Data Lakehouse Resource Center

Cookie Notice

This site uses cookies for performance, analytics, personalization and advertising purposes.

For more information about how we use cookies please see our Cookie Policy.

Manage Consent Preferences

Essential/Strictly Necessary Cookies

Required

These cookies are essential in order to enable you to move around the website and use its features, such as accessing secure areas of the website.

Analytical/ Performance Cookies

These are analytics cookies that allow us to collect information about how visitors use a website, for instance which pages visitors go to most often, and if they get error messages from web pages. This helps us to improve the way the website works and allows us to test different ideas on the site.

Functional/ Preference Cookies

These cookies allow our website to properly function and in particular will allow you to use its more personal features.

Targeting/ Advertising Cookies

These cookies are used by third parties to build a profile of your interests and show you relevant adverts on other sites. You should check the relevant third party website for more information and how to opt out, as described below.

“It was not that hard at all, and what resulted was real-time analysis powered by Starburst.”

— Mitchell Posluns, Senior Data Scientist, Assurance

“The ability to access the data in Google Cloud Storage is where Starburst really stands out and stands apart.”

— Sachin Gopalakrishna Menon, Senior Director of Data, Priceline

“We can make decisions faster based on the analytics. Instead of waiting for days, this happens in near real-time.”

— Sachin Gopalakrishna Menon, Senior Director of Data, Priceline

Unlock the full potential of your lakehouse was Apache Iceberg

Introduction to Apache Iceberg In Trino

Apache Iceberg is an open source table format that brings database functionality to object storage such as S3, Azure’s ADLS, Google Cloud Storage and MinIO. This allows an organization to take advantage of low-cost, high performing cloud storage while providing data warehouse features and experience to their end users without being locked into a single vendor.

Iceberg Partitioning and Performance Optimizations in Trino

Partitioning is used to narrow down the scope of the data that needs to be read for a query. When dealing with big data, this can be crucial for performance and can be the difference between getting a query that takes minutes or even hours down to seconds!

Apache Iceberg DML (update/delete/merge) & Maintenance in Trino

One key feature of the Apache Iceberg connector is Trino’s ability to modify data that resides on object storage. As we all know, storage like AWS S3 is immutable which means they cannot be modified. This was a challenge in the Hadoop era where data needed to be modified or removed at the individual row level. Trino allows full DML(data manipulation language) using the Iceberg connector which means full support for update, delete and merge.

Apache Iceberg Schema Evolution in Trino

Schema evolution simply means the modification of tables as business rules and source systems are modified over time. Trino’s Iceberg connector supports different modifications to tables including the table name itself, column and partition changes.

Apache Iceberg Time Travel & Rollbacks in Trino

Time travel in Trino using Iceberg is a handy feature to “look back in time” at a table’s history. As we covered in this blog, each change to an Iceberg table creates a new “snapshot” which can be referred to by using standard sql.

Building Reporting Structures on S3 using Starburst Galaxy and Apache Iceberg

AWS S3 has become one of the most widely used storage platforms in the world. Companies store a variety of data on S3 from application data to event based and IoT data. Oftentimes, this data is used for analytics in the form of regular BI reporting in addition to ad hoc reporting.

Trino On Ice I: A Gentle Introduction To Iceberg

Back in the Gentle introduction to the Hive connector blog, I discussed a commonly misunderstood architecture and uses of our Hive connector. In short, while some may think the name indicates Trino makes a call to a running Hive instance, the Hive connector does not use the Hive runtime to answer queries.

Trino on Ice II: In-Place Table Evolution and Cloud Compatibility with Iceberg

Welcome back to this blog post series discussing the awesome features of Apache Iceberg. The first post covered how Iceberg is a table format and not a file format. It demonstrated the benefits of hidden partitioning in Iceberg in contrast to exposed partitioning in Hive. There really is no such thing as “exposed partitioning.”

Trino on Ice III: Iceberg Concurrency Model, Snapshots, and the Iceberg Spec

Welcome back to this blog series discussing the amazing features of Apache Iceberg. In the last two blog posts, we’ve covered a lot of cool feature improvements of Iceberg over the Hive model. I recommend you take a look at those if you haven’t yet.

Trino on Ice IV: Deep Dive Into Iceberg Internals

Welcome back to the Trino on Ice blog series that has so far covered some very interesting high level concepts of the Iceberg model, and how you can take advantage of them using the Trino query engine. This blog post dives into some of the implementation details of Iceberg by dissecting some of the files that result from various operations carried out using Trino.

Starburst Galaxy and the Data Lakehouse with Dain Sundstrom, Trino Co-Creator & Starburst CTO

This webinar, hosted by Dain Sundstrom, co-founder of Trino and Chief Technology Officer at Starburst, Tom Nats, Director of Customer Solutions at Starburst, and Mike Marolda, Manager of Product Marketing at Starburst, dives into Starburst Galaxy and the Data Lakehouse.

Building a Data Lake Strategy with Starburst Galaxy

Join us as we provide an introduction to Starburst Galaxy on the data lake. In this session, we will provide you with an introduction to Starburst Galaxy, using the platform for data lake analytics and more.

Starburst Smart Indexing: The New Standard of Data Lake Analytics

Proprietary and patented indexing technology sets a new benchmark in data lake analytics, empowering organizations to more quickly and efficiently derive greater insights from their data.

Starburst Solutions: Data Lakehouse

Learn more about the Starburst solution, a Data Lakehouse.

Analytics Anywhere – An Introduction to Starburst Enterprise

Learn the value of decentralized data access, and how to use the Starburst Enterprise as a lakehouse engine and a single point of secure access to data in any location and regions.

Starburst Lakehouse Solution Brief

This solution brief discusses how the Starburst Data Lakehouse may be right for you.

Starburst data products

Learn how to create and share high quality data products vital for the lakehouse

Future proof your analytics with Starburst Galaxy

Spend less time managing your data and more time analyzing it. Starburst Galaxy is a fully-managed platform providing you with the easiest way to access the power of Starburst’s best-in-class data lake MPP SQL engine

Starburst performance whitepaper

Learn how Starburst and Trino achieve lightning fast performance and data transformations on the data lake

Starburst Enterprise security guide

From native fine-grained access control, to robust integrations, learn how Starburst Enterprise can easily secure your data lake

Starburst Delta Lake connector

Leverage the power of Trino and Delta Lake, deliver fast performance, and advanced data transformations to operationalize your lakehouse

Building a federated lakehouse with Starburst Galaxy

Your lakehouse doesn’t need data in one place. Learn how Starburst Galaxy can federate to multiple sources and different locations

Starburst and Trino really enabled us to accelerate time to insight improve our conversion rates and enable robust modeling.

— Mitchell Posluns, Senior Data Scientist

We can make decisions faster based on the analytics. Instead of waiting for days, this happens in near real-time.

— Sachin Gopalakrishna Menon, Senior Director of Data

Starburst was very data-lake friendly. It was as if it was built for that model. That was a key differentiator for us. We were very invested in the data lake.

— Ivan Black, Director

Our data lake backbone was on a traditional Hadoop infrastructure. While that approach had its day, it’s not flexible. We needed to scale out and separate our compute from our storage without moving the data.

— Mike Prior, Principal IO Engineer

The data warehouse and data lake space is constantly evolving, and our enterprise focus means we have to support customer requirements across different platforms. Starburst gives us the ability to move quickly to support ever-changing use cases within complex enterprise environments.

— David Schulman, Head of Partner Marketing

Starburst powers the serving layer of Zalando’s data lake on S3. We have more than a thousand internal users and 100s of applications using Starburst daily, running 30k+ queries that are processing half a petabyte per day.

— Onur Y., Engineering Lead

Essential/Strictly Necessary Cookies

Analytical/ Performance Cookies

Functional/ Preference Cookies

Targeting/ Advertising Cookies

By Use Cases

By Industry

Starburst Galaxy

Starburst Enterprise

By Use Cases

By Industry

Filter:

Blog

Resources

Pages

Documentation

.no-wrap-header { white-space: nowrap; } @media screen and (max-width: 1250px) { .no-wrap-header { white-space: normal; } }

Data Lakehouse Resource Center

Data Lakehouse combines the benefits of two tested data storage systems improving scalability and performance: a data warehouse and a data lake

What is Data Lakehouse?

Data Lakehouse has four key features:

Low cost, highly scalable storage

Modern open table formats

Distributed MPP query engine

Fine-grained access control

“It was not that hard at all, and what resulted was real-time analysis powered by Starburst.”

“The ability to access the data in Google Cloud Storage is where Starburst really stands out and stands apart.”

“We can make decisions faster based on the analytics. Instead of waiting for days, this happens in near real-time.”

Data Lakehouse 101

Unlock the full potential of your lakehouse was Apache Iceberg.slider-header {margin-bottom: 0; }

Introduction to Apache Iceberg In Trino

Iceberg Partitioning and Performance Optimizations in Trino

Apache Iceberg DML (update/delete/merge) & Maintenance in Trino

Apache Iceberg Schema Evolution in Trino

Apache Iceberg Time Travel & Rollbacks in Trino

Building Reporting Structures on S3 using Starburst Galaxy and Apache Iceberg

Trino On Ice I: A Gentle Introduction To Iceberg

Trino on Ice II: In-Place Table Evolution and Cloud Compatibility with Iceberg

Trino on Ice III: Iceberg Concurrency Model, Snapshots, and the Iceberg Spec

Trino on Ice IV: Deep Dive Into Iceberg Internals

More Resources

What is Data Lakehouse?

Data Lakehouse Use Cases

Get in touch

A single point of access to all your data

Stay in the know - Sign up for our newsletter!

Resources

Quick Links

Get In Touch

Start Free withStarburst Galaxy

For more deployment options:

Unlock the full potential of your lakehouse was Apache Iceberg

Start Free with
Starburst Galaxy