When a conversation turns to analytics or big data, the terms structured, semi-structured and unstructured might get ... using Structured Query Language (SQL)—a programming software language ...

Four key considerations to keep in mind when you need a database designed for analytical queries of vast quantities of time series data. SQL often ... semi-structured or unstructured data ...

Many companies are missing out on substantial financial opportunities because it’s enormously difficult to use their proprietary unstructured ... enterprise data. But what’s more, internal ...

In this article, we look at what’s particular to working with unstructured ... Those are more than likely SQL databases, configured with a table-based schema and data held in rows and columns ...

SQL databases ... a large volume of data, and you don’t want to lock yourself into a schema, as changing the schema later could be slow and painful. You’re taking in unstructured data from ...

Compounding the problem, most of that growth will be of unstructured data, so-called because it doesn’t adhere ... which means “not only SQL.” That is, it sets up databases that are not based on the ...

Even after 50 years, Structured Query Language, or SQL ... data as graph structures using nodes (similar to a table) and edges (similar to a relationship), show complex relationships between vast ...

New capabilities bring observability to unstructured data—no SQL required Monte Carlo, the leading data + AI observability platform, today announced the launch of unstructured data monitoring ...

While some of the data is well-organized in structured databases like Oracle or SQL ... of unstructured data — and the troubling unknowns about the nature and risk associated with what's ...

Data scientists are analytical data experts who use data science to discover insights from massive amounts of structured and unstructured data to help shape or meet specific business needs and goals.