GitHub<\/a>, onde tamb\u00e9m est\u00e3o dispon\u00edveis diversos outros exemplos de c\u00f3digo:<\/p>\n#EPM Processor modules\r\nimport epmprocessor as epr\r\nimport epmwebapi as epm\r\n\r\nimport datetime\r\n\r\n\r\n\r\n@epr.applicationMethod('GetHistoryInterpolative')\r\ndef get_history_interpolative(epmdataobject, starttime, endtime, interval_seconds):\r\n\r\n try:\r\n queryperiod = epm.QueryPeriod(starttime,endtime)\r\n processInterval = datetime.timedelta(seconds=interval_seconds)\r\n aggregationdetails = epm.AggregateDetails(processInterval, epm.AggregateType.Interpolative)\r\n data = epmdataobject.historyReadAggregate(aggregationdetails,queryperiod)\r\n\r\n except:\r\n raise Exception('Error in read aggregation')\r\n\r\n print(data)\r\n\r\n return epr.ScopeResult(True)<\/pre>\nEste c\u00f3digo de exemplo serve para consultar os dados de uma vari\u00e1vel informada no par\u00e2metro “epmdataobject” no per\u00edodo informado nos par\u00e2metros “starttime” e “endtime”, com intervalo informado no par\u00e2metro “interval_seconds”.<\/p>\n
Enviando os dados para o BigQuery:<\/h2>\n
A partir deste c\u00f3digo, \u00e9 poss\u00edvel expandir e configur\u00e1-lo para gravar os dados consultados em uma tabela do Google BigQuery. Abaixo, apresentamos um exemplo de c\u00f3digo para essa tarefa:<\/p>\n
import pyodbc\r\n\r\n# Configurar a conex\u00e3o ODBC\r\ndsn = 'DSN=dsBigQuery'\r\n\r\n# Conectar ao BigQuery\r\nconn = pyodbc.connect(dsn)\r\ncursor = conn.cursor()\r\n\r\nselect_table_query = \"select * from MyDatasetTest.MyBQTable;\"\r\ncursor.execute(select_table_query)\r\nconn.commit()\r\n\r\n# Inser\u00e7\u00e3o de dados na tabela\r\ninsert_query = \"INSERT INTO your_dataset.your_table (Name, Timestamp, Value) VALUES ('Nome da vari\u00e1vel ou medida', ?, ?, ?)\"\r\n\"\"\"\r\ncursor.executemany(insert_query, data)\r\nconn.commit()\r\n\r\n# Fechar a conex\u00e3o\r\ncursor.close()\r\nconn.close()<\/pre>\nComo pode ser visto, o c\u00f3digo \u00e9 bastante simples e pode ser alterado conforme a necessidade para lidar com diferentes situa\u00e7\u00f5es.<\/p>\n
Automatizando o envio de dados:<\/h2>\n
O c\u00f3digo pode ser configurado no EPM Processor para execu\u00e7\u00e3o peri\u00f3dica, mantendo a tabela do BigQuery sempre atualizada com novos dados, sem necessidade de interven\u00e7\u00e3o manual.<\/p>\n","protected":false},"excerpt":{"rendered":"
Descri\u00e7\u00e3o: Este artigo demonstra como enviar dados armazenados no Elipse Plant Manager (EPM) para o Google BigQuery, utilizando um c\u00f3digo Python executado pelo EPM Processor e uma conex\u00e3o ODBC com…<\/p>\n","protected":false},"author":22,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_exactmetrics_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0},"categories":[676],"tags":[],"yoast_head":"\n
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