Maximizing Data Pipeline Reliability with Delta Live Table: Key Features

Delta Live Table Source: Databricks

Delta Live Table is an ETL (Extract, Transform, Load) framework that automates the process of building reliable data pipelines. It uses a simple declarative approach to build pipelines for batch and streaming data using SQL. In the previous article, we talked about Procedural vs Declarative ETL. In this article, we will discuss Delta Live Table and its key features

Features of Delta Live Table

Delta Live Table makes it easy to turn simple SQL queries into production-ready ETL pipelines. It automates away virtually all of the inherent operational complexity of building such pipelines, making it easier for data analysts and data engineers to focus on getting value from data.

The key features of Delta Live Table are:

1. Declarative approach: Delta Live Table uses a simple declarative approach to building reliable data pipelines. This approach makes it easy to turn simple SQL queries into production-ready ETL pipelines.

#Live Tables
CREATE OR REFRESH LIVE TABLE report
AS SELECT sum(profit)
FROM prod.sales

The above code describes the syntax of creating a Live Table.

2. Live data dependencies: Delta Live Table understands live data dependencies and automates away virtually all of the inherent operational complexity of building pipelines. This makes it easier for data analysts and data engineers to focus on getting value from data.

CREATE LIVE TABLE events
AS SELECT * FROM prod.raw_data

CREATE LIVE TABLE report
AS SELECT * FROM LIVE.events

Using the LIVE schema, the dependency between tables is established within the same pipeline. DLT detects LIVE dependencies and executes all operations in the correct order

3. Quality constraints: Delta Live Table allows users to define quality constraints to ensure that the data is of the required quality. Users can define as many constraints as they want to fail the pipeline completely, drop records, or just flag the issue and move on.

CREATE STREAMING LIVE TABLE report
(CONSTRAINT valid_timestamp EXPECT (timestamp >= '2012-01-01') 
ON VIOLATION DROP ROW)
SELECT * FROM prod.raw_data

The above query is a syntax for creating Live Table with constraints. The query drops rows whose timestamp value is less than 1st January 2012.

4. Streaming support: Delta Live Table supports streaming data and makes it easy to process new data incrementally as soon as it arrives. This makes it possible to build near real-time data pipelines that can react instantly to changes in the data.

#Streaming Live Table
CREATE OR REFRESH STREAMING LIVE TABLE report
AS SELECT sum(profit)
FROM cloud_files(prod.sales)

The above code describes the syntax of creating a Streaming Live Table.

In conclusion, Delta Live Tables is a powerful framework that simplifies the process of building reliable data pipelines. By automating away almost all of the inherent operational complexity, it allows SQL-savvy data analysts and Python-centric data engineers to focus on getting value from data. It understands data dependencies live and helps to ensure data quality and governance while simplifying the ETL pipeline process. With its simple declarative approach, Delta Live Tables makes it easy to build powerful data pipelines for analytics.

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