Apache Druid® is an open-source distributed database designed for real-time analytics at scale. Since our founding ten years ago, Imply has been committed to growing the Apache Druid project by making it as easy as possible for people to use Druid and build awesome data applications on top of it.
In 2022, we launched Imply Polaris: a hassle-free, fully managed cloud environment that serves as the “easy button” for Apache Druid. This database-as-a-service brings you all the performance advantages of Druid, plus add-in auto-scaling capabilities for seamless data ingestion, and an easy interface to build analytics applications to visualize your data in real time. Within minutes, you can derive valuable insights from all your data without the complexities of procuring and managing infrastructure.
We’ve made a lot of progress over the past decade. As we reflect upon the past year, we’re proud to share a summary of the top 2024 product updates across both Druid and Imply.
2024 was a banner year, totaling over 1,348 commits by 106 contributors across Apache Druid 29, 30, and 31. On top of this, we released over 54 unique product updates, 28 other improvements, and 18 fixes to Imply Polaris.
Here’s a snapshot of 2024 growth across data usage and community activity overall:
While there are many ways to categorize our progress across features and functionalities, there were four main strategic pillars that guided our product innovation in 2024:
In our co-founder Eric Tschetter’s Druid Summit keynote, he introduced a new perspective on data solutions and infrastructure. The future lies in thinking about data in terms of entities (“things” like videos on Netflix or communities on Reddit) and events (“actions” like views or plays). Druid is focused on an events-first mindset. At its core, Druid provides a high-performance, ultra-low latency query engine that ingests events from streams and batch storage, transforms them by aggregating events and reconstructing entities, and then queries those events and entities for real-time and historical analysis.
To build upon this vision, expanding connections to more partners and data types is just one piece of the puzzle (e.g. expanded support for data lakes and Azure in 2024). Adding a data type is one thing, but making it so the data can be queried efficiently is a game changer. Our goal is to make ingestion as easy as possible, minimizing the workarounds to prepare your data for transformation and analysis (e.g. async query, dimension tables).
SQL is the standardized language most developers use when building applications, so we strive to improve ANSI SQL compatibility across all analytics powered by our query engine.
In addition to making significant enhancements to our SQL planner engine in 2024 (e.g. new window functions, larger joins), we also explored ways to simplify the steps to do complex data transformations and analysis, from reducing subqueries to enabling easier visualization (e.g. Imply Pivot enhancements and time series analysis for Imply Polaris).
Druid is optimized for running on clusters with thousands of nodes without having to worry about complexity around data synchronization, metadata management and data movement. Our goal is to push scale and operational efficiency boundaries to new limits.
While the aforementioned SQL planner improvements impact analytical efficiency, there are three main areas that we explored in 2024 to improve query engine performance while compacting storage from a system automation perspective: a new query engine called Dart, continuous defragmentation, and enhanced auto-scaling for Imply Polaris.
The three strategic pillars above focus on the scope, functionality and efficiency of Druid’s query engine, with the majority of updates available in both Druid and Imply. That said, protecting the database itself is of equal importance. While OSS users have to manually implement Druid without the support of Imply’s managed service, they also have to construct entire systems to protect the resiliency, security and governance of the database.
Our fourth strategic pillar focuses exclusively on Imply Polaris, our fully managed database-as-a-service. With Polaris, we took advantage of all of the capabilities above, included all the Druid features, and added a whole layer of enterprise-grade security (private networking, role-based access control, audit logs, compliance), resiliency (fault tolerance, cluster self-healing), elasticity (scaling, no downtime, high availability) and operational efficiency (ZK monitoring UI, data deletion controls, no downtime or patching), deployed in multiple clouds and regions globally.
There are many technical details to explore within these updates, so we encourage you to dive deeper in our 2024 Product Updates: Technical Summary. This expansive Developer Center article fully defines the purpose and value of the top 2024 product updates shown in the image above, plus a few extra updates that didn’t make the top of the list.
Learn More: Full Update Summary
Throughout 2024, we extended our commitment to provide better experiences for Imply and Druid users alike by supporting additional data types, making it easier and more cost effective to conduct complex analysis, improving performance for operational efficiency at scale, and enhancing data protection for better security, resilience, and governance.
While most of these updates are available in Druid OSS, Imply customers benefit from earlier access to updates, 24/7 professional support, enhanced auto-scaling, more robust security and resilience, and many more features summarized here. Many of our customers have found that the cost of Imply pays for itself, reducing the total cost of ownership (TCO) by 50% or more through time savings and reduced data storage.
So what’s next for 2025? As we continue to innovate across ecosystem, analytics, performance, and data protection functionalities, there are three standout projects to look out for in the coming year:
Whether you’re using Druid, Imply, or other analytics tools today, we hope you’ve found some inspiration from what we’ve been working on to reshape the real-time data space.
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