Data Engineering Podcast

Speeding Up The Time To Insight For Supply Chains And Logistics With The Pathway Database That Thinks

Oct 16, 2022
Ask episode
Chapters
Transcript
Episode notes
1
Introduction
00:00 • 2min
2
The Story of Pathway
02:13 • 2min
3
Pathway - Challenges in Logistics and Supply Chain
03:45 • 4min
4
IoT Data Cleaning
07:44 • 5min
5
Detecting Anomalies in Logistics Operations
12:16 • 4min
6
The Relationship Between Data Processing, Steam Data Processing and Enterprise Intelligence
16:33 • 4min
7
Using a Data Base to Quickly Update a World View
20:09 • 2min
8
Prefect - The Data Flow Automation Platform for the Modern Data Stack
22:37 • 5min
9
The Pathway Project Is a Framework for Building Data Products
27:20 • 3min
10
The Challenges of Reacting to User Submitted Code
29:52 • 5min
11
Aiming to Embedded Business Logic in an ORM Data System
35:08 • 5min
12
Hevodata Is a Data Pipeline Platform for Data Engineers
40:12 • 5min
13
Using a Reactive Design to React to Data Updates
45:32 • 2min
14
Streaming APIs - What Are the Paradigms of Pathway?
47:27 • 3min
15
The Most Interesting Lessons You've Learned Along the Way
50:04 • 2min
16
The Potential Role of Machine Learning in Data Engineering
51:47 • 3min
17
Reliability Engineering Is a Big Data Engineering Challenge
55:05 • 2min
18
Towards the Developer Launch of the Pathway Product
57:10 • 2min
19
Is There a Gap in the Tooling for Machine Learning?
59:25 • 3min