What Is a Universal Data Lakehouse?

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Your Data Is Locked in Silos

How Did We Get Here?

Like many organizations, your journey probably began with running analytics directly on your operational database, before implementing a data warehouse or two. This journey may have entirely taken place in the cloud, or it may have even started out in a data center.

It’s Starting to Snow

At some point, you likely adopted Snowflake (or BigQuery or Redshift or another popular cloud data warehouse). These warehouses offered a fully-managed easy SQL experience for your data, and you were good to go. BI and reporting use cases practically ran themselves. Your analysts and their downstream data consumers never complained.

Hello, Streaming Data, Data Science, and Data Engineering

As use cases began to get more advanced, it was time to bring on data science and data engineering teams. Only, data scientists didn’t want to be confined by the rigidity of a data warehouse. They wanted to use frameworks such as Spark to explore data at scale. Data engineers wanted to integrate data into a data lake using Flink.

Data Silo Blues

Suddenly, you found yourself writing duplicate pipelines to Snowflake and Databricks. In fact, surveys show a roughly 45% and growing overlap in install base between the two platforms. Even worse, you were struggling to identify which data sets are actually the source of truth, managing copies of data passing between the pipelines, trying to keep up with the demands of GDPR and other regulations, all while managing multiple data silos.

As Your Data Grew, So Did The Complexity

Everything started out great, but as more users and use cases came up, your cloud costs shot up due to all the duplicate storage and redundant data processing. Without any clear source of truth for your data, data quality issues crept in and you needed a massive data platform team to keep up.

Enter the Universal Data Lakehouse: True Separation of Storage and Compute

Luckily, the most tech-forward companies out there have been building a solution for this all along - the universal data lakehouse architecture. Built on open data formats with universal data interoperability, it provides a proven model to deliver a true separation of storage and compute. While some data warehouses separate storage and compute, the distinction is technical - the capabilities are still joined at the hip at a product level, often tied to specific data formats, with extremely limited interoperability.

With the universal data lakehouse, you can ingest and transform data from any source, manage it centrally in a data lakehouse and query or access it with the engine of your choice. It’s the simplest, most cost-efficient, performant way to democratize data within your organization, while reducing costs and streamlining access.

A Closer Look at the Universal Data Lakehouse

Inefficiency breeds invention. For a decade, organizations have been asking data engineers to build platforms that ingest and store a single copy of source data in one place, with the opportunity to access that data from purpose-built query engines as they see fit. Industry giants such as Uber, LinkedIn, and others have achieved this by hiring the best data engineers.


The universal data lakehouse makes it simple to ingest data from streams, databases and cloud storage into a single platform - one time, at a fraction of the cost.

Manage centrally

With the universal data lakehouse, you no longer have to copy data between data warehouses and data lake silos.


Process data in-flight from bronze to silver tables.

Query with your warehouse

The universal data lakehouse connects to all popular BI and reporting engines such as Snowflake.

Add data science

It also serves data to popular machine learning and data science engines such as Databricks.

Future-proof your data

With the universal data lakehouse, you can always query your data with the right tool for the job - now, and in the AI future that is unfolding.

The universal data lakehouse architecture is a future-proof, open architecture that eliminates lock-in and frees your data for diverse data needs. It eliminates the constraints of traditional data platforms and is now available as a fully-managed cloud service with Onehouse.

What Users Achieve

Reduce costs by 50-80%

Cut data warehouse ETL/ingestion costs 50% or more compared to Fivetran + Snowflake.

Ingest with minute-level freshness

Go from hours to minutes to dramatically accelerate pipelines and reduce SLAs.

Scale effortlessly from gigabytes to terabytes per day

Ingest terabytes or even petabytes with ease.

Eliminate Lock-in: One Single Source of Truth to Support All Use Cases

BI & Reporting

Deliver BI and reporting on top of the same platforms you use today.

Real-time Analytics

Feed real-time analytics engines at a fraction of the cost.

Data Engineering

Build pipelines from any source to any query engine with data quality validation checks and observability in minutes.


Provide data science tools with up-to-the-minute data for the freshest machine learning models.

Generative AI

Efficiently store and process massive volumes of unstructured and structured data lightning fast to develop foundational models and large language models.

Stream Processing

Process event streams in near real-time so you always have the freshest insights about customers, operations, and more.

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