How DuckDB is ushering in the next frontier of embedded OLAP databases.
Data warehouses are now a default piece of the modern data stack. Teams of all sizes use a data warehouse to power analytics use cases. Snowflake's rapid rise has been the poster child of this trend.
However, the current warehouse paradigm is designed for a client-server use case and ignores a growing segment of users. The next frontier of data warehouses will close this gap by focusing on in-process analytics.
An emerging category of data warehouses sits at the intersection of analytical queries and embedded deployments. To illustrate why this is so compelling, it's helpful to categorize databases along two axes:
1. What types of workloads they are optimized for: analytical versus transactional
Analytical workloads — also called Online analytical processing (OLAP) — are complex queries on historical data. For example, you may want to analyze user signups broken down by demographics such as age and location. On the other hand, transactional workloads — also referred to as Online transactional processing — are optimized for quick real-time reads and writes. This is what your typical CRUD (create, retrieve, update, delete) app runs on.
2. How they are deployed: stand-alone versus embedded
Stand-alone databases are typically deployed in a client-server paradigm. The database sits on a centralized server and is queried by a client application. Embedded databases run within the host process of whatever application is accessing the database.
Putting these two axes together reveals the gap. Innovation in OLAP databases has focused on stand-alone OLAP databases such as Snowflake, ClickHouse, and Redshift. As a result, embedded analytics use cases have been underserved. DuckDB is changing this.
DuckDB's core innovation is that it is an in-process OLAP database. This means it is tightly integrated with your application and runs on the same host compute workload. By design, using DuckDB is as easy as installing a package and importing a module with no external dependencies.
The elegance of DuckDB’s approach can't be understated. It contrasts with that of data warehouses like Snowflake, which decouples compute from storage in a client-server model.
Merging compute and storage unlocks a handful of use cases I am excited about:
The idea that an in-process OLAP database needs to exist seems obvious in hindsight. However, several technical innovations and demand drivers converged to drive DuckDB's growing popularity. First, data science and analytics is now a core business process, not just a research function. Additionally, DuckDB's implementation draws upon decades of research on topics such as execution engines, concurrency control, and hardware resilience.
Though it is still early, the DuckDB ecosystem is showing promise, with several companies like Hex, Mode, and Voltron Data supporting the project. I would love to hear from you if you are building in the DuckDB ecosystem.