracking of data lineage, allowing you to trace data transformations and understand the origin and flow of data within your pipelines. This feature is crucial for debugging and auditing purposes.

4. Incremental Data Processing

DLT supports incremental data processing, enabling efficient handling of both batch and streaming data. This ensures that new data is processed as it arrives, minimizing latency and optimizing resource usage.

Benefits of Delta Live Tables

• Simplified Pipeline Management: With DLT, you can easily create and manage complex data pipelines without dealing with the intricacies of orchestration and scheduling. The declarative approach reduces the likelihood of errors and makes the code more intuitive.
• Enhanced Data Quality: DLT’s data quality enforcement mechanisms help maintain high standards of data integrity, reducing the risk of downstream issues caused by bad data.
• Scalability: DLT leverages the power of the cloud, allowing you to scale your data pipelines seamlessly. This ensures that your pipelines can handle increasing data volumes without compromising performance.
• Cost Efficiency: By optimizing resource usage and supporting incremental processing, DLT helps reduce the overall cost of running data pipelines. It eliminates the need for always-on infrastructure, further enhancing cost efficiency.

Practical Implementation

To get started with DLT, follow these steps:

1. Set Up Your Environment: Ensure you have access to a Databricks workspace and the necessary permissions to create clusters and run jobs.
2. Create a Pipeline Notebook: Create a new notebook in your Databricks workspace and define your initial pipeline.

Example:

CREATE STREAMING LIVE TABLE customers
COMMENT "The customers buying finished products, ingested from /databricks-datasets."
TBLPROPERTIES ("quality" = "bronze")
AS
SELECT * FROM cloud_files("/databricks-datasets/retail-org/customers/", "csv");

CREATE STREAMING LIVE TABLE sales_orders_raw
COMMENT "The raw sales orders, ingested from /databricks-datasets."
TBLPROPERTIES ("quality" = "bronze")
AS
SELECT * FROM cloud_files("/databricks-datasets/retail-org/sales_orders/", "json", map("cloudFiles.inferColumnTypes", "true"));

• Create and Configure the Pipeline: Use the Databricks UI to create a new Delta Live Table pipeline, specifying the notebook path and other settings.
• Run the Pipeline: Start the pipeline and monitor its execution through the Databricks interface. Check the logs and outputs to ensure everything is running smoothly.

Example Pipeline Configuration:

{
  "name": "Sales Order Pipeline",
  "notebook_path": "/path/to/dlt_retail_sales_pipeline",
  "storage_location": "/mnt/dlt-storage",
  "target": "default",
  "pipeline_mode": "Triggered",
  "cluster_size": {
    "min_workers": 2,
    "max_workers": 10
  }
}

Advanced Features

Handling Complex Workflows: DLT allows you to create complex workflows by chaining multiple transformations and tables. You can define dependencies between tables, ensuring that each transformation occurs in the correct order.

CREATE STREAMING LIVE TABLE sales_orders_cleaned(
  CONSTRAINT valid_order_number EXPECT (order_number IS NOT NULL) ON VIOLATION DROP ROW
)
PARTITIONED BY (order_date)
COMMENT "The cleaned sales orders with valid order_number(s) and partitioned by order_datetime."
TBLPROPERTIES ("quality" = "silver")
AS
SELECT f.customer_id, f.customer_name, f.number_of_line_items,
  TIMESTAMP(from_unixtime((cast(f.order_datetime as long)))) as order_datetime,
  DATE(from_unixtime((cast(f.order_datetime as long)))) as order_date,
  f.order_number, f.ordered_products, c.state, c.city, c.lon, c.lat, c.units_purchased, c.loyalty_segment
  FROM STREAM(LIVE.sales_orders_raw) f
  LEFT JOIN LIVE.customers c
      ON c.customer_id = f.customer_id;

Real-Time and Batch Processing

DLT supports both real-time and batch processing within the same pipeline. This flexibility allows you to handle various data processing requirements efficiently.

CREATE LIVE TABLE sales_order_in_la
COMMENT "Sales orders in LA."
TBLPROPERTIES ("quality" = "gold")
AS
SELECT city, order_date, customer_id, customer_name,
  SUM(ordered_products_explode.price) as sales,
  SUM(ordered_products_explode.qty) as quantity,
  COUNT(ordered_products_explode.id) as product_count
FROM (
  SELECT city, DATE(order_datetime) as order_date, customer_id, customer_name,
  EXPLODE(ordered_products) as ordered_products_explode
  FROM LIVE.sales_orders_cleaned
  WHERE city = 'Los Angeles'
)
GROUP BY order_date, city, customer_id, customer_name;

Monitoring and Observability

DLT provides comprehensive monitoring and observability features, allowing you to track the performance and health of your pipelines. Event logs and metrics give you insights into the operation of your pipelines, helping you identify and resolve issues quickly.

Conclusion

Delta Live Tables in Databricks offers a powerful and intuitive way to build and manage data pipelines. By leveraging its declarative approach, data quality enforcement, and scalability features, you can create robust and efficient data workflows. Whether you are dealing with batch or real-time data, DLT provides the tools and capabilities to meet your data processing needs effectively.

By following this introductory overview and practical examples, you can get started with Delta Live Tables and unlock the potential of your data pipelines in Databricks. For more detailed information and advanced features, refer to the official Databricks documentation and resources.

Read Part 2 here