Deep Dives

This blog looks at how BI evolved, how dashboards are actually used today, and what survives when AI enters the picture — starting with the foundation that was never really about dashboards in the first place, and ending with the problem nobody in the AI hype cycle wants to talk about: who maintains it all.

The convergence of embedded analytics engines (DuckDB/MotherDuck), declarative BI-as-code (Rill), and AI agent protocols (MCP) is creating a new architecture for business intelligence, one where dashb

Discover why Coinbase and Pinterest migrated to StarRocks for sub-second analytics. Learn how its lakehouse-native design and colocated joins handle terabyte-scale data on S3.

FinOps Made Easy: A Starter Repo to Oversee Cloud Costs from Different Hyperscalers.

Learn how to unify AWS and GCP costs with revenue data in a single dashboard. Step-by-step guide using dlt, Parquet, and Rill. Clone and run immediately.

Master the three pillars of agentic data modeling: Metrics SQL for semantics, sub-second analytics for speed, and AI guardrails for trusted insights.

Learn how to build sub-second real-time analytics with ClickHouse. Complete guide covering data modeling strategies, optimization techniques, and practical S3-to-dashboard examples.

How conversational BI and MCP deliver on two decades of promises

This blog explores the four layers of the ICE Stack, from storage to catalogs, and why managed Iceberg might represent the post-Modern Data Stack future where data independence truly matters.

By shifting left, data teams can create more maintainable, performant, and reliable data systems while reducing duplication and inconsistency throughout the data stack.

This blog explores how real-time databases address critical analytical requirements. We highlight the differences between cloud data warehouses like Snowflake and BigQuery, legacy OLAP databases like Vertica, and a new class of real-time analytical databases like ClickHouse and StarRocks that combine elements of both of these categories. We will also examine the categories of today's analytics solutions and how to choose the right one.

This blog chronicles that journey of discovery, examining the key architectural considerations and trade-offs in building a declarative data stack and its engine. We'll explore existing approaches, compare different implementation strategies, and work through practical examples

Imagine creating business dashboards by simply describing what you want to see. This is the promise of Generative Business Intelligence (GenBI). The key lies in the declarative BI stack where dashboards and metrics are defined as code rather than hidden behind graphical user interfaces.

The future looks more code-first. From ingestion to transformation, orchestration, and measures in dashboards—all defined declaratively. But what does this shift actually mean?

Rill’s open-source IDE, uses AI to transform an S3 data source into an operational dashboard in one click. This creative magic is made possible by combining (1) Rill’s BI-as-code philosophy defines dashboards entirely with SQL & YAML code and (2) Large language models, like OpenAI’s GPT series, excel at code generation

Time is an integral component of data analysis. Learn about how Rill is built to handle the complexity of time in modern data visualization.

Achieve flow in your work with tools that bring data insights at the speed of conversation.

Apache Druid Rollups improve storage and query performance. Keep these 8 important concepts in mind to use rollups effectively and avoid mistakes.

This blog is a step-by-step how-to demo for streaming data into Rill’s platform for sub-second analysis. We selected aircraft telemetry data to answer time-series analytical questions.

Kubernetes is an orchestration engine which can run and manage containerized applications. Each application has different ways to autoscale...

The transition from on-prem to cloud has picked up speed. While just five years ago, companies were resisting moving to the cloud due to data security concerns, the more common question now is, “How can I move to the cloud as quickly and cost efficiently as possible?”

Apache Druid is an open source data store designed for high performance (sub-second) OLAP queries on large (terabyte) datasets. Learn how you can experience all of the benefits of Apache Druid's high performance real-time analytics database without the maintenance.