In the era of analytics where query volume is the sine qua non “V” of data, how should we think about system architecture – what matters and why?

For decades, analytics were defined by business intelligence and executive-style reports powered by read-optimized data warehouses. This article dives how an analytics are shifting from batch reporting workflows to real-time application workflows.

After over 30 years of working with data analytics, we’ve been witness (and sometimes participant) to three major shifts in how we find insights from data – and now we’re looking at the fourth.

In this article, we explore the challenges posed by schemaless databases and introduce Druid, a groundbreaking database that seamlessly combines schema flexibility with high-performance capabilities, eliminating the need for trade-offs.

Apache Druid is an excellent addition to any machine learning environment and can facilitate analytics, streamline monitoring, and add real-time data to operations and training.

This blog explains how Druid’s architecture and built-in automation makes it easy to operate and scale in cloud and k8s environments.

There’s an increasing need for immediacy in data analytics, and it’s happening at scale on large data sets. This post unpacks the key building blocks and data architecture for real-time analytics.

This post dives into Apache Druid’s architecture with details on how it can efficiently handle analytics applications needing high QPS.

Apache Kafka, Flink, and Druid, when used together, create a real-time data architecture for a wide range of streaming data-powered use cases from alerting, monitoring, dashboards, ad-hoc exploration, and decisioning workflows.