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1
Intro
2
History & Motivation
3
Use Cases
4
Business Intelligence Queries
5
Solution Space
6
Relational Database
7
Key/Value Stores
8
General Compute Engine
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Column stores
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Raw data
11
Summarization
12
Segmentation
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Columnar Storage
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Plugin Architecture
15
Approximate Algorithms
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Architecture (Batch Ingestion)
17
Real-time Nodes
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Architecture (Streaming Ingestion)
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Architecture (Lambda)
20
End-to-end Data Stack
21
Integration
22
Takeaway
Description:
Explore the architecture and capabilities of Druid, an analytics data store designed for OLAP queries on event data, in this 45-minute conference talk from Strange Loop. Discover how Druid addresses the challenges of powering interactive data applications at scale, offering millisecond-level query latencies crucial for user-facing analytic applications. Learn about Druid's inspiration from Google's Dremel and PowerDrill, its columnar storage, plugin architecture, and approximate algorithms. Examine the solution space for analytics, including relational databases, key/value stores, and general compute engines, and understand why many large technology companies are adopting Druid. Gain insights into Druid's batch and streaming ingestion capabilities, its lambda architecture, and how it fits into an end-to-end data stack. Walk away with a comprehensive understanding of Druid's strengths in powering interactive analytics and its potential impact on your organization's data strategy.

Powering Interactive Data Applications at Scale

Strange Loop Conference
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