26 DLT aka Delta Live Tables | DLT Part 1 | Streaming Tables & Materialized Views in DLT pipeline

Summary

This video introduces Delta Life Tables (DLT), a declarative framework by Databricks for simplifying ETL pipelines. DLT allows developers to focus on writing transformations without managing background orchestration or error handling. The video covers setting up DLT, working with streaming tables, materialized views, and views, and demonstrates how to create a pipeline using Python. Key concepts such as incremental data processing, transformations, and debugging in development mode are discussed, with practical examples of building a pipeline to aggregate order data by market segment. The video concludes with running and debugging the pipeline successfully.

Takeaways

• 😀 Delta Life Tables (DLT) simplify ETL processes by allowing developers to focus only on writing transformations while DLT handles orchestration, cluster management, data quality, and error handling.
• 😀 DLT requires a Premium plan, and users on the standard plan need to upgrade to use it.
• 😀 DLT pipelines are built on Delta Lake, allowing users to leverage Delta Lake's features like ACID transactions, time travel, and schema evolution.
• 😀 Three types of datasets are used in DLT pipelines: streaming tables, materialized views, and views. Streaming tables support incremental data processing, materialized views handle transformations and aggregations, and views are used for temporary transformations.
• 😀 You can create DLT pipelines using Python or SQL, and special job compute is required to execute them.
• 😀 A Python decorator `@dlt.table` is used to define DLT tables, with optional table properties like quality (e.g., bronze) and comments.
• 😀 Streaming tables are created by reading data from a streaming source, while materialized views are based on batch data and stored at the target schema.
• 😀 Temporary views are used for intermediate transformations and do not get stored in the target schema. These views are essential for joining multiple datasets.
• 😀 Debugging in DLT pipelines is made easier with development mode, where the pipeline runs continuously and allows for troubleshooting without terminating the cluster.
• 😀 When running DLT pipelines, a series of steps occur, including initializing resources, setting up tables, and processing data, with visual feedback on the pipeline's progress.
• 😀 After successful execution, the DLT pipeline produces aggregated datasets, such as the 'orders aggregated gold' table, with operations like counting order keys and adding timestamps for tracking data load.

Q & A

What is Delta Live Tables (DLT) and how does it simplify ETL processing?

Delta Live Tables (DLT) is a declarative framework developed by Databricks to simplify ETL (Extract, Transform, Load) processing pipelines. It allows developers to focus on writing data transformations while the system handles orchestration, cluster management, data quality, and error handling automatically.

What types of datasets are used in Delta Live Tables pipelines?

DLT pipelines use three types of datasets: Streaming Tables, Materialized Views, and Views. Streaming Tables are used for processing real-time data, Materialized Views are typically used for transformations and aggregations, and Views are used for intermediate transformations.

What are the key differences between Streaming Tables and Materialized Views in Delta Live Tables?

The key difference is that Streaming Tables are used for processing real-time or incremental data, and they allow upserting data. Materialized Views, on the other hand, are typically used for batch processing and are often used for transformation or aggregation operations.

Can Delta Live Tables be used with any Databricks plan?

No, Delta Live Tables requires the Premium plan in Databricks. If you're using a Standard plan, you'll need to upgrade to the Premium plan to use DLT features.

What programming languages can be used to write code for Delta Live Tables pipelines?

You can write Delta Live Tables pipelines in two programming languages: Python and SQL. Both languages allow you to define transformations for streaming tables, materialized views, and views.

What is the role of the 'live' keyword when working with materialized views in Delta Live Tables?

The 'live' keyword is used when reading a materialized view that has been created within the same pipeline. It ensures that the system processes the data as a live data source rather than as a static dataset.

What are the benefits of using the development mode in a Delta Live Tables pipeline?

In development mode, the pipeline allows for debugging by keeping the cluster running even if the pipeline fails. This is useful for troubleshooting and making corrections without restarting the entire process.

What happens if a Delta Live Tables pipeline fails during execution?

If a DLT pipeline fails, Databricks provides detailed error logs that help in debugging. Errors like unresolved columns or missing functions can be quickly identified, and adjustments can be made. After correcting the issues, the pipeline can be restarted.

What is the significance of the 'triggered' and 'continuous' pipeline modes in Delta Live Tables?

'Triggered' mode allows the pipeline to run based on a schedule, while 'continuous' mode is used for real-time data processing, where the pipeline runs continuously to process streaming data without stopping.

What is the role of the Unity Catalog in Delta Live Tables pipelines?

Unity Catalog provides a centralized governance model that manages and organizes data across different workspaces. In Delta Live Tables, Unity Catalog is used to define and manage schemas where DLT tables and views are stored.