

Episode 504: Frank McSherry on Materialize
Mar 22, 2022
Chapters
Transcript
Episode notes
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Introduction
00:00 • 2min
Streaming Interstructures - What Are They?
01:30 • 2min
Data Management - Where Does It Materialize Fit In?
03:21 • 4min
Analytics on Top of Steaming Data?
06:52 • 3min
Using Pg Wire to Materialize
09:29 • 3min
Querying Using a Sequel Like Interface
12:02 • 3min
The Differences Between a Traditional Data Base and a Sequel Data Base?
14:44 • 3min
Stream Processing Systems - How to Get Correct Results From Multiple Sources?
17:32 • 3min
Confluent - Is There a Way to Get a Materialize Few?
20:33 • 2min
How to Unpack Objects in Kofka?
22:16 • 2min
How to Share State Across Data Flows
23:49 • 2min
Creating a Materialize View and Creating an Index
25:40 • 2min
Is There a Better Accis Pattern for Your Data?
27:37 • 3min
Getting Consistent Views on Streaming Data?
30:44 • 3min
Stream Processing - Is There a Consistent View?
33:39 • 3min
Is There a Difference Between the Two?
36:18 • 2min
Scaling a Data Warehouse?
38:18 • 4min
Using Windows in a Data Capture Query
42:02 • 2min
Using Materialis in Neted Allisions
44:04 • 3min
Do You Have a Recomputed Kofka Reader?
46:48 • 2min
Can You Replicate Data From Materialized Views?
48:21 • 2min
Do You Support Log Rotation?
50:02 • 2min
Using a Persistence Representation, You Can't Do That
51:34 • 4min
What's in the Near Future, Far Future?
55:26 • 2min