Resources

Whether you've been using Hudi for years, or you’re new to Hudi’s robust capabilities, our early-release chapters of this O'Reilly Guide will help you build robust, open, and high-performing data lakehouses.

Apache Hudi™ helped power Uber to global leadership. Organizations large and small, from Amazon to Walmart, have joined in, helping to create one of the livelier and more effective open source projects. Learn about Hudi's storage format, versatile capabilities in flexible reads and writes, its robust table services, and Hudi Streamer.

NOW Insurance is a rapidly growing pioneer in the insurtech space. Read about why they chose the Universal Data Lakehouse architecture - and partnered with Onehouse to make it happen, fast.

Apna, Notion, Uber, Walmart, Zoom. What do these companies have in common? Aside from their businesses generating massive volumes of data - at high velocity - all of their teams have chosen the universal data lakehouse as a core component of their data stack and pipelines.

You shouldn’t have to move copies of data around, never knowing which is the real source of truth for different applications such as reporting, AI/ML, data science, and analytics. Learn how the universal data lakehouse architecture architecture is reshaping how businesses like Uber, Walmart, and TikTok handle vast and diverse data with unparalleled efficiency.

The data lakehouse is gaining strong interest from organizations looking to build a centralized data platform. Many are struggling to choose between the three popular lakehouse projects: Hudi™, the original data lakehouse developed at Uber; Iceberg, developed at Netflix, and; Delta Lake, an open source version of the Databricks lakehouse. Learn about the goals and differences of each project.

Learn how Onehouse delivers Apache Iceberg™ tables to Snowflake for a truly open data architecture at 60% lower cost for teams who wish to keep control of their data and avoid proprietary formats.

Onehouse Data Integration Guide -- Explore Onehouse’s comprehensive guide to discover a wealth of data integration and connector options tailored to your needs.

Combine the scalability and flexibility of data lakes with the stability and accessibility of data warehouses, and open it to your entire ecosystem.

Build realtime workloads in minutes to power use cases across your entire ecosystem, including change data capture, analytics, AI and ML, and more.

Onehouse builds on the data lakehouse architecture with a universal approach that makes data from all your favorite sources - streams, databases, and cloud storage, for example - available to all the common query engines, languages, and data lakehouse formats your data consumers use every day.

See how you can replicate Postgres tables into the lakehouse using Onehouse's new Confluent CDC source. This demo showcases fully automated integration with Onehouse and Confluent to provision and manages resources in Confluent to facilitate CDC data ingestion into the lakehouse.

This guide explains clustering strategies such as linear ordering, Z-Ordering, and Hilbert curves for Onehouse and Apache Hudi™. It covers optimal use cases for each clustering strategy, deployment options, and best practices.

This setup guide walks you through connecting Onehouse’s metadata catalog with Databricks Unity Catalog, ensuring a unified and secure data governance experience across AWS and Databricks. It covers everything from creating a Databricks workspace and Unity Catalog to configuring IAM roles, S3 buckets, and external locations for seamless data storage and syncing.

This guide describes how to implement fully-managed change data capture (CDC) from a SQL Server database to a data lakehouse, using Confluent Cloud’s managed Kafka Connect, Confluent Schema Registry, and Onehouse.

Read this guide to learn how to leverage integrated table services from Onehouse for optimizing read and write performance of Apache Hudi™ tables. Compaction, clustering, and cleaning are supported out-of-the-box features for Hudi tables optimized by Onehouse.

In this guide, we will show you how to set up the Change Data Capture (CDC) feature of Onehouse to enable continuous synchronization of data from an OLTP database into a data lakehouse. We will be using Amazon RDS PostgreSQL as our example source database.

This guide helps users integrate the Snowflake Data Cloud with a fully managed data lakehouse from Onehouse.

This guide shows how to seamlessly integrate Amazon Athena with your Onehouse Managed Lakehouse. This will allow you to power serverless analytics at scale on top of the data in your Lakehouse.

This guide will show how to ingest data from DynamoDB Tables into your Onehouse Managed Lakehouse using Onehouse's deep integration with Kafka.

This guide provides a pattern for creating a cross-region disaster recovery solution for Hudi™ using savepoints - enabling highly resilient lakehousees.

Gain maximum value from your data lakehouse while ensuring robust security and tailored access control.

Integrate the Onehouse Universal Data Lakehouse with Amazon Sagemaker to build machine learning models in near real-time.

Replicate your operational PostgreSQL database to the Onehouse Universal Data Lakehouse with up-to-the-minute data.

Integrate Confluent and Onehouse to seamlessly replicate operational databases in near real-time.

The workshop will leverage TPC-DS dataset in volume of 10 GB to demonstrate the various capabilities of read and write with Hudi™ and Presto. The dataset will be made available at a common S3 location accessible to workshop attendees. 

Learn how Conductor eliminated complex Apache Spark management, reduced query times by >75%, and freed their engineering team to focus on innovation by transforming their data infrastructure with Onehouse.

Olameter turned to Onehouse to bring years of historical XML data into a fully managed, open data lakehouse. They cut data ingestion and processing times by 10x, powering ML models and adding value to their data pipelines.

NOW Insurance is a rapidly growing pioneer in the insurtech space. Read about why they chose the Universal Data Lakehouse architecture - and partnered with Onehouse to make it happen, fast.

Apna is the largest and fastest-growing site for professional opportunities in India. Read on to learn how they rearchitected their data infrastructure to move from daily batch workloads to near real-time insights about their business while reducing costs.

Apna, Notion, Uber, Walmart, Zoom. What do these companies have in common? Aside from their businesses generating massive volumes of data - at high velocity - all of their teams have chosen the universal data lakehouse as a core component of their data stack and pipelines.

Today's data platforms too often start with an engine-first mindset, picking a compute engine and force-fitting data strategies around it. This approach seems like the right short-term decision, but given the gravity data possesses, it ends up locking organizations into rigid architectures, inflating costs, and ultimately slowing innovation.