The rise of artificial intelligence(AI) is fundamentally changing the world of data analytics and data engineering.

AI projects stumble not because of flawed algorithms but because the underlying data pipelines are weak or chaotic.

In addition to building the semantic data layer, at Schema App, we’ve seen an increase in related queries after adding entity linking. For example, “near me” queries increased after we ...

Rich results generated from structured data are more visually appealing than standard search results. For example, a local bakery using schema markup might appear with review stars, a photo ...

Businesses are moving from proof of concept (POC) tests to more widespread adoption of AI this year, according to software ...

As digital transformation becomes essential, cloud-based data integration is revolutionizing how businesses manage complex ...

Data quality alone is no longer sufficient. Enter data observability, which is particularly suited to the shift to more ...

Shiva Kumar Ramavath is a Data Scientist and Machine Learning Engineer with a deep passion for artificial intelligence and ...

Any unit of data defined for processing is a data element; for example, ACCOUNT NUMBER, NAME, ADDRESS and CITY. A data element is defined by size (in characters) and type (alphanumeric ...

We might even need to design the questions we are going to ask in order to better help us collect the data we need to answer our questions. The trading cards are an example of a database.