Here’s a handy summary at the enhancements Millersoft has made to help make Apache Superset™ even more invaluable to our enterprise clients:
In early versions Superset’s email reporting capabilities were relatively limited, typically supporting only basic recipient and subject fields.
Millersoft expanded this functionality significantly by adding CC and BCC fields for broader communication flexibilit. Superset now supports a custom email body, allowing teams to tailor their message content beyond the standard “Explore in Superset” link.
Millersoft has integrated Keycloak authentication, enabling teams to manage user access through a central identity provider. This integration supports enterprise requirements like single sign-on (SSO), role-based access control (RBAC), and multi-factor authentication. Enabling organisations to manage Superset users securely and at scale.
To make sensitive data handling both secure and user-friendly within Superset, Millersoft introduced a new Detokenisation feature. This is available in both SQL Lab and the charting interface, which allows users to view tokenised data in a more readable format.
When enabled, this feature manipulates the returned dataframe to replace tokens (marked with a prefix like t:) with their plaintext values, which makes data exploration and analysis more intuitive for users who have permission.
This new feature balances data security with ease of analysis, ensuring that tokenised values can be revealed only when needed. These PII values are only visible for authorised users.
At Millersoft, we’ve been working to make Apache Druid™ more efficient, flexible, and cost-effective for analytics workloads. One area we’ve focused on is the Python-to-Druid connector (pydruid), which powers integrations with Apache Superset™.
We’re excited to share some improvements we’ve made that open the door to new capabilities in Superset dashboards and the potential for substantial cost savings in large-scale Druid deployments.
We extended the pydruid connector to support Druid’s MSQ (Multi-Stage Query) engine, which means Superset dashboards and charts can now use the MSQ engine. In addition, Superset can finally cancel running Druid queries, preventing wasted resources and speeding up the user experience.
These enhancements make Superset more responsive for analysts and more efficient for operators.
Traditionally, Druid queries rely heavily on historical nodes that keep data loaded on disk. While this ensures speed, it can be expensive to maintain, especially as data volumes grow.
The MSQ engine changes that. Now:
This hybrid model allows companies to reduce the number and size of historical nodes they need to operate.
For example:
Depending on data volumes, this strategy could save thousands of dollars per month in infrastructure costs - all while retaining complete access to historical data.
From the end-user’s perspective, the improvements are seamless:
This makes Superset not just a visualization tool, but also a cost-conscious analytics platform that adapts to both business and technical needs.
The combination of Superset’s flexibility and Druid’s MSQ engine gives organizations a new way to balance performance, cost, and data accessibility.
By making MSQ a first-class citizen in Superset through our pydruid improvements, we’re helping teams:
In other words: do more with less, without sacrificing depth of insight.
When working with Druid SQL, it's easy to fall into a common trap when counting distinct values: using COUNT(DISTINCT ...) directly can sometimes return unexpected results. Recently, I hit a case where COUNT(DISTINCT) returned a different value than selecting the DISTINCT rows manually - and this post explains why that happens, and how to fix it.