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Is Performing Big Data Processing through XML Conversions Possible?

XML conversions have emerged as a crucial way for processing data of any organization or business to extract meaningful insights, patterns, and trends. In the highly competitive world, it is challenging for any organization to maintain its top position in its industry. And that’s where big data processing takes centre stage. In simple words, big data processing is a multi-dimensional approach that involves collecting, storing, and analyzing massive datasets to gain valuable information. If you are already well aware of big data processing, you may know that XMLs are generally known to be less efficient for big data processing. But, with the debut of a highly advanced tool (which will be discussed later in the blog post), it seems like performing big data processing is possible with XML conversions. You might be wondering, even after being human-readable & machine-readable data interchange formats, why XML is generally regarded as a misfit for big data processing. & why converting XML is essential to extract data of specific enterprises. Well, the answer to these questions lies in this detailed post. 

XML: A 90s Data Exchange Format, Still Relevant in 2024

XML (eXtensible Markup Language) was released in 1998 by the WWC. Initially being marketed as the most advanced data exchange format, XML was a force to be reckoned with. However, with the rise of JSON and other formats, XML somehow lost its ‘Main Character’ status in the market. Still, the XML is one of the most popular data interchange formats. It is used for expressing RDF data, building ontologies, and exchanging data between two enterprises. That’s why XML is among the few data exchanges that fulfill industry standards. 

How XML Conversions Impact Data Processing? 

From the start, you might be wondering what XML conversions have to do with data processing. But the truth is that whole data processing relies on XML conversions. The primary use of XML is to provide a seamless data exchange between two enterprises. However, internally, every organization uses different tools for data processing and storage. Therefore, organizations have to convert their data into XML to boost the whole process. 

Steps of Data Processing

The data processing can be divided into two steps: 

  1. Data Exchange or Transactional Processing, where the sending party converts data to XML, e.g. based on HL7 data standard. 
  2. Data Analytics, where the receiving party converts the data into relational databases.

XML is an excellent choice for data exchange, but when it comes to data analytics, it is not a good fit. Instead, relational databases are the preferred choice for data storage. 

Why is XML Not a Good Fit for Data Analytics? 

There are many reasons why enterprises generally avoid using XML for data analytics, such as: 

  1. Difficulty in managing concurrent access to XML. 
  2. Inability to integrate with BI tools. 
  3. High Verbosity. 
  4. A myriad of complications due to XML’s hierarchical structures.
  5. An abundant of time is consumed to convert XML Docs during transactional processing. 

However, the most common reason is the inability of XML to perform big data processing. XML files over 1 GB are generally regarded as very large. Thus, data processing of these files through XML is challenging. 

Flexter: An Advanced Enterprise XML Conversion Tool, Performing Big Data Processing Efficiently

During big data processing, Document Object Parsers (DOM) load the entire XML Doc. into memory, resulting in the creation of a navigable tree structure. If the process is further conducted on large XML files, the system or application may crash due to excessive memory usage. That’s why many organizations that try to do big data processing through XML often fail. But not from now onward since Flexter has entered the game. 

What is Flexter? 

Flexter is an advanced enterprise-level XML conversion tool that automatically converts XML to any relational database or big data format such as ORC & Parquet. 

How to Perform Big Data Processing through Flexter? 

Follow these steps to do data processing of large formats through Flexter. 

Step #1: Streaming Approach to XML Processing 

As discussed before, loading an entire XML file into memory through traditional processing may lead to the crash of an application. So, avoid this process. Instead, consider a streaming approach to process the XML file sequentially, reading it line by line or byte by byte. Instead of loading the entire file into memory, it emits events like start tags, end tags, and text. 

Step #2: SAX Parser 

Use SAX parsers instead of using traditional parsers like DOM (Document Object Model). They are efficient and have a lower memory footprint, making them ideal for large XML files. 

Step #3: Parameter -byte-stream

Flexter provides a parameter called -byte-stream. Through this parameter, Flexter loads the XML file bye by byte to the SAX parser. By doing so, the threat of crashing the application is reduced. At last, the XML file is processed sequentially, enabling the efficient and low-memory processing of very large XML files. 

The Future of Efficient Data Processing of Large XML File: Flexter

Generally, XML conversions are considered not a good choice for data processing of very large files. But, with the debut of Flexter, this popular statement may have to undergo some significant edits. With Flexter, an enterprise can process big data within minutes without avoiding the numerous complications and guesswork. Moreover, no coding skills are necessary to perform data processing through Flexter. It can be said that the future of efficient data processing of large XML files lies in the hands of Flexter

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