Watch our open demo of the Universal Data Lakehouse!
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.
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.
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.
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.
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.
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.
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.
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.
The universal data lakehouse connects to all popular BI and reporting engines such as Snowflake.
It also serves data to popular machine learning and data science engines such as Databricks.
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.
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