Videos Archive - PrestoDB

Fireside Chat: Journey to Innovation: Unleashing the Power of Open Source Through Open Governance

The Presto Foundation is the organization that oversees the development of the Presto open source project. Hosted at the Linux Foundation, the Presto Foundation operates under a community governance model with representation from all its members. In this fireside chat, we’ll hear more from Girish Baliga, Chair of the Presto Foundation, on what it actually means to be a Presto Foundation member and why this governance model is so important for open source projects. We’ll also talk with Vikram Murali of IBM, the newest member of the Presto Foundation. He’ll share more about IBM’s journey to Presto, how they’re using it in IBM’s new watsonx.data lakehouse, and why the Presto Foundation played an important role in IBM’s decision to choose Presto.

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

Query Execution Optimization for Broadcast Join using Replicated-Reads Strategy – George Wang, Ahana

Today presto supports broadcast join by having a worker to fetch data from a small data source to build a hash table and then sending the entire data over the network to all other workers for hash lookup probed by large data source. This can be optimized by a new query execution strategy as source data from small tables is pulled directly by all workers which is known as replicated reads from dimension tables. This feature comes with a nice caching property given that all worker nodes N are now participating in scanning the data from remote sources. The table scan operation for dimension tables is cacheable per all worker nodes. In addition, there will be better resource utilization because the presto scheduler can now reduce the number plan fragment to execute as the same workers run tasks in parallel within a single stage to reduce data shuffles.