Developer Center > Architecture


Learn key concepts and considerations for system design involving streaming data and real-time analytics.

Things to Consider When Scaling Analytics for High QPS

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?

Why Analytics Needs More than a Data Warehouse

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.

Why Data needs more than CRUD

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.

Overcome tradeoffs with schemaless databases

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.

Three Ways to Use Apache Druid for Machine Learning Workflows

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.

Distributed by Nature: Druid at Scale

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

Real-Time Analytics: Building Blocks and Architecture

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.

Apache Druid: Making 1000+ QPS for Analytics Look Easy

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: Open Source Essentials for Real-Time Data Products

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.

Snowflake or Druid

This video explains the technical differences between Snowflake and Apache Druid and what workloads each data system is best optimized for.

Apache Druid in 5 minutes

This video answers “Why Druid instead of a data warehouse – like Snowflake, BigQuery, or Redshift – or an operational database like PostgreSQL or MongoDB? What makes Druid special?

Apache Druid’s Fit in the Modern Data Stack

In a crowded database market, it can be hard to figure out why to use one technology vs another. This video explains where Druid fits in the data ecosystem relative to other databases.

How is Druid So Fast

This lightboard video explains some of the key Druid concepts that enables it to deliver consistent sub-second performance for analytical queries.

Apache Druid Explained | Core Concepts

This video provides a technical overview of Apache Druid. The intent of this video is to provide technical builders a general understanding of Druid’s core architecture and design principles across ingestion, storage and high performance querying.

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