  Recent advances with in-memory columnar database techniques have increased
the performance of analytical queries on very large databases and data
warehouses. At the same time, advances in artificial intelligence (AI)
algorithms have increased the ability to analyze data. We use the term AI to
encompass both Deep Learning (DL or neural network) and Machine Learning (ML
aka Big Data analytics). Our exploration of the AI full stack has led us to a
cross-stack columnar database innovation that efficiently creates features for
AI analytics. The innovation is to create Augmented Dictionary Values (ADVs) to
add to existing columnar database dictionaries in order to increase the
efficiency of featurization by minimizing data movement and data duplication.
We show how various forms of featurization (feature selection, feature
extraction, and feature creation) can be efficiently calculated in a columnar
database. The full stack AI investigation has also led us to propose an
integrated columnar database and AI architecture. This architecture has
information flows and feedback loops to improve the whole analytics cycle
during multiple iterations of extracting data from the data sources,
featurization, and analysis.
