Data Analytics & Avalanche

Affordability, ease-of-use and, of course, performance are critical decision-making factors when deciding on a Cloud Data Analytics platform to support day-to-day, real-world operational analytics. In this session, Adam Luciano looks under the hood at what are the key requirements including separation of compute and storage, query result caching and workload management.

Actian Avalanche has been redesigned from the ground-up to take advantage of Google Cloud infrastructure including the Google Kubernetes Engine, Google Cloud Storage and Google’s advanced networking. Explore how moving from a static Hadoop based architecture to a flexible contrainerized deployment dramatically reduced deployment time and simplified maintenance. Further, how moving to using Cloud native storage unlocked the full potential of Avalanche scalability and performance in GCP.

Join this panel for a discussion of typical concerns power users should have and how to bridge the gap between IT and the rest of us when it comes to moving data analytics to the Cloud

Join this session to see Jason provide a walk-through of how Actian Avalanche can be used to pull disparate desktop Excel spreadsheets, data from an Enterprise Data Warehouse and data from a Web Service API into a unified dataset that can be queried in Avalanche and then visualized through Looker (as an example) – all from a single console, without coding or specialized training.

User Defined Functions, or UDFs help extend built in functionality for SQL-based data analytics systems used by business analysts, engineers, and scientists. With Avalanche and Vector, these power users can further take advantage of UDF’s by deploying ML models into their database, enabling the models and associated data to be scored directly from within the database. Join this session to hear a discussion and simple examples of model management using an Iris Dataset and Logistic Regression.