Simplifying Data Management through Metadata Integrations and AI Infusion – Kevin Shen, IBM

Simplifying Data Management through Metadata Integrations and AI Infusion – Kevin Shen, IBM

In this demo we’ll go through two key pieces of watsonx.data, IBM’s new Data Lakehouse offering. Multiple analytics engines working on the same data: – Demo: Multiple engines working on the same data set so you can use the analytics tools you love without having to deal with the ugly plumbing Semantic Automation: Leverage AI to simplify data discovery and manipulation, allowing your data to work for you – Demo: Using a chat interface to find tables of relevance and how AI can enrich data sets with semantic information

Headless BI Architecture and Trade-offs – Pavel Tiunov, Cube Dev

Headless BI Architecture and Trade-offs – Pavel Tiunov, Cube Dev

There has been a proliferation of tools in different categories of the modern data stack. This talk will focus on the Headless BI category and Cube’s implementation of Headless BI. Headless BI injects a component between data warehouses and other data sources and tools on the other side of the stack (e.g. CDP, data exploration tools, custom data apps, etc.). This new component encapsulates several critical functions like data modeling, access control, and aggregate awareness while deliberately omitting others, like data visualization and presentation. We’ll explore: – Keeping data models separate from data sources and not substituting data modeling with mere data transformation. – Managing access control centrally, aggregate awareness, and caching in a separate layer upstack from data consumers. – Removing data presentation features and embracing data accessibility via a set of APIs.