This course aims to teach learners how to deliver low-latency and highly concurrent analytics over data lakes. The course covers the utilization of recent advancements in lake-scale optimized approaches to storage and indexing, enabling new levels of efficiency for analytics over large data volumes. The teaching method includes real-world practical examples and a live demo. This course is intended for individuals interested in optimizing analytics performance over data lakes.
Overview
Syllabus
Intro
Agenda
A new leap in the evolution of data warehouses
The Firebolt difference
Why low-latency analytics matters
Challenges with low-latency analytics in data lakes
Storage & compute optimized together
What are sparse indexes?
Adapting sparse indexes for the cloud
A new take on materialized views Aggregating Indexes
The DW as a data lake accelerator
Taught by
Databricks
