Chunking and metadata are two terms that frequently come up when discussing SaaS, content on the Internet, and related topics.
So, what’s the difference between them?
That’s what we’ll be discussing in this blog post.
Overview:
- What is chunking?
- What are the different forms of chunking?
- What is metadata?
- What is the difference between chunking and metadata?
- Concluding thoughts.
What is chunking?
The term “chunking” comes from the psychology concept of breaking information down into manageable pieces, according to Couch Base, a business that helps companies run modern applications. Just like human beings need to study information in “chunks” to process it, computers need to receive data in chunks for their processing.
For artificial intelligence, cloud computing, and big data, chunking is essential for optimizing memory, processing speed, and scalability.
Data can be chunked in a variety of ways. For instance, text, numbers, binary data, images, videos, audio files, and network or streaming data can all be chopped into smaller subsets.
What are the different forms of chunking?
Many different kinds of chunking are utilized for various purposes, according to Couch Base.
Fixed-sized chunking is breaking up data into equal-sized parts. It’s commonly used for file storage systems, batching in machine learning, and streaming data processing.
Variable-sized chunking breaks up data into different-sized parts, usually for irregular patterns of data or deduplication in storage systems.
In content-based chunking, data is chunked according to different patterns in data rather than size. This chunking is typically used for backup and deduplication of systems with like content.
Logical chunking breaks data up into logical units, like text into sentences or paragraphs, data into time intervals, or database records into keys.
Dynamic chunking is used for streaming applications, adaptive systems, and real-time analytics and can divide data based on restraints like memory availability and workload distribution.
In file-based chunking, big files are split into smaller pieces for transfer, storage, and processing. This type of chunking can be used for cloud storage, file sharing systems, and video streaming.
Lastly, in task-based chunking, data is split into chunks for parallel processing tasks.
As you can see, chunking can be used for multiple purposes.
Why is chunking important?
Chunking is important because it helps both humans and AI/ML models process and comprehend information better. It boosts AI/ML performance in RAG and data processing, and it enhances application efficiency and scalability, according to Fresh Consulting.
What is metadata?
Metadata is “data about data,” according to IBM. In other words, it’s information that describes a piece of data.
Take, for example, an article. Some pieces of metadata in an article include the title, name of the author, and the date it was published.
IBM notes five key types of metadata:
Structural: Structural metadata is any data that identifies how data elements relate to each otehr, like how the home page links to other webpages and categorizes those pages into sections.
Descriptive: Descriptive metadata explains the content and provides contextual information about it, such as the title, author name, keywords, and summary.
Administrative: Administrative metadata involves data ownership and permissions, as well as data retention policies.
Technical: Technical metadata involves the document’s technical details, like the file type, encoding information, and storing location.
Preservation: Preservation metadata entails strategies for data’s long term use and reliability, such as transferring data from one system to another to protect and maintain it. For instance, a healthcare organization may use preservation metadata when it transfers data from legacy systems to a modern electronic health record (EHR) formats to preserve all patient records.
Metadata is crucial for data creation, data retrieval, and data archiving and preservation. It makes it easier to log, find, and fetch data when needed. Here are a few examples of metadata usage in the workplace, according to IBM:
Database management: A tool that can help users filter, tag, and sort data. This, in turn, can help them share data in between systems through a shared metadata system.
Data governance and compliance: Administrative data logs who can access, modify, or delete data, which helps organizations follow policies like the Health Insurance Portability and Accountability Act (HIPPA) and GDPR.
SEO: Title tags and meta descriptions embedded in the HTML codes in websites helps search engines receive the context they need to properly index and display content.
Cybersecurity: Investigators can use metadata to trace the origin, modification, and access of files in digital forensics and incident responses.
Social media: Metadata such as hashtags and geolocation help users find the information they want to see.
Consumer Insights: Retailers use metadata to track consumer interactions and generate targeted recommendations and ads.
Rights management: Administrative metadata includes data about who can access what in regards to copyright and intellectual property. For instance, a photo may include who can re-share the image, and under which guidelines they need to do so (such as by crediting the creator).
Why is metadata important?
Metadata serves several purposes. For data creation, metadata is used to capture when and where a photo was taken, for example, For data retrieval, metadata such as keywords, file descriptions, or creation dates make it easy for users to find the files they want. For data storage, metadata defines how relational databases are connected. Metadata can also add descriptions, labels, or tags to make it easier to find unstructured data. Lastly, metadata can make it easier to access and retrieve data through keywords, file names, or creation dates.
What’s the difference between chunking and metadata?
While chunking is used to break up datasets (which are often technical in nature), metadata is used to classify and organize information. Think of chunking like the divider, and metadata like the naming system or the file organizer on your desk.
Conclusion
Both chunking and metadata are key technological concepts to understand if you’d like to work with software, AI, IT, or web development.
By understanding these techniques and the uses for them, you’ll be well-equipped to help organizations better organize and classify data types.