How to Read Parquet File in Azure Databricks

BRIEF OVERVIEW

Azure Databricks is a fast, easy, and collaborative Apache Spark-based analytics platform. It provides a unified interface for data engineering, machine learning, and business analytics. Parquet is an efficient columnar storage file format that is widely used in big data processing frameworks like Apache Spark.

In Azure Databricks, you can easily read Parquet files using the built-in capabilities of Apache Spark. The following steps outline the process:

Create an instance of the SparkSession class.
Use the read method of the SparkSession object to load the Parquet file into a DataFrame.
Perform any necessary transformations or analysis on the DataFrame.
Show or save the results as required.

FAQs

Q: What are some advantages of using Parquet files?

A: Parquet files offer several benefits such as high compression ratios, efficient predicate pushdowns for filtering data during query execution, schema evolution support allowing addition/removal/modification of columns without rewriting entire datasets, and compatibility with various big data processing frameworks including Apache Spark.

Q: Can I read multiple Parquet files at once?

A: Yes. You can specify multiple paths while loading parquet files by providing either a comma-separated list or directory pattern matching all relevant files. For example:

spark.read.parquet("path/to/file1.parq,path/to/file2.parq")
spark.read.parquet("path/to/files/*.parq")

Q: How can I specify the schema while reading Parquet files?

A: By default, Spark infers the schema of the Parquet file during read operations. However, you can also provide an explicit schema using the `schema` option when loading a Parquet file. For example:

from pyspark.sql.types import StructType

custom_schema = StructType().add("column1", "string").add("column2", "integer")
df = spark.read.schema(custom_schema).parquet("path/to/file.parq")

BOTTOM LINE

Reading Parquet files in Azure Databricks is straightforward with Apache Spark’s built-in capabilities. By following a few simple steps, you can load and analyze your data efficiently using this efficient columnar storage format.

How To Read Parquet File In Azure Databricks