Steps on Handling JSON, CSV, and Parquet in Spark Tutorial

Apache Spark is one of the most powerful frameworks for big data processing and analytics. One of the most common tasks in Spark is reading and writing data in different formats such as JSON, CSV, and Parquet. These formats are widely used in the real world for data storage, exchange, and analysis.

In this tutorial, we’ll walk through the step-by-step process of handling JSON, CSV, and Parquet files in Spark using PySpark (Python API for Spark).

Why Use Spark for JSON, CSV, and Parquet?

• Scalability – Handles massive datasets efficiently.

• Flexibility – Supports multiple file formats out of the box.

• Compatibility – Integrates with Hadoop, Hive, and modern data lakes.

• Performance – Optimized execution with Spark SQL and DataFrame API.

Prerequisites

Before we begin, ensure you have the following:

1. Installed Apache Spark and PySpark

   pip install pyspark
   

2. A working Python environment (Python 3.8+ recommended).

3. Sample data files in JSON, CSV, or Parquet formats.

Step 1: Initialize SparkSession

In PySpark, everything starts with creating a SparkSession.

from pyspark.sql import SparkSession

# Create SparkSession
spark = SparkSession.builder \
    .appName("File Handling Tutorial") \
    .getOrCreate()

Step 2: Handling JSON Files in Spark

Reading JSON

# Load JSON file
json_df = spark.read.json("data/sample.json")

# Show data
json_df.show()

Writing JSON

# Write DataFrame to JSON
json_df.write.mode("overwrite").json("output/json_data")

✅ Use case: JSON is widely used in APIs, logs, and web applications.

Step 3: Handling CSV Files in Spark

Reading CSV

# Load CSV with header
csv_df = spark.read.csv("data/sample.csv", header=True, inferSchema=True)

# Display data
csv_df.show()

Writing CSV

# Save DataFrame as CSV
csv_df.write.mode("overwrite").csv("output/csv_data", header=True)

✅ Use case: CSV is one of the most common formats for raw datasets, reports, and spreadsheets.

Step 4: Handling Parquet Files in Spark

Reading Parquet

# Load Parquet file
parquet_df = spark.read.parquet("data/sample.parquet")

# Display data
parquet_df.show()

Writing Parquet

# Save DataFrame as Parquet
parquet_df.write.mode("overwrite").parquet("output/parquet_data")

✅ Use case: Parquet is a columnar storage format optimized for performance and widely used in big data systems.

Step 5: Converting Between Formats

Spark makes it easy to convert data between JSON, CSV, and Parquet.

# Convert CSV to Parquet
csv_df.write.parquet("output/csv_to_parquet")

# Convert JSON to CSV
json_df.write.csv("output/json_to_csv", header=True)

# Convert Parquet to JSON
parquet_df.write.json("output/parquet_to_json")

Best Practices for File Handling in Spark

1. Use inferSchema=True cautiously – Explicitly define schemas for large datasets.

2. Partition data for faster queries using .repartition() or .partitionBy().

3. Compress outputs (e.g., Snappy, Gzip) for storage efficiency.

4. Use Parquet for analytical queries due to its performance benefits.

Conclusion

In this tutorial, we learned how to read, write, and convert JSON, CSV, and Parquet files in Spark using PySpark. Mastering these formats is essential for data engineers and analysts working with big data.

By following these steps, you’ll be able to handle diverse datasets effectively and optimize your data pipelines in Spark.

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