Lesson - 6 - Databricks: Reading JSON/CSV files directly using SPARK SQL

Reading JSON/CSV files directly using Spark SQL refers to the process of loading JSON or CSV files into Spark DataFrames using Spark SQL APIs. Spark SQL provides a high-level interface that allows you to query and manipulate structured and semi-structured data using SQL-like syntax.

To read JSON or CSV files directly into Spark DataFrames using Spark SQL, you need to follow these steps:

Perform operations on the DataFrame: Once you have the DataFrame, you can apply various transformations and actions on it. Spark SQL provides a rich set of functions for filtering, aggregating, joining, sorting, and more. You can use SQL-like syntax or DataFrame APIs to perform these operations.

Here's an example that demonstrates reading JSON and CSV files using Spark SQL:

python
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# Import the necessary modules

# Create a SparkSession

# Read JSON files into a DataFrame

# Read CSV files into a DataFrame

# Perform operations on the DataFrames
In the above example, replace "path/to/json/files" and "path/to/csv/files" with the actual paths to your JSON and CSV files, respectively.

By using Spark SQL to read JSON/CSV files directly into Spark DataFrames, you can leverage the power of Spark's distributed computing capabilities and perform large-scale data processing and analysis efficiently.

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