Apache Druid: The Foundation of Fortune 500 “Analytical Decision-Making"

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This document discusses Apache Druid, an open-source distributed real-time analytics database. It summarizes Druid's evolution, architecture, use cases, and how companies use it. The document outlines Druid's ability to handle large, high-dimensional datasets with sub-second queries and discusses its core components like segments for efficient storage and parallelism. It concludes by inviting the reader to join the Druid community.

Software

Who Am I
!2
Rommel Garcia
Director, Field Engineering @Imply
Author: Virtualizing Hadoop
10+ years: distributed systems, big data, security, cloud, gpu

Agenda
• Evolution of analytic platforms
• Yet, decision makers wants more
• The technical challenges
• Apache Druid: The Genesis
• Architecture
• Real-time Use Cases
• Powered by Druid
• Join the community!

Yet, decision makers wants more
!5
Still has problems to solve:
• can’t get data fast enough
• interacting with data instantly is tough
• large amount of data to slice and dice, drill down
• need to make decisions now

The technical challenges
!6
• Scale: when data is large, we need a lot of servers
• Speed: aiming for sub-second response time
• Complexity: too much ﬁne grain to precompute
• High dimensionality: 10s or 100s of dimensions
• Concurrency: many users and tenants
• Freshness: load from streams

Apache Druid: The Genesis
!7
Vadim Ogievetsky Gian Merlino Fangjin Yang

Segment
!9
▸ Highly optimized storage unit
▸ Highly compressed bitmap indexes
▸ 150MB - 700MB size
▸ Determines parallelism
▸ Read in memory
▸ No contentions between read and writes
▸ 10x - 75x storage space savings

Real-time Use Cases
!10
• Quality of experience
• Increasing production yield
• Cost to serve
• Pricing optimization
• Ad campaign performance
• Customer behavior analysis
• Netflow performance
• APM
• Security

Powered by Druid
!11
Source: http://druid.io/druid-powered.html

Join the community
!12
Druid community site (current): http://druid.io/
Druid community site (new): https://druid.apache.org/
Imply distribution: https://imply.io/get-started

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Apache Druid: The Foundation of Fortune 500 “Analytical Decision-Making"

1. Rommel Garcia rommel.garcia@imply.io

2. Who Am I !2 Rommel Garcia Director, Field Engineering @Imply Author: Virtualizing Hadoop 10+ years: distributed systems, big data, security, cloud, gpu

3. Agenda • Evolution of analytic platforms • Yet, decision makers wants more • The technical challenges • Apache Druid: The Genesis • Architecture • Real-time Use Cases • Powered by Druid • Join the community!

4. Evolution of analytic platforms !4

5. Yet, decision makers wants more !5 Still has problems to solve: • can’t get data fast enough • interacting with data instantly is tough • large amount of data to slice and dice, drill down • need to make decisions now

6. The technical challenges !6 • Scale: when data is large, we need a lot of servers • Speed: aiming for sub-second response time • Complexity: too much ﬁne grain to precompute • High dimensionality: 10s or 100s of dimensions • Concurrency: many users and tenants • Freshness: load from streams

7. Apache Druid: The Genesis !7 Vadim Ogievetsky Gian Merlino Fangjin Yang

8. Architecture !8

9. Segment !9 ▸ Highly optimized storage unit ▸ Highly compressed bitmap indexes ▸ 150MB - 700MB size ▸ Determines parallelism ▸ Read in memory ▸ No contentions between read and writes ▸ 10x - 75x storage space savings

10. Real-time Use Cases !10 • Quality of experience • Increasing production yield • Cost to serve • Pricing optimization • Ad campaign performance • Customer behavior analysis • Netflow performance • APM • Security

11. Powered by Druid !11 Source: http://druid.io/druid-powered.html

12. Join the community !12 Druid community site (current): http://druid.io/ Druid community site (new): https://druid.apache.org/ Imply distribution: https://imply.io/get-started

13. Try this at home 13

Apache Druid: The Foundation of Fortune 500 “Analytical Decision-Making"

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