What is unstructured data?
In big data, unstructured data is information without any predefined data model. It doesn’t fit into the traditional data formats or databases. While unstructured data is usually text-heavy, it can also contain data such as numbers, facts and images.
Unstructured data is messy and complicated. Just ask any data analyst! It requires more significant storage options as compared to structured data.
That's because the size can vary anywhere from a few bytes (blog post comments) to a few terabytes (a full-length 8K resolution video). However, it’s worth the effort as it is richer and more insightful than structured data. (No pain, no gain, right? 💯)
Unlike structured data, which is generally created by systems, unstructured data is mostly generated by people.
Examples of unstructured data include data from books, emails, audio recordings, video content, photos, surveillance data, sensor data, satellite imagery and social media and blog comments.
According to IBM, 80% of the world's data is unstructured.
How can you store unstructured data?
Unstructured data cannot be stored in relational databases. It’s usually stored in non-relational databases (also sometimes referred to as NoSQL databases) such as MongoDB, Cassandra, HBase, Redis, BigTable and Oracle NoSQL Database.
How are non-relational databases different from relational databases?
Unlike relational databases, non-relational databases don’t use the tabular schema of rows and columns. They use a storage model that’s tailored to the requirements of the type of data being stored.
For example, if the data is an object (images, videos), then it's stored in an object database. Graphs are stored in graph databases, and so on. Other examples of non-relational databases include key-value pairs, documents (JSON, XML, plain text), columns and time series.
How can you manage unstructured data?
Unstructured data cannot be managed using SQL.
You use big data tools and technologies to manage unstructured data in cloud object storages (AWS, Microsoft Azure), data warehouses (Amazon RedShift, Google BigQuery) and data lakes (Amazon S3, Azure Blob Storage).
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