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config.example.yml
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config.example.yml
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datastorages:
csv_input:
type: csv
data:
location: ./my_data.csv
postgresql_input_0:
type: postgresql
credentials:
host: postgis
user: user
password: password
port: 5432
db: db
data:
query: SELECT * FROM schema.table;
postgresql_input_1:
type: postgresql
credentials:
host: postgis
user: user
password: password
port: 5432
db: db
data:
schema: schema
table: table
postgresql_output:
type: postgresql
credentials:
host: postgis
user: user
password: password
port: 5432
db: db
data:
schema: povo_engine
table: sf_containers
append: True
bigquery_input:
type: bigquery
data:
query: SELECT * FROM `dataset.table` WHERE updated_at >= '2019-05-04T00:00:00Z' AND updated_at < '2019-06-01T00:00:00Z';
carto_input:
type: carto
credentials:
url: https://domain.com/user/user/
api_key: api_key
data:
table: table
carto_output:
type: carto
credentials:
url: https://domain.com/user/user/
api_key: api_key
data:
table: table
append: false
transformations:
pandas_agg:
type: pandas
time:
field: updated_at
start: "2019-05-02T00:00:00Z" # As string or YAML will parse them as DateTimes
finish: "2019-05-03T00:00:00Z"
step: 1H
aggregate:
by:
- id
- updated_at
fields:
field_0: mean
field_1: max
carto_agg:
type: carto
time:
field: updated_at
start: "2019-05-02T00:00:00Z" # As string or YAML will parse them as DateTimes
finish: "2019-05-03T00:00:00Z"
step: 15 minutes
aggregate:
by:
- id
- updated_at
fields:
field_0: [avg, min]
field_1: max
bigquery_agg:
type: bigquery
time:
field: updated_at
start: "2019-05-02T00:00:00Z" # As string or YAML will parse them as DateTimes
finish: "2019-05-03T00:00:00Z"
step: 5 MINUTE
aggregate:
by:
- container_id
- updated_at
fields:
field_0: avg
field_1: max
extract: bigquery_input
transform: bigquery_agg
load: carto_output