Traditionally, SQL has been limited to structured and semi-structured data, leaving analysts dependent on developers to access insights from unstructured sources.

Organizations have also turned to semi-structured formats to store data. Semi-structured data types include XML, HTML, CSV, and even email, and generally contain tags or other markers (rather than ...

2. Data is increasingly complex. It’s not just the amount of data that’s the issue—it’s the diversity of data. We’ve moved on from the kind of structured data stored in tables, columns ...

As an example, SQL was the first programming language to return multiple rows per single request. This makes it easier to get data on what is taking place within a set of data—and consequently ...

Organizations are collecting ever-increasing amounts of structured, unstructured, and semi-structured data in the modern data landscape. According to MIT, unstructured data represents 80–90% of ...

Google’s structured data documentation for vacation rentals was recently updated to require more specific data in a change that is more of a clarification than it is a change in requirements.

Analysis Style – With semi-structured data, you can use SQL or an unstructured language to interact with the data, depending on your final intent. Final Thoughts. Unstructured, ...

We look at how to gain structure from unstructured data, via AI/ML analytics to create new records, selecting object data via SQL and storing unstructured files in NoSQL formats.

An SQL database might hold name, date of birth, address, etc, but analysing unstructured data – via making it semi-structured – can get closer to what consumers think.