Databricks Certified Data Engineer Associate Study Guide

Chapter 4. Transforming Data with Apache Spark

The Databricks platform provides numerous transformative capabilities powered by Apache Spark. In this chapter, we will navigate through various data transformations tasks such as querying data files, writing to tables with various strategies, and performing advanced ETL operations. Moreover, we will discover the potential of higher-order functions and user-defined functions (UDFs) in Spark SQL.

Querying Data Files

Querying files in Databricks is a fundamental aspect of data exploration and analysis. In this section, we will explore the process of querying file content using SQL-like syntax. The primary mechanism for this is the SELECT statement, which allows us to query files directly to extract the file content.

To initiate a file query, we use the SELECT * FROM syntax, followed by the file format and the path to the file, as illustrated in Figure 4-1.  It’s important to note that the filepath is specified between backticks (`</path/>`), and not single quotes ('</path/>'). This distinction is essential to prevent potential syntax errors and ensure the correct interpretation of the path.

A filepath in this context can refer to a single file, or it can incorporate a wildcard character to simultaneously read multiple files. Alternatively, the path can point to an entire directory, assuming that all files within that directory adhere to the same format and schema. This flexibility is particularly advantageous when dealing with large ...