# Cinco de Trino recap: Learn how to build an efficient data lake

DevFeed: [Cinco de Trino recap: Learn how to build an efficient data lake](<https://devfeed.tech/articles/cinco-de-trino-recap-learn-how-to-build-an-efficient-data-lake-8674.md>)

Original publisher: [Read original article](<https://trino.io/blog/2022/05/17/cinco-de-trino-recap.html>)

Author: Brian Olsen, Brian Zhan

Published: 2022-05-17T00:00:00Z

Content type: article

Language: en

Sources: [Trino Blog](<https://devfeed.tech/sources/trino-blog.md>)

Topics: [data](<https://devfeed.tech/topics/data.md>), [data-engineering](<https://devfeed.tech/topics/data-engineering.md>), [big-data](<https://devfeed.tech/topics/big-data.md>), [Query (disambiguation)](<https://devfeed.tech/topics/query.md>), [datasets](<https://devfeed.tech/topics/datasets.md>), [Apache Iceberg](<https://devfeed.tech/topics/apache-iceberg.md>), [Stack Overflow](<https://devfeed.tech/topics/stackoverflow.md>), [X (Twitter)](<https://devfeed.tech/topics/twitter.md>)

Tags: [analytics](<https://devfeed.tech/tags/analytics.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [big-data](<https://devfeed.tech/tags/big-data.md>), [build](<https://devfeed.tech/tags/build.md>), [clusters](<https://devfeed.tech/tags/clusters.md>), [community](<https://devfeed.tech/tags/community.md>), [conference](<https://devfeed.tech/tags/conference.md>), [cost-savings](<https://devfeed.tech/tags/cost-savings.md>), [data](<https://devfeed.tech/tags/data.md>), [data-engineering](<https://devfeed.tech/tags/data-engineering.md>), [data-lake](<https://devfeed.tech/tags/data-lake.md>), [etl](<https://devfeed.tech/tags/etl.md>), [learn](<https://devfeed.tech/tags/learn.md>), [pipelines](<https://devfeed.tech/tags/pipelines.md>), [recap](<https://devfeed.tech/tags/recap.md>), [recovery](<https://devfeed.tech/tags/recovery.md>), [scale](<https://devfeed.tech/tags/scale.md>), [tools](<https://devfeed.tech/tags/tools.md>)

## AI overview

A recap of the Cinco de Trino conference covering Trino's role in data lakehouse architectures, interactive and federated querying, adaptive query planning, and fault-tolerant execution. It highlights Project Tardigrade for autoscaling, spot-instance use, cost savings, and failure recovery, plus a Starburst Galaxy lab for ingesting, cleaning, and analyzing Twitter and Stack Overflow data.

## Source excerpt

When Trino (formerly PrestoSQL) arrived on the scene almost 10 years ago, it immediately became known as the much faster alternative to the data warehouse of big data, Apache Hive. The use cases that you, as the community, have built had far exceeded anything we had imagined in complexity. Together we've made Trino not only the fastest way to interactively query large data sets, but also a convenient way to run federated queries across data sources to make moving all the data optional. At Cinco de Trino, we came full circle back to the next iteration of analytics architecture with the data lake. This conference offers advice from industry thought leaders about how to use best lakehouse tools with Trino to manage that data complexity. Hear from industry thought leaders like Martin Traverso (Trino), Dain Sundstrom (Trino), James Campbell (Great Expectations), Jeremy Cohen (DBT Labs), Ryan Blue (Iceberg), Denny Lee (Delta Lake), Vinoth Chandar (Hudi). You can watch the talks on-demand on the Cinco de Trino playlist. In this post, I'd like to cover the key items from each talk you won't want to miss.