Data-at-rest: Data that is stored in a database, warehouse, file system, data lake, or other data store.

Data Confidence: Treating data quality with a full circle solution that not only offers to identify anomalies but also to take action against that anomalous data.

Data Drift: Changes in a data set’s properties or characteristics over time.

Data-in-flight: Data that is on the move, transporting from one location to another, such as through a message queue, API, or other pipeline

Data Lake: ​​A centralized repository that allows you to store all your structured and unstructured data at any scale. (**)

Data Quality: Ensuring data is free from errors, including duplicates, inaccuracies, inappropriate fields, irrelevant data, missing elements, non-conforming data, and poor data entry.

Data Stack: A combination of technologies that extracts, transforms, cleans, and loads data through a variety of steps until it is ready for production use.

Data Store: Where data is persisted in a database, file system, or other connected retrieval systems. (*)

Data Warehouse: A system that aggregates data from different sources into a single, central, consistent data store to support data analysis, data mining, artificial intelligence (AI), and machine learning. (*)