Data Engineering Podcast

Scale Your Spatial Analysis By Building It In SQL With Syntax Extensions

Feb 7, 2022
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Episode notes
1
Introduction
00:00 • 2min
2
The Best Data Bases for Geo Spatial Analysis
01:57 • 2min
3
Is It a Good Idea to Use Spatial Relationships in the Real World?
03:47 • 3min
4
Post G I S, and Other Implementations of Post S
07:16 • 3min
5
The Difference Between Intersex and Within Versus Overlaps
10:03 • 3min
6
Geospatial Data Engineering - Is There a Common Representation?
13:09 • 4min
7
The Challenges of Segmenting Geometries
16:46 • 3min
8
How Much Background Knowledge Is Necessary?
19:30 • 4min
9
Is There a Difference Between a Projection and an Analysis?
23:01 • 2min
10
Is There a Difference Between a Data Scientist and a Statistical Analyst?
25:27 • 4min
11
Scaling for Multiple Geographies or Cities or Something Like That
29:02 • 3min
12
Geospatial Data Engineer - Is There a Way to Integrate Geometric Data Checks?
32:07 • 4min
13
Data Pipelines - A Data Integration Platform Built for Constant Change
36:02 • 4min
14
Is There a Way to Convert a Vector Into a Geometry?
40:29 • 4min
15
What Are Some of the Coolest Use Cases for Geo Spatial Data?
44:57 • 4min
16
The Challenges of Working With Spatial Data in a Sequel Environment
48:53 • 3min
17
The Best Workflow for Go Data?
51:32 • 4min
18
Geospatial Data - What's Next?
55:16 • 2min
19
The Biggest Gap in Data Management
57:04 • 3min