We look at alternatives to relational databases that have emerged to help bring some structure to unstructured data and gain valuable insight by making it semi-structured.

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.

Harder to Query: Unlike structured data, which can be easily queried in databases, unstructured data requires specialized query systems or AI models to retrieve specific information.

In the dynamic world of data science, the conversion of unstructured data into structured data is a key process. This transformation is crucial for enabling more efficient data analysis and ...

Data Loss During Conversion: When converting unstructured or semi-structured data into structured formats, valuable context or details may be lost.

Allowing quality data in can lead to a better understanding of an organization. Read more to improve your data quality for unstructured data.

Let's start by examining those two broad-brush data categories: structured and unstructured. Structured data refers to the data resident within relational databases, often presented via customer ...

Discover best practices that allow data pipelines to scale and support both structured and unstructured data.

Anomalo’s Unstructured Data Monitoring with Workflows furthers the company’s mission to provide data trust across all data types and all data use cases.

Unstructured data — information that doesn’t follow conventional models or fit into structured database formats — represents more than 80% of all new enterprise data.