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1
Introduction
2
Challenges in the industry
3
Feedback
4
Hard curve over
5
Building the pyramid
6
Making programmatic KPI definitions
7
Google Analytics
8
Tableau
9
Project Adventures
10
The Pyramid
11
AV Testing
12
Funding
13
Leftovers
14
Machine Learning
15
Reality
16
One constructive approach
17
Data DevOps
18
Compression
19
Imagenet
20
A Cigar
21
Park
22
Data Engineering
23
Open Roots
24
Our original sin
25
The problem with distributed computing
26
Any advice
27
What makes a data engineer
28
Cloud agnostic advice
29
Better quality code
30
Transit
31
Hadoop
32
Showdown
33
Spark
34
Presto
35
Redshift vs Presto
36
Clickhouse
37
Google
38
Open Source
39
The Shepherd
40
The Bird of Prey
41
Making a decision
42
Finding data engineers
43
Marketing and data integration
44
Y2K
45
Outro
Description:
Explore a pragmatic approach to data-related challenges in this 48-minute conference talk from MLCon. Learn how to tackle problems with limited resources, navigate GDPR concerns, and cut through the hype surrounding deep learning. Discover insights on data integration within organizations, the evolution of roles, and practical problem-solving strategies based on real-world experience. Gain valuable knowledge on making impactful decisions, understanding the data pyramid, and implementing effective KPI definitions. Delve into topics such as Google Analytics, Tableau, AV testing, machine learning realities, data DevOps, and cloud-agnostic advice. Examine the pros and cons of various data engineering tools and technologies, including Hadoop, Spark, Presto, and Clickhouse. Acquire tips on finding skilled data engineers and integrating data with marketing efforts in this comprehensive, down-to-earth presentation for data professionals facing resource constraints.

The Data Janitor Returns

MLCon | Machine Learning Conference
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