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1
Introduction
2
The challenges
3
Campaign phases - user's point-of-view
4
Campaign phases - campaign owner's point-of-view
5
Views vs Unique Users
6
Introducing: Apache Druid
7
Roll-up - Simple Count (Views)
8
Druid architecture
9
Common use-cases for Druid
10
Druid in a nutshell
11
What is Theta Sketch?
12
Theta Sketch error
13
The Theta Sketch module in Druid
14
Roll-up - Count Distinct (Unique Users)
15
Funnel analysis pipeline - high-level architecture
16
Funnel analysis pipeline - Data Lake
17
Funnel analysis pipeline - Mart Generator
18
Funnel analysis pipeline - ingesting data into Druid
19
Funnel analysis pipeline - Druid datasources
20
Funnel analysis - simple use-case revisited
21
Funnel analysis pipeline - Enricher
22
Funnel analysis pipeline - querying Druid (SQL)
23
Funnel analysis - complex use-case
24
A few tips
Description:
Explore funnel analysis techniques for measuring advertising campaign effectiveness using Apache Spark and Druid in this 26-minute talk from Databricks. Learn how to combine Spark, Druid, and DataSketches to perform complex funnel analysis at scale, addressing challenges such as tracking chronological event order and distinct user interactions across multiple campaign phases. Discover the architecture of a funnel analysis pipeline, including data lake integration, mart generation, and data enrichment. Gain insights into Druid's capabilities for roll-up operations, Theta Sketch module for count distinct queries, and SQL querying for advanced funnel analysis scenarios. Acquire practical tips for implementing these techniques to evaluate and optimize large-scale advertising campaigns.

Funnel Analysis with Apache Spark and Druid for Advertising Campaign Effectiveness

Databricks
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