Big data types: streaming data, unstructured data, large textual data:
Let's Dive into the Abyss of Big Data Types
Did you know that every minute, Facebook users share nearly 2.5 million pieces of content, and YouTube users upload 72 hours of new video content? This is a typical example of streaming data. 🌐💾
Streaming data refers to data that is continuously generated by thousands or even millions of data sources, which typically send data to the server simultaneously in small sizes (kilobytes). Social media feeds, website clicks, financial transactions, online activity logs, and sensor-enabled equipment in the internet of things — these all contribute to the deluge of streaming data.
However, processing this real-time data can be a challenge. For instance, streaming data requires sophisticated algorithms to process it in real-time, and it's also crucial to handle potential errors and anomalies that might occur in the data stream.
Now, let's talk about unstructured data. 📚🔍
Unstructured data refers to information that doesn't fit into conventional data models or databases. It includes text, images, audio and video files, social media posts, and more. With unstructured data making up approximately 80% of the world's data, it's an area that holds vast potential for businesses.
However, its complexity and variety pose a unique set of challenges. For instance, trying to analyze text data from social media can be complicated by slang, typos, and other inconsistencies. Even images and videos require specialized techniques to extract relevant information.
Lastly, we delve into large textual data. 📖🔬
Large textual data refers to substantial amounts of text data that are challenging to process and analyze because of their size. This could include books, research papers, legal documents, or any large collection of text files.
For instance, consider the task of analyzing all the books written in the last century. It would be a Herculean task, wouldn't it? But fear not. Techniques such as Natural Language Processing (NLP) and machine learning have made it possible to sift through large textual data and extract meaningful insights.
In the world of big data, these three types - streaming data, unstructured data, and large textual data - pose their unique challenges. Still, with advanced data analytics techniques, we can turn these challenges into opportunities.