Videos Archive - PrestoDB

Building Modern Data Lakes for Analytics Using Object Storage – Satish Ramakrishnan, MinIO

The modern data lake is distributed, unstructured and demands performance and scale – or better stated, performance at scale. Modern object stores are the ideal platform to pair with MPP query engines like Presto – particularly as the scale reaches tens or hundreds of petabytes with tens to hundreds of concurrent queries. In this talk, Satish Ramakrishnan will outline the better together attributes of the two technologies with a focus on the most sophisticated modern object storage features – from throughput optimizations, multi-cloud capabilities, cross-cloud active active replication and lifecycle management. Participants will come away with a reference architecture suited to query processing at object scale.

A Git-like Repository for your Data Lake – Vinodhini Sivakami Duraisamy, Treeverse

A Git-like Repository for your Data Lake – Vinodhini Sivakami Duraisamy, Treeverse We tend to adopt practices that improve the flexibility of development and the velocity of code deployment, but how confident are we that the complex data system is safe once it arrives in production? We must be able to experiment in production and automate actions while minimizing customer pain and reducing damage to code and data. If your product’s value is derived from data in the shape of analytics or machine learning, losing it, or having corrupted data, can easily translate into pain. In this session, you will discover how chaos engineering principles apply to distributed data systems and the tools that enable us to make our data workloads more resilient.

Presto SQL Functions – Facebook

In this talk we will show how to use the recently introduced SQL function feature, how it works, and the ongoing work to support invoking arbitrary functions remotely with remote UDF server.

Dynamic UDF Framework and its Applications – Rongrong Zhong, Alluxio & Yanbing Zhang, Bytedance

Presto supports dynamically registered User Defined Functions (UDFs) since 2020. Over the years, we used this framework to add support for SQL UDFs and remote / external UDFs. One common community request in the UDF domain is to support Hive UDFs. Many companies have legacy Hive pipelines, and engineers who are familiar with HQL and Hive UDFs. With remote UDF, one can implement Hive UDF support as UDFs running on the remote cluster. But since HiveUDFs are written in Java, we can also run them inside the engine. We extended the dynamic UDF framework to support Java UDFs, and used this new extension to add HiveUDF support in Presto. With this feature, users can directly use their familiar HiveUDFs and UDAFs in their Presto query.

(Chinese) Presto at Bytedance – Hive UDF Wrapper for Presto

Presto has been widely used at Bytedance in several ways such as in the data warehouse, BI tools, ads etc. And, the Presto team at Bytedance has also delivered many key features and optimizations such as the Hive UDF wrapper, coordinator, runtime filter and so on which extend Presto usages and enhance Presto stabilities. Nowadays, most companies will use both Hive (or Spark) and Presto together. But Presto UDFs have very different syntax and internal mechanisms compared with Hive UDFs. This restricts Presto usage while users need to maintain 2 kinds of functions. In this talk, we will present a way to execute Hive UDF/UDAF inside Presto.

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