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.
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 such as Apache Druid or Pinot for ...
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 ...
