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Episode 504: Frank McSherry on Materialize

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