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ernestoarbitrio
April 09, 2017
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Fast In-memory Analytics for Retail Data with Columnar Databases
ernestoarbitrio
April 09, 2017
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Transcript
Fast In-memory Analytics for Retail Data with Columnar Databases Ernesto
Arbitrio - Valerio Maggio arbitrio |
[email protected]
Florence April 6, 2017
Retail Data • Overview of data we have • granularity
• refresh/update rate • Quantity and storage required (space) • services developed around these data
“Materialized Views” • Description of what they are (non-technical) •
Some examples of Analytics we do on this data
The Problem! ~1 TByte Data We need OLAP Performance: 75M
rows -> 5hours
The Solution! Use a Column-oriented Database (i.e. Just swap Rows
with Columns) Chuck Norris Test Passed!
None
Query
None
Thank you get in touch @__pamaron__ @leriomaggio