What are the advantages of using Delta Live tables (DLT) over Data Build Tool (dbt) in Databricks?

[Prachi_Sankhala]

Please explain with some use cases which show the difference between DLT and dbt.

[Priyag1]

Data build tool (dbt) is a transformation tool that aims to simplify the work of the analytic engineer in the data pipeline workflow. It specifically implements only the Transformation in the ETL process.

whereas Delta Live Tables (DLT) is a framework that makes it easier to design data pipelines and control the data quality. It covers the whole ETL process and is integrated in Databricks

[Priyag1]

Use Cases :

Data lineage graph

To make data teams more efficient, a data lineage graph can be used to find problems in the data easier and faster. It also makes it simpler for new members of the team, data analysts or other colleagues to understand the data pipeline.

• dbt

The data lineage graph includes the source table in the data warehouse, the tables after the different transformations and the dashboard in which the business value of the tables is displayed. It is however not possible to hover above or click on the tables and see more information.

• Delta Live Tables

The data lineage graph shows the tables that load data from the data lake and the different tables after transformation. More information about the different tables can be obtained by clicking on the tables.

• Comparison

Both show the tables of the sources and the transformations, but only dbt also shows what the end point of the data is, either if this is a dashboard, application or a data science pipeline.

[Priyag1]

Incremental tables

In big data, the amount of data is so vast that it is impossible to load all the data every time a few rows were added, the delays would be enormous. To solve this, incremental tables only loads those extra rows. The transformation that first took a few hours drops now down to a few seconds.

• dbt

An incremental model creates the whole table the first time it is run and then adapts the SQL code in an incremental run to incrementally transform the data

• Delta Live Tables

The incrementally transforming and loading of the data from a data lake to the data warehouse is both possible.

• Comparison

Both can incrementally transform data, but Delta Live Tables can also incrementally load data.

[Priyag1]

Detailed logging

For the engineers that maintain the different infrastructures and connections, logging is very important to pinpoint what the error was and more importantly, where a certain error took place. With logging, one can determine exactly when something went wrong very efficiently and action can immediately take place. This way a production line does not experience much or any downtime.

• dbt

The logs are generated from running a dbt command in its command line interface and are stored in a folder named logs in the project folder.

delta Live Tables

• The logs are directly integrated in the UI. More information about every event can be found when clicking on the particular event.
• There are real-time live updates when running on the cluster.
• The failing of data quality requirements are captured in the event log.
• Automatic monitoring, recovery and management.

Comparison

Both have an extensive amount of logs, but only Delta Live Tables has real-time updates and a visual interface to follow the process.