Videos Archive - PrestoDB

Implementing Lakehouse Architecture with Presto at Bolt – Kostiantyn Tsykulenko, Bolt.eu

Bolt.eu is the first European mobility super-app. We have over 100M users across Europe and Africa and have to deal with data at a large scale on a daily basis (over 100k queries daily). Previously we were using a traditional data warehouse solution based on Redshift but we’ve faced scalability issues that were hard to overcome and after doing our research we chose Presto as the solution. In just a single year we’ve managed to migrate to the Lakehouse architecture using AWS, Presto, Spark and Delta lake. We would like to talk about our journey, some of the challenges we’ve encountered and how we solved them.

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

Presto on AWS Journey at Twilio – Lesson Learned and Optimization – Aakash Pradeep & Badri Tripathy

Twilio as a leader in cloud communication platforms is very heavy on data and data-based decision making. Most data related use cases are currently powered by the Presto engine. Two years back we started the Journey with Presto in Twilio and today the system has scaled to a multi-PB data lakehouse and supports more than 75k queries per day. In this journey, we learned a lot about how to effectively operationalize Presto on AWS and some of the tricks to have better query reliability, query performance, guard-railing the clusters and save cost. With this talk, we want to share this experience with the community.

The Future of Presto’s Query Optimizer – Bill McKenna, Ahana

In this talk, you will hear from the query optimizer OG himself, Bill McKenna (Principal software engineer at Ahana, Architect for the query optimizer that became the code base of the Amazon Redshift query optimizer, and co-author of The Volcano Optimizer Generator: Extensibility and Efficient Search) go into detail about the state of modern query optimizers, and how Presto stacks up against them and where it will go in the near future. If database theory is your jam, you won’t want to miss this deeply technical presentation from one of the pioneers in the field.

Keynote: Data Lakehouse: Country Club or Community Center? – Steven Mih, Co-founder & CEO, Ahana

Over the last two decades, we’ve seen the birth and emergence of the data lake systems–from the internal walls of Google to modern Lakehouses at Meta/Facebook, which promise the best of both data lake and data warehouse worlds. Equally important is the role open source–and more broadly, openness–has played and will play in this journey. In this talk, Steven will draw his experience with open source distributed systems (Couchbase, Mesosphere, Alluxio, Linux Foundation Presto) to explore the significance of the “5 shades of openness” with respect to the composable open data lakehouse ecosystem.

Build & Query Secure S3 Data Lakes with Ahana Cloud and AWS Lake Formation

AWS Lake Formation is a service that allows data platform users to set up a secure data lake in days. Creating a data lake with Presto and AWS Lake Formation is as simple as defining data sources and what data access and security policies you want to apply. In this talk, Wen will walk through the recently announced AWS Lake Formation and Ahana integration.

Running PrestoDB on Kubernetes with Ahana Cloud and AWS EKS

PrestoDB is built to be cloud agnostic and container-friendly, but getting it to run on Kubernetes in the cloud can be challenging. In this talk, Gary Stafford (AWS) and Dipti Borkar (Ahana) will discuss: Why use the in-VPC deployment model with AWS and demo, etc – Deploying PrestoDB on AWS EKS using the Ahana Cloud managed service within the user’s AWS account.

Building the Presto Open Source Community – Ahana Round Table

In this round table moderated by Eric Kavanagh of The Bloor Group, panelists from Uber, Facebook, Ahana, and Alibaba will discuss all aspects of building a thriving open source community around PrestoDB including why Presto is so popular & the problems it solves, the open source model the foundation follows, why governance and transparency are so important to an open source community, and what the community looks for in open source projects.

Free-Forever Managed Service for Presto for your Cloud-Native Open SQL Lakehouse – Wen Phan, Ahana

Getting started with a do-it-yourself approach to standing up an open SQL Lakehouse can be challenging and cumbersome. Ahana Cloud Community Edition dramatically simplifies it and gives you the ability to learn and validate Presto for your open SQL Lakehouse—for free. In this session, we’ll show you how easy it is to register for, stand up, and use the Ahana Cloud Community Edition to query on top of your Lakehouse.

How Blinkit is Building an Open Data Lakehouse with Presto on AWS – Satyam Krishna & Akshay Agarwal

Blinkit, India’s leading instant delivery service, uses Presto on AWS to help them deliver on their promise of “everything delivered in 10 minutes”. In this session, Satyam and Akshay will discuss why they moved to Presto on S3 from their cloud data warehouse for more flexibility and better price performance. They’ll also share more on their open data lakehouse architecture which includes Presto as their SQL engine for ad hoc reporting, Ahana as SaaS for Presto, Apache Hudi and Iceberg to help manage transactions, and AWS S3 as their data lake.

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.

PrestoDB and Apache Hudi for the Lakehouse – Sagar Sumit & Bhavani Sudha Saktheeswaran

Apache Hudi is a rich platform to build self-managing, exabyte-scale data lakes, optimized for incremental as well as regular batch processing. Hudi tables can be seamlessly synced to Hive metastore, which unlocks the powerful capabilities of Presto engine via the Hive connector. Presto-Hudi integration is over five years old. What started as simply fetching splits using a custom input format for a Hudi Copy-On-Write table has evolved into snapshot querying of Merge-On-Read tables and using Hudi’s internal metadata table to boost query performance. In this session, we trace that journey and discuss in detail the recent developments that have made this integration stronger not only in terms of usability but also performance. We discuss the additional features that come with the brand new presto-hudi connector, such as multi-modal index and data skipping for better query performance.

Presto & the Foundations of Open Lake House: Trends & Opportunities – Biswapesh Chattopadhyay, Meta

Building open and shared foundational tech to build a lake house architecture can provide the best-of-breed user experience across the Analytics and ML domains and potentially beyond. In this talk, Biswa will share examples drawn from the evolution of the data stack at Meta over the last few years including efforts towards dialect unification (Sapphire aka Presto-on-Spark and Xstream-IE streaming engine efforts), eval unification (using Velox as the base), eliminating the need for data duplication for interactive analytics by building smart caching (RaptorX), building a best-of-breed file format that works across Analytics and ML (Alpha), and building an open source ML data pre-proc engine (TorchArrow) which shares the core dialect and eval components with Presto.

Panel Discussion: Presto for the Open Data Lakehouse

Today’s digital-native companies need a modern data infra that can handle data wrangling and data-driven analytics for the ever-increasing amount of data needed to drive business. Specifically, they need to address challenges like complexity, cost, and lock-in. An Open SQL Data Lakehouse approach enables flexibility and better cost performance by leveraging open technologies and formats. Join us for this panel where leading technologists from the Presto open source project will share their vision of the SQL Data Lakehouse and why Presto is a critical component.

Updates from the New PrestoDB C++ Execution Engine – Deepak Majeti, Ahana & Dave Cohen, Intel

This talk will provide an overview of an ongoing effort to enable the new PrestoDB C++ execution engine, code-named Prestissimo, and its use of the Velox library with the ability to run the TPC-H benchmark with good performance.

The Top 10 Reasons Ahana Makes Running Presto in the Cloud Easy – Dipti Borkar & Wen Phan, Ahana

Want to run Presto in the cloud? We’ll give you 10 reasons why it’s easy! Join us for this lightening session where we’ll cover the most cutting edge innovation happening with Presto in the cloud

Presto for Batch Processing in Data Lake – Santhosh Kumar Ravindran, Adroitts

We like to explain how we explored Presto via Ahana service in AWS for our client’s data processing in data lake.

Presto Authorization with Apache Ranger – Reetika Agrawal, Ahana & William Brooks, Privacera

Apache Ranger has been the user’s choice to support authorization in various data platforms from small-scale to enterprise-grade production environments. At Ahana, engineers are working on the Presto-Ranger integration, aiming to support global fine-grained data access control across all catalogs for Presto, while also providing auditing and monitoring of user access. We would like to collaborate with the Privacera and share our learnings, what we developed so far, and also hope to shed light on the future work of the Ranger Presto Plugin with Apache Ranger committer.