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Question 33
A data engineer has three notebooks in an ELT pipeline. The notebooks need to be executed in a specific order for the pipeline to complete successfully. The data engineer would like to use Delta Live Tables to manage this process.
Which of the following steps must the data engineer take as part of implementing this pipeline using Delta Live Tables?
A
They need to create a Delta Live Tables pipeline from the Data page.
B
They need to create a Delta Live Tables pipeline from the Jobs page.
C
They need to create a Delta Live tables pipeline from the Compute page.
D
They need to refactor their notebook to use Python and the dlt library.
E
They need to refactor their notebook to use SQL and CREATE LIVE TABLE keyword.
Explanation:
Delta Live Tables (DLT) pipelines are created and managed from the Jobs page in Databricks, not from the Data or Compute pages. Here's why:
Option B is correct: The Jobs page is where you create and manage all types of jobs in Databricks, including Delta Live Tables pipelines. This is the primary interface for scheduling and orchestrating data processing workflows.
Option A is incorrect: The Data page is primarily for exploring and managing datasets, tables, and databases, not for creating pipelines.
Option C is incorrect: The Compute page is for managing compute clusters and SQL warehouses, not for creating pipelines.
Option D and E are incorrect: While DLT supports both Python (using the dlt library) and SQL (using CREATE LIVE TABLE statements), refactoring notebooks is not mandatory for using DLT. You can reference existing notebooks in a DLT pipeline without refactoring them, as long as they contain valid DLT code. The key requirement is creating the pipeline from the Jobs page.
Key Point: The essential step for implementing a DLT pipeline is creating it from the Jobs page, where you can specify the notebooks to run, their execution order, and other pipeline configurations.