What is XML (eXtensible Markup Language)?

XML (eXtensible Markup Language) is a highly rigid, heavily structured, text-based data format explicitly designed to store and transport massive, deeply hierarchical information in a format that is both human-readable and machine-readable. Invented in the late 1990s as a flexible evolution of HTML, XML became the absolute foundational backbone of the early enterprise internet, serving as the core syntax for massive corporate integration protocols (like SOAP) and the internal structural language for massive file formats (like Microsoft Office .docx files).

In modern data engineering, XML is universally recognized as a heavy, highly verbose, and extremely slow legacy format. It has been almost entirely eradicated from modern web applications and fast-moving APIs, ruthlessly overthrown by the vastly superior, lightweight JSON format. However, because multi-billion dollar legacy banking, healthcare, and government systems were architected in the 2000s, modern Data Lakehouses are still forced to ingest, parse, and dismantle massive volumes of XML data every single day.

The Architecture of Tags and Schemas

XML fundamentally operates using an aggressive, highly strict system of custom-defined hierarchical Tags (enclosed in angle brackets < >).

<Customer>
    <CustomerID>1045</CustomerID>
    <Name>John Doe</Name>
    <IsActive>true</IsActive>
    <ShippingAddresses>
        <Address>
            <City>New York</City>
            <Zip>10001</Zip>
        </Address>
    </ShippingAddresses>
</Customer>

The XML Schema Definition (XSD)

The single greatest architectural strength of XML—and the reason massive, hyper-regulated financial institutions loved it—is its capacity for absolute, cryptographic strictness via the XML Schema Definition (XSD). An XSD is a separate, highly complex file that explicitly dictates exactly what the XML is legally allowed to look like. It enforces that a <Customer> tag must contain exactly one <Name>, and that the <CustomerID> must absolutely be a 4-digit integer. If a massive banking mainframe receives an XML payload that violates the XSD rules even slightly, it violently rejects the payload before processing it, guaranteeing absolute data integrity.

The Downfall: Verbosity and Parsing Weight

The exact rigidity that made XML powerful caused its eventual destruction in the era of mobile computing and Big Data.

1. Catastrophic Data Bloat (Verbosity)

XML is catastrophically inefficient. Every single piece of data must be explicitly wrapped in both an opening tag <Name> and a closing tag </Name>. If a payload contains one million customer names, the exact text string Name is written to the hard drive and transmitted across the network two million times. This massive textual bloat completely destroys network bandwidth and massively inflates cloud storage costs. (JSON entirely eliminated the closing tags, cutting payload sizes in half).

2. The Parsing Penalty

Extracting data from a massive XML file requires computationally heavy algorithms (like DOM or SAX parsing). The CPU must build a massive, complex mathematical tree in active RAM just to understand the relationship between the tags. If a Data Engineer attempts to execute a basic SQL query directly against a 5-Gigabyte XML log file in a Data Lake, the query engine will frequently crash the server just trying to parse the nested tags.

Modern Ingestion Strategies

Modern Data Lakehouse query engines (like Apache Spark or Dremio) despise XML. When data engineers are forced to ingest XML from legacy mainframe FTP servers, they never leave the data in XML format. They utilize powerful ETL pipelines to instantly shred the XML, rip out the underlying data values, flatten the hierarchical structure, and permanently convert the data into the highly compressed, binary Apache Parquet format.

Summary of Technical Value

XML is a massive, highly rigid relic of early enterprise software architecture. While its strict, XSD-enforced structural validation made it the perfect protocol for early, hyper-secure financial integrations, its catastrophic textual bloat and massive CPU parsing penalties rendered it completely obsolete for modern, high-speed API data exchange and petabyte-scale Data Lakehouse analytics, relegating it primarily to legacy system integrations.

Learn More

To learn more about the Data Lakehouse, read the book “Lakehouse for Everyone” by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.