Databricks-Certified-Data-Engineer-Associate Exam Dumps : Databricks Certified Data Engineer Associate Exam

The array functions from Spark SQL are a subset of the collection functions that operate on array columns1. They provide an ability to work with complex, nested data ingested from JSON files or other sources2. For example, the explode function can be used to transform an array column into multiple rows, one for each element in the array3. The array_contains function can be used to check if a value is present in an array column4. The array_join function can be used to concatenate all elements of an array column with a delimiter. These functions can be useful for processing JSON data that may have nested arrays or objects. References: 1: Spark SQL, Built-in Functions - Apache Spark 2: Spark SQL Array Functions Complete List - Spark By Examples 3: Spark SQL Array Functions - Syntax and Examples - DWgeek.com 4: Spark SQL, Built-in Functions - Apache Spark : Spark SQL, Built-in Functions - Apache Spark : [Working with Nested Data Using Higher Order Functions in SQL on Databricks - The Databricks Blog]

According to the medallion lakehouse architecture, gold tables are the final layer of data that powers analytics, machine learning, and production applications. They are often highly refined and aggregated, containing data that has been transformed into knowledge, rather than just information. Silver tables, on the other hand, are the intermediate layer of data that represents a validated, enriched version of the raw data from the bronze layer. They provide an enterprise view of all its key business entities, concepts and transactions, but they may not have all the aggregations and calculations that are required for specific use cases. Therefore, gold tables are more likely to contain aggregations than silver tables. References:

What is the medallion lakehouse architecture?

What is a Medallion Architecture?