How to Convert XML to PARQUET?

Jaskirat Singh Anand — Tue, 06 Oct 2026 11:24:12 +0000

To Convert XML to PARQUET format can be achieved either through automated enterprise tools or customized ETL scripts:

Automated Enterprise Software: Use Professional XML Converter tool to automate the conversion of nested XML documents into Parquet files without any coding, schema loss or risk of Out-Of-Memory (OOM) errors.

Python Scripting Approach: Utilize Python's pandas and pyarrow modules to parse XML tags, de-nest the nested objects through pd.json_normalize() method and export to Parquet using df.to_parquet('output.parquet', engine='pyarrow').

Big Data Processing Engine: Use PySpark along with the com.databricks:spark-xml library to import XML tags using .option("rowTag", "root_element") and save the file using df.write.parquet("s3://bucket/parquet_data").

Primary Advantages: The transformation of XML hierarchical markup into columnar Apache Parquet data shrinks the size of file by up to 85%-90% (due to Snappy compression) and increases the speed of query processing in Snowflake, AWS Athena, and Databricks by 100 times.

*How to Convert XML to PARQUET Using Python and Pandas?
*
To Convert XML to PARQUET using Pandas and PyArrow offers a simple start. This approach works well for ad-hoc scripts handling single XML files under 500 MB.
Prerequisites
First, install the necessary Python Modules using pip:

pip install pandas pyarrow lxml
Python script Use:
import pandas as pd
import xml.etree.ElementTree as ET

def convert_xml_to_parquet_pandas(xml_file_path, parquet_output_path):
"""
Parses a simple XML document, converts it into a Pandas DataFrame,
and writes it to Apache Parquet columnar format using PyArrow.
"""
try:
# Step 1: Parse the XML file into an ElementTree
tree = ET.parse(xml_file_path)
root = tree.getroot()

    # Step 2: Extract record elements (Assumes repeating child elements)
    records = []
    for child in root:
        record = {}
        # Capture tag attributes
        for attr_name, attr_val in child.attrib.items():
            record[f"attr_{attr_name}"] = attr_val

        # Capture child elements
        for subelement in child:
            record[subelement.tag] = subelement.text

        records.append(record)

    # Step 3: Load into Pandas DataFrame
    df = pd.DataFrame(records)

    # Step 4: Write to Parquet with Snappy compression
    df.to_parquet(
        parquet_output_path, 
        engine='pyarrow', 
        compression='snappy', 
        index=False
    )
    print(f"Successfully converted {xml_file_path} to {parquet_output_path}")

except Exception as e:
    print(f"Error during XML to Parquet conversion: {str(e)}")

Execution example

if name == "main":
convert_xml_to_parquet_pandas("sales_data.xml", "sales_data.parquet")

*What are Different Limitations to Convert XML to PARQUET Using Pandas Approach?
*
Inflation of Memory Space: Pandas creates an in-memory Python dictionary of the complete XML DOM tree structure. This way, a 1GB XML file will take 6GB to 10GB RAM, resulting in a MemoryError.

Elimination of Nested Structures: Hierarchical structures consisting of deeply nested nodes along with array tags get lost unless you write recursive algorithms for normalization.

*How to Convert XML to PARQUET Using PySpark?
*
In order for enterprise-scale pipelines to learn How to Convert XML to PARQUET on distributed clusters like AWS EMR, Databricks, or Azure Synapse, the use of Apache Spark with the spark-xml library is needed.
PySpark Setup & Command
Run PySpark with the Databricks XML package dependency:
spark-submit --packages com.databricks:spark-xml_2.12:0.18.0 spark_etl_script.py
PySpark Script Implementation
from pyspark.sql import SparkSession
def convert_xml_to_parquet_pyspark():
# Initialize Spark Session with XML package capabilities
spark = SparkSession.builder \
.appName("Enterprise-XML-to-Parquet-Pipeline") \
.getOrCreate()

# Define the root row tag inside the XML document
row_tag_name = "Transaction"
xml_input_path = "s3a://data-lake-raw/xml-ingest/transactions.xml"
parquet_output_path = "s3a://data-lake-processed/parquet/transactions/"

# Step 1: Read XML into PySpark DataFrame with automatic schema inference
df = spark.read \
    .format("xml") \
    .option("rowTag", row_tag_name) \
    .option("attributePrefix", "attr_") \
    .option("valueTag", "element_value") \
    .load(xml_input_path)

# Step 2: Print Schema to inspect nested structures
print("Inferred XML Schema:")
df.printSchema()

# Step 3: Write DataFrame directly to Parquet columnar format
df.write \
    .mode("overwrite") \
    .option("compression", "snappy") \
    .parquet(parquet_output_path)

print("Distributed XML to Parquet transformation complete.")
spark.stop()

if name == "main":
convert_xml_to_parquet_pyspark()

What are the Different Limitations to Convert XML to PARQUET Using PySpark?
**
**Schema Type Inference Bottlenecks: A complete pass through the whole data set is necessary for PySpark to infer XML schema types, causing execution time to double for large XML data sets.
Driver Node Out-Of-Memory: When XML tags hold huge chunks of textual data or have many sub-elements, the Spark driver node gets java.lang.OutOfMemoryError: Java heap space.

**How to Convert XML to PARQUET Using a Professional Tool?
**In situations where code maintenance is an issue, the most reliable method to convert from XML to PARQUET using automation is by using enterprise desktop applications that are designed to transform data in batches.
XML Converter simplifies coding, memory issues, and manual schema mappings. It is especially developed for database administrators, data analysts, forensic analysts, and IT management who need reliable conversions without having to develop a fragile pipeline.

What are The Outperforming Customs Scripts of This Professional XML Converter?
Automatic Schema Discovery & Flattening: Automatically discovers and converts multi-leveled, nested XML nodes, relations between them, and their attributes directly to clean and well-structured Parquet files.

Bulk Operations Support: Imports unlimited amount of XML files and directories within one execution without encountering memory overload or heap limitations.

Retains Directory Structure & Hierarchy: Maintains your corporate directory hierarchy while performing bulk operations.

Multi-FORMAT Export Options: Besides Parquet format, allows users to export XML files in legacy and relational formats. You can find more information here - how to convert XML to SQLite or how to convert XML to PDF.

What are The Steps to Convert XML to PARQUET Using This Professional XML Converter?
Follow these simple steps to perform bulk conversion without writing code:
Download and Launch: Install and open the SysTools XML Converter software on your system.
Add XML Files or Folders: Click on Add File(s) or Add Folder to select your single or bulk XML documents.
Preview Data Structure: The software loads the files into an intuitive tree hierarchy, allowing you to preview XML elements and attributes before export.
Select Target Format: From the list of export choices, select Parquet as your output format.
Configure Export Settings: Choose whether to preserve folder hierarchies, set filter rules, and specify your destination path.
Execute Conversion: Click the Export button to start the conversion process. Upon completion, a summary report detailing total successfully converted records will be displayed.
Frequently Asked Questions (FAQs)
Q1: How do I convert XML to PARQUET without writing any custom code?
The best way to do this is by using XML Converter software. You can feed in the folder tree structure of your XML files, choose Parquet as your desired output format, and press Export.

Q2: How to convert XML to Parquet when the files are larger than available system memory (OOM)?
Standard parsers, such as DOM parser, will break down because of out-of-memory problems. The options here are to either use an iterative SAX/StAX parsing technique in your code, horizontally scale out to PySpark multi-node cluster or use XML Converter that uses disk-based streaming memory management for large batch jobs.

Q3: Why is Parquet faster than XML for SQL analytical queries?
This is because of the nature of the data format. With Parquet being column-oriented, reading from disk the SELECT total_amount FROM sales query will read only the bytes required for the total_amount column. While with an XML file, all tags and elements must be parsed throughout the whole document.

Q4: Does Parquet maintain the XML tag attributes?
Yes. While transforming, XML tag attributes (for example ) are transformed into separate table columns (for example item_id). This is done using both hand-written code libraries and automated software tools.
Conclusion
One of the most effective actions that an organization can take to improve cloud data analytics is to modernize its enterprise data architecture from legacy XML hierarchies to Apache Parquet. Through this transformation, it becomes possible for data engineers to save space, reduce costs associated with using cloud computing resources, and increase the speed of queries up to 100 times on platforms such as AWS Athena, Databricks, and Snowflake by changing row-based text markup into compact columnar data storage. While programming frameworks, including Python and PySpark, allow customizability and are useful when it comes to writing scripts and handling large cloud clusters, they add to complications. The zero code approach to transforming data architecture requires automated tools, such as the XML Converter, for optimal performance and scalability.

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How to Convert XML to PARQUET?

Execution example