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© Andrew Carruthers 2024
A. Carruthers,
Tuning the Snowflake Data Cloud
, https://doi.org/10.1007/979-8-8688-0379-6
Index
A, B
Automatic clustering, 404
cost monitoring, 184, 185
definition, 181
micro-partitions, 182
reclustering, 183, 184
workflow, 182, 183
auto_suspend attribute, 14
C
Carriage return/line feed (CRLF), 156
Cartesian join
definition, 70
example, 71, 72
identifying, 72, 73
implementation approach, 73, 74
join explosion costs, 75
query profile, 71
remediating, 76
stored procedure, 75
TPC baseline data, 70
Client interactions
CSP, 318
curated data products, 317
entitlement model approaches,
319, 320
embedded, 320–322
prefiltered, 322, 323
examples, 319
legacy source data sets, 317
performant data products, 318
standard setting
client collaboration, 349
curated data sets, 353
data catalogs, 351
data model, 350
historized data, 350
hydration approach, 352
imported database entitlement, 349
shared tag references, 351
sharing limited data, 352
SQL use cases, 349
Cloud-based global marketplace, 269
Cloud service provider (CSP), 4, 105,
189, 270
Cluster key management
attribute cardinality, 161
clustered table, 168
default clustering, data load, 160
DTs, 176–179
good/bad partition depth, 166, 167
investigating cluster key, 162–165
investigating unclustered tables,
157, 158
lifecycle, 162
lineitem_baseline table, 170
materialized view, 173–175, 179, 180
objectives, 167
partial date, 169, 170
query rewrite, 181
reclustering, 171
total_constant_partition_count, 172
unclustered table, 172