Blog

In this walkthrough, we explore how developers and data teams can use the combination of Rill Data + Google Antigravity to build, modify, and scale analytics workflows entirely in code.

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.

Mike Driscoll interviews Lukas Biewald, co-founder of Weights & Biases. In this conversation, he traces the full arc: from an unpaid internship at a 30-person OpenAI to building one of the definin

Walk through of how Rill enables a fully agentic BI workflow using tools like Cursor, local development environments, and modern CI/CD practices — all while delivering instant, production-ready insigh

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

Learn how to build fast agentic analytics using Claude Code and Rill. Discover how AI coding agents and developer-first BI enable faster metrics, dashboards, and analytics workflows.

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.

Wes McKinney, creator of Pandas & co-creator of Apache Arrow, explains his unexpected switch from Python to Go for new projects. His reason: AI coding agents fundamentally changed the trade-offs t

Alexey Milovidov — co-founder & CTO of ClickHouse — joins Michael Driscoll to break down why ClickHouse has become one of the fastest-growing databases in the world. This conversation goes deep on

Lloyd Tabb — creator of Looker and co-founder of Malloy — joins Rill Data Co-Founder & CEO Michael Driscoll to unpack one of the most misunderstood problems in modern data engineering. From low-la

As the year closes, it's the perfect time to reflect on how Rill has grown and the features that made the biggest difference. Over the past year, we shipped improvements focused on making analytic

Hannes Mühleisen — creator of DuckDB — joins Rill Data Co-Founder & CEO Michael Driscoll to explain why DuckDB has become one of the fastest-growing databases in the world.

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

Matthaus Krzykowski — co-founder & CEO of dltHub — joins Rill Data Co-Founder & CEO Michael Driscoll to break down one of the biggest shifts happening in data engineering right now.

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.

Joe Reis joins Michael Driscoll to break down what he calls The Great Reset—a moment where AI shockwaves, consolidation, and collapsing toolchains have erased the advantages data teams spent a decade building.

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.

Glenn and Mike dive deep into old world vs new world analytics with specific examples of how Rill helps Digital Turbine achieve speed and efficiency.

A comprehensive curated list of data engineering resources. Features trending topics like lakehouse architectures, declarative data stacks, and rising tools such as DuckDB and ClickHouse.

To get the most out of your data conversations, we've compiled a list of our top 10 tips. Get connected, try them out, and let us know if you find any new techniques!

How conversational BI and MCP deliver on two decades of promises

In our inaugural episode of our Data-Driven Leaders Series, we interview Kat Tomlin, Director of Business Operations at Disco.

Michael Driscoll discusses real-time databases and next-generation ETL with a legendary panel of technical founders from ClickHouse), StarTree, MotherDuck, and Tobiko.

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.

We’re excited to introduce Canvas dashboards, a flexible way to arrange and visualize your metrics however you’d like.

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

This video interview covers Pinot's architectural decisions and actual use cases from Uber, Stripe, and Walmart. It also highlights in depth details on Pinot's real time benefits of freshness, latency, and concurrency.

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.

Once metrics are defined with SQL expressions, we automatically generate an exploratory dashboard -- enabling slicing, dicing, and drill down across any dimension.

This blog explores why pivot tables have endured for over decades, how they evolved in the AI era, and why they might be the key to making business intelligence (BI) accessible to everyone.

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

In 2024, we launched more features than we can count and together with our customers solved interesting and challenging data problems. Alex Thor, Product Manager, shares his top 5 features we shipped this year.

In a conversation with Simon Späti, we explore Bluesky’s open social graph, DuckDB’s rise, declarative data systems, and the enduring importance of data modeling.

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?

This presentation was given at a ClickHouse meetup in San Francisco on June 4, 2024

With the File Explorer, developers now have the ability to interface with their entire Rill project natively and restructure the project, including assets and nested folders, to better align with the needs of their use cases or organization.

The pivot table is the most popular tool in data exploration, analysis, and reporting software. Given their widespread popularity and utility, we’re proud to now introduce pivot tables to Rill.

Why not scale up Rill using ClickHouse? We're excited to announce our live connector for ClickHouse. Now it’s possible to build interactive, exploratory, embedded ClickHouse dashboards.

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

2023 has been a wild year for Rill. We have made tremendous improvements into the product. Here is a list of our top features from 2023.

Fireside chat with the founders of Vercel and Tabular

Vibrant panel discussion featuring Edo Liberty, Erik Bernhardsson, and Katrin Ribant

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

Connect MotherDuck with Rill to help your team visualize data and draw timely insights with fast, exploratory dashboards.

Your GitHub data has countless insights waiting to be discovered. Learn how you can build your own GitHub analytics dashboard in minutes with Rill.

Deliver fast, exploratory dashboards in minutes. Install Rill, connect to your S3 or GCS bucket, build SQL-defined dashboards, and deploy to Rill Cloud.

We hosted a webinar with Michael Driscoll and Hammond Guerin, Sr. Director of Analytics at Liftoff to share how the Metamarkets solution for interactive ad tech dashboards has been reimagined into a new solution called Rill.

The data community is obsessed with DuckDB - and so are we. It’s the perfect engine to power Rill, our conversation-fast data profiling and dashboard tool.

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

Neil Buesing crafted four hands-on examples of common Druid roll-up scenarios: roll-up comparison, Kafka ingestion, DataSketches, and custom time granularities.

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

For pipeline management of critical applications, consider the lifecycle of your data. Use this conceptual framework to identify potential issues as early in the lifecycle as possible.

Monitoring the health of data pipelines and the underlying infrastructure-supporting applications and dashboards is mission critical for operational analytics. Learn how to set up our observability architecture using Apache Airflow integrated with Opsgenie and Slack.

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.

A list of articles, customer stories, and reference architectures that best helped our team get up to speed learning about Apache Druid...

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

This is a step-by-step guide on how to leverage Looker’s modeling and ad-hoc analytics with the performance of Druid to generate an operational analytics experience that is fast and interactive.

Part 3 of our Modern Data Stack panel posed the question: "How do you build a data community that is authentic and real?" We considered the differences between audiences and communities, as well as differences between developer and user communities.

This next segment of the Modern Data Stack event had five data company founders respond to "the largest unsolved problems in modern data." They considered: What is the ROI of data, or of a dashboard? How do we reduce complexity? and more...

What does the "Modern Data Stack" really mean? What does it include? We hosted an in-person event in San Francisco to discuss the Modern Data Stack—ETL pipelines, quality monitoring, operational analytics, fast OLAP stores, headless BI, and more...

Leverage the speed of Druid in either a purpose-built analytics deployment or with the ease of your favorite analytics tools. If you have Tableau, use this short guide to walk you through connecting Tableau to Rill’s public Druid cluster.

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 tool that comes with a standard set of connectors to ingest data from Kafka, Amazon S3, Google Cloud Storage, and Azure, and now with Rill Data ... BigQuery.

If you are designing an analytics stack, it’s important to stay flexible. When it comes to analytics, you may choose a data warehouse such as BigQuery or Snowflake for periodic reporting on historical data, and you may choose an operational database.

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.

Apache Druid is purpose built to generate high performance at low cost on a set of use cases that are becoming increasingly common: Operational Analytics. We've assembled the "Apache Druid Optimal Characteristics Checklist" to make it easy to understand the costs and benefits of using Druid for your use case.

Ten years ago today, the Druid data store was introduced to the world by Eric Tschetter, its creator, working at a small start-up named Metamarkets. Eric had left LinkedIn six months earlier to join us as the first full-time employee, and I was the CTO and co-founder, working ...

At Rill, we believe the need for operational intelligence will dramatically expand in the coming years. In this post we lay out why operational intelligence matters now, its salient differences with traditional business intelligence, and why it demands new technology architectures.