├── 03-01-criar-lake-database-ler-parquet.sql ├── 01-01-ler-arquivo-csv.sql ├── 01-02-ler-arquivo-csv-UTF8.sql ├── 02-02-analisar-cotacao-dolar-criar-tabela-externa.sql ├── 02-03-analisar-cotacao-dolar-criar-view.sql ├── azure-synapse-analytics-controle-versao.md ├── synapse-analytics-ler-arquivo-csv.md ├── 03-02-criar-lake-database-criar-tabela-externa-parquet.sql ├── azure-synapse-obter-municipios-formato-parquet.md ├── azure-synapse-analytics-criar-sql-database.md ├── synapse-analytics-como-criar-quanto-custa.md ├── azure-synapse-analytics-criar-lake-database.md ├── azure-synapse-analytics-integrar-power-bi.md ├── azure-synapse-analisar-cotacao-dolar.md ├── 02-01-analisar-cotacao-dolar.sql ├── azure-synapse-analytics-criar-apache-spark.md ├── azure-synapse-analytics-web-scraping.md ├── 02-analisar-cotacao-dolar-pipeline.json ├── README.md ├── 04-web-scraping.ipynb ├── 05-obter-municipios-formato-parquet.pipeline.json └── LICENSE /03-01-criar-lake-database-ler-parquet.sql: -------------------------------------------------------------------------------- 1 | -- This is auto-generated code 2 | SELECT 3 | TOP 100 * 4 | FROM 5 | OPENROWSET( 6 | BULK 'https://fabiomsdatalake.dfs.core.windows.net/fabiomsfilesys/ibge-municipios.parquet', 7 | FORMAT = 'PARQUET' 8 | ) AS [result] 9 | -------------------------------------------------------------------------------- /01-01-ler-arquivo-csv.sql: -------------------------------------------------------------------------------- 1 | -- This is auto-generated code 2 | SELECT 3 | DISTINCT NO_CURSO 4 | FROM 5 | OPENROWSET( 6 | BULK 'https://fabiomsdatalake.dfs.core.windows.net/fabiomsfilesys/SUP_CURSO_2019.CSV', 7 | FORMAT = 'CSV', 8 |         PARSER_VERSION = '2.0', 9 | FIELDTERMINATOR = '|', 10 | HEADER_ROW = TRUE 11 | ) WITH ( 12 | 13 | [NO_CURSO] VARCHAR(4000) COLLATE Latin1_General_CI_AS 14 | 15 | ) AS [result] 16 | -------------------------------------------------------------------------------- /01-02-ler-arquivo-csv-UTF8.sql: -------------------------------------------------------------------------------- 1 | -- This is auto-generated code 2 | SELECT 3 | TOP 100 * 4 | FROM 5 | OPENROWSET( 6 | BULK 'https://fabiomsdatalake.dfs.core.windows.net/fabiomsfilesys/OnlineRetail.csv', 7 | FORMAT = 'CSV', 8 |         PARSER_VERSION = '2.0', 9 | HEADER_ROW = TRUE 10 | ) 11 | WITH ( 12 | 13 | StockCode VARCHAR(255) COLLATE Latin1_General_100_BIN2_UTF8, 14 | [Description] VARCHAR(255) COLLATE Latin1_General_100_BIN2_UTF8, 15 | Country VARCHAR(255) COLLATE Latin1_General_100_BIN2_UTF8, 16 | Quantity FLOAT 17 | 18 | ) 19 | AS [result] 20 | -------------------------------------------------------------------------------- /02-02-analisar-cotacao-dolar-criar-tabela-externa.sql: -------------------------------------------------------------------------------- 1 | IF NOT EXISTS (SELECT * FROM sys.external_file_formats WHERE name = 'CsvTextFormat') 2 | CREATE EXTERNAL FILE FORMAT [CsvTextFormat] 3 | WITH ( FORMAT_TYPE = DELIMITEDTEXT , 4 | FORMAT_OPTIONS ( 5 | FIELD_TERMINATOR = ';', 6 | STRING_DELIMITER = '"', 7 | USE_TYPE_DEFAULT = FALSE 8 | )) 9 | GO 10 | 11 | IF NOT EXISTS (SELECT * FROM sys.external_data_sources WHERE name = 'dsDatalake') 12 | CREATE EXTERNAL DATA SOURCE [dsDatalake] 13 | WITH ( 14 | LOCATION = 'abfss://fabiomsfilesys@fabiomsdatalake.dfs.core.windows.net' 15 | ) 16 | GO 17 | 18 | CREATE EXTERNAL TABLE BCB_COTACOES ( 19 | [DATA] VARCHAR(10) COLLATE Latin1_General_100_BIN2_UTF8, 20 | MOEDA_COD VARCHAR(5) COLLATE Latin1_General_100_BIN2_UTF8, 21 | TIPO VARCHAR(2) COLLATE Latin1_General_100_BIN2_UTF8, 22 | SIGLA VARCHAR(3) COLLATE Latin1_General_100_BIN2_UTF8, 23 | VALOR VARCHAR(255) COLLATE Latin1_General_100_BIN2_UTF8 24 | ) 25 | WITH ( 26 | LOCATION = 'bcb-dolar-cotacoes.csv', 27 | DATA_SOURCE = [dsDatalake], 28 | FILE_FORMAT = [CsvTextFormat] 29 | ) 30 | GO 31 | 32 | 33 | SELECT TOP 100 * FROM dbo.BCB_COTACOES 34 | GO -------------------------------------------------------------------------------- /02-03-analisar-cotacao-dolar-criar-view.sql: -------------------------------------------------------------------------------- 1 | CREATE VIEW [dbo].[VW_BCB_COTACOES] 2 | AS 3 | 4 | 5 | WITH DATA_VALUES AS ( 6 | 7 | SELECT * 8 | FROM 9 | [BCB_COTACOES] 10 | 11 | ), 12 | 13 | /* 03. Converter Tipo de Dados e uso Funções de Janela */ 14 | DATA_ANALYSIS AS ( 15 | 16 | SELECT 17 | *, 18 | -- Média móvel 50 dias 19 | AVG([Valor]) -- Definir o cálculo 20 | OVER(PARTITION BY Sigla ORDER BY [Data] -- Definir Over 21 | ROWS BETWEEN 49 PRECEDING AND CURRENT ROW)-- Definir Período 22 | AS dia50_media_movel, 23 | 24 | -- Média móvel 200 dias 25 | AVG([Valor]) -- Definir o cálculo 26 | OVER(PARTITION BY Sigla ORDER BY [Data] -- Definir Over 27 | ROWS BETWEEN 199 PRECEDING AND CURRENT ROW)-- Definir Período 28 | AS dia200_media_movel 29 | 30 | FROM ( 31 | SELECT 32 | 33 | SIGLA, 34 | -- String para Datetime 35 | CONVERT(DATETIME, CONCAT(SUBSTRING([DATA],1,2),'-',SUBSTRING([DATA],3,2),'-',SUBSTRING([DATA],5,4)), 103) AS [DATA], --DD-MM-YYYY 36 | -- String para Numeric 37 | CONVERT(NUMERIC(18,4),REPLACE([VALOR],',','.')) AS VALOR 38 | 39 | FROM DATA_VALUES 40 | ) AS RS 41 | 42 | ) 43 | /* 04. Executar a consulta */ 44 | SELECT * 45 | FROM DATA_ANALYSIS 46 | 47 | 48 | -------------------------------------------------------------------------------- /azure-synapse-analytics-controle-versao.md: -------------------------------------------------------------------------------- 1 | ## Como fazer controle de versão no Azure Synapse Analytics 2 | 3 | Como fazer controle de versão no Azure Synapse Analytics 4 | 5 | Apresentamos nesse vídeo como habilitar o controle de versão dos objetos existentes do Azure Synapse Analytics associando ao repositório do Azure DevOps. 6 | 7 | Iremos conhecer as técnicas: 8 | 🔹Criar pasta em repositório do Azure DevOps (REPOS, NEW FOLDER); 9 | 🔹Criar Commit das alterações (COMMIT, COMMENT, BRANCH); 10 | 🔹Configurar controle de versão no Azure Synapse (GIT CONFIGURATION); 11 | 🔹Definir a importação dos objetos existentes (IMPORT EXISTING RESOURCE); 12 | 🔹Definir ramificação de trabalho atual (WORKING BRANCH); 13 | 🔹Salvar alterações do objeto (COMMIT); 14 | 🔹Visualizar os objetos e suas alterações (REPOS, COMPARE, COMMITS). 15 | 16 | ▶️ Acesse o vídeo no link abaixo: 17 | https://www.fabioms.com.br/?url=azure-synapse-analytics-controle-versao 18 | 19 | 📁 Arquivos disponíveis no GitHub: 20 | https://www.fabioms.com.br/?url=github 21 | 22 | 😉 Gostou do conteúdo? Inscreva-se também no canal: 23 | https://www.fabioms.com.br/?url=youtube-subscribe 24 | 25 | #microsoft #dataplatform #azure #synapseanalytics #computação #produtividade #sql #dicadofabinho -------------------------------------------------------------------------------- /synapse-analytics-ler-arquivo-csv.md: -------------------------------------------------------------------------------- 1 | ## Como ler dados em arquivo CSV no Azure Synapse Analytics 2 | 3 | Como ler dados em arquivo CSV no Azure Synapse Analytics 4 | 5 | Diminuímos a curva de aprendizando utilizando o Azure Synapse Analytics para ler dados em arquivo CSV de forma simples e rápida. 6 | 7 | Iremos conhecer as técnicas: 8 | 🔹Enviar arquivos do seu computador para Azure Datalake (UPLOAD); 9 | 🔹Criar script automaticamente de leitura de arquivo CSV (OPENROWSET); 10 | 🔹Definir caracter de separação de coluna (FIELDTERMINATOR); 11 | 🔹Definir primeira linha como cabeçalho (HEADER_ROW); 12 | 🔹Monitorar a quantidade de dados processados; 13 | 🔹Limitar a quantidade de dados processados por período; 14 | 🔹Enviar arquivos do seu computador pelo software Azure Storage Explorer; 15 | 🔹Definir formato de texto para UTF-8 (COLLATE); 16 | 🔹Exibir e salvar o resultado dos dados em gráfico. 17 | 18 | ✅ Acesse o vídeo no link abaixo: 19 | https://www.fabioms.com.br/?url=synapse-analytics-ler-arquivo-csv 20 | 21 | 📁 Arquivos disponíveis no GitHub: 22 | https://www.fabioms.com.br/?url=github 23 | 24 | 😉 Gostou do conteúdo? Inscreva-se também no canal: 25 | https://www.fabioms.com.br/?url=youtube-subscribe 26 | 27 | 🎁 Banco de Dados para Estudo SQL: 28 | Se você quer uma base para estudar liguagem #SQL ou #BusinessIntelligence, elaborar seu curso ou treinamento, aqui você encontrará material. 29 | Contribua e cite o projeto para fortalecê-lo! 30 | https://www.fabioms.com.br/?url=github-estudados 31 | 32 | #microsoft #dataplataform #azure #synapse #analytics #sql #DicaDoFabinho -------------------------------------------------------------------------------- /03-02-criar-lake-database-criar-tabela-externa-parquet.sql: -------------------------------------------------------------------------------- 1 | IF NOT EXISTS (SELECT * FROM sys.external_file_formats WHERE name = 'ParquetFormat') 2 | CREATE EXTERNAL FILE FORMAT [ParquetFormat] 3 | WITH ( FORMAT_TYPE = PARQUET) 4 | GO 5 | 6 | CREATE EXTERNAL TABLE IBGE_Municipios ( 7 | [id] nvarchar(4000), 8 | [nome] nvarchar(4000), 9 | [microrregiao_id] nvarchar(4000), 10 | [microrregiao_nome] nvarchar(4000), 11 | [microrregiao_mesorregiao_id] nvarchar(4000), 12 | [microrregiao_mesorregiao_nome] nvarchar(4000), 13 | [microrregiao_mesorregiao_UF_id] nvarchar(4000), 14 | [microrregiao_mesorregiao_UF_sigla] nvarchar(4000), 15 | [microrregiao_mesorregiao_UF_nome] nvarchar(4000), 16 | [microrregiao_mesorregiao_UF_regiao_id] nvarchar(4000), 17 | [microrregiao_mesorregiao_UF_regiao_sigla] nvarchar(4000), 18 | [microrregiao_mesorregiao_UF_regiao_nome] nvarchar(4000), 19 | [regiao-imediata_id] nvarchar(4000), 20 | [regiao-imediata_nome] nvarchar(4000), 21 | [regiao-imediata_regiao-intermediaria_id] nvarchar(4000), 22 | [regiao-imediata_regiao-intermediaria_nome] nvarchar(4000), 23 | [regiao-imediata_regiao-intermediaria_UF_id] nvarchar(4000), 24 | [regiao-imediata_regiao-intermediaria_UF_sigla] nvarchar(4000), 25 | [regiao-imediata_regiao-intermediaria_UF_nome] nvarchar(4000), 26 | [regiao-imediata_regiao-intermediaria_UF_regiao_id] nvarchar(4000), 27 | [regiao-imediata_regiao-intermediaria_UF_regiao_sigla] nvarchar(4000), 28 | [regiao-imediata_regiao-intermediaria_UF_regiao_nome] nvarchar(4000) 29 | ) 30 | WITH ( 31 | LOCATION = 'ibge-municipios.parquet', 32 | DATA_SOURCE = [dsDatalake], 33 | FILE_FORMAT = [ParquetFormat] 34 | ) 35 | GO 36 | 37 | 38 | SELECT TOP 100 * FROM dbo.IBGE_Municipios 39 | GO -------------------------------------------------------------------------------- /azure-synapse-obter-municipios-formato-parquet.md: -------------------------------------------------------------------------------- 1 | ## Como ler dados no formato JSON no Azure Synapse Analytics 2 | 3 | Como ler dados no formato JSON no Azure Synapse Analytics 4 | 5 | Apresentamos nesse vídeo como ler dados em formato JSON, obtendo os munícipios do Brasil do site IBGE e armazenando no Azure Datalake em format Parquet utilizando o Azure Synapse Analytics. 6 | 7 | Iremos conhecer as técnicas: 8 | 🔹Criar pipeline com atividade de copiar dados; 9 | 🔹Configurar os dados de origem (HTTP, JSON); 10 | 🔹Obter URL dos dados no site do IBGE; 11 | Acesse por: https://servicodados.ibge.gov.br/api/docs 12 | 🔹Mapear o esquema dos dados de origem (IMPORT SCHEMA); 13 | 🔹Configurar os dados de destino (DATALAKE, PARQUET); 14 | 🔹Alguns motivos para utilização do formato Parquet; 15 | 🔹Executar e monitorar gatilho (TRIGGER); 16 | 🔹Identificar os custos da execução do pipeline (RUN CONSUMPTION); 17 | 🔹Gerar script de leitura do arquivo formato PARQUET; 18 | 🔹Mapear as colunas de destino (MAPPING). 19 | 20 | ▶️ Acesse o vídeo no link abaixo: 21 | https://www.fabioms.com.br/?url=azure-synapse-obter-municipios-formato-parquet 22 | 23 | 😉 Gostou do conteúdo? Inscreva-se também no canal: 24 | http://www.fabioms.com.br/?url=youtube-subscribe 25 | 26 | 🎁Conheça o Projeto Banco de Dados de Estudo gratuito, disponível para comunidade no Repositório Github. 27 | http://www.fabioms.com.br/?url=github-estudados 28 | 29 | 📁 Arquivos disponíveis no GitHub, não esquece de seguir: 30 | http://www.fabioms.com.br/?url=github 31 | 32 | #microsoft #dataplatform #datamanagement #azure #synapseanalytics #pipeline #json #parquet #ibge #dataanalysis #analytics #DicadoFabinho -------------------------------------------------------------------------------- /azure-synapse-analytics-criar-sql-database.md: -------------------------------------------------------------------------------- 1 | ## Como criar banco de dados SQL no Azure Synapse Analytics 2 | 3 | Como criar banco de dados SQL no Azure Synapse Analytics 4 | 5 | Apresentamos nesse vídeo como utilizar a estrutura do Azure Synapse Analytics para criar o banco de dados SQL Serverless e disponibilizar os dados no Power BI. 6 | 7 | Iremos conhecer as técnicas: 8 | 🔹Criar o banco de dados no SQL Pool Serverless; 9 | 🔹Gerar o script de criação de tabela externa; 10 | 🔹Definir o tratamento dos dados nulos ou vazios (USE_TYPE_DEFAULT); 11 | 🔹Código SQL para criar o objeto 'Formato do Arquivo Externo' (CREATE EXTERNAL FILE FORMAT); 12 | 🔹Código SQL para criar o objeto 'Fonte de dados Externa' (CREATE EXTERNAL DATA SOURCE); 13 | 🔹Identificar o caminlho para a fonte de dados (Azure Datalake); 14 | 🔹Código SQL para criar o objeto 'Tabela Externa' (CREATE EXTERNAL TABLE); 15 | 🔹Definir as colunas, os tipos de dados, e o formato UTF8 (COLLATE); 16 | 🔹Definir o nome do arquivo CSV para popular a tabela (LOCATION); 17 | 🔹Criar Exibição adicionando a média móvel da cotação da moeda (CREATE VIEW); 18 | 🔹Conectar o Power BI ao Azure Synapse Analytics SQL; 19 | 🔹Escolher o modo de contectividade para reduzir os custos; 20 | 🔹Exibir os dados analisando em visual de linha. 21 | 22 | ▶️ Acesse o vídeo no link abaixo: 23 | https://www.fabioms.com.br/?url=azure-synapse-analytics-criar-sql-database 24 | 25 | 📁 Arquivos disponíveis no GitHub: 26 | https://www.fabioms.com.br/?url=github 27 | 28 | 😉 Gostou do conteúdo? Inscreva-se também no canal: 29 | https://www.fabioms.com.br/?url=youtube-subscribe 30 | 31 | #microsoft #dataplatform #azure #synapse #analytics #sql #database #DicaDoFabinho -------------------------------------------------------------------------------- /synapse-analytics-como-criar-quanto-custa.md: -------------------------------------------------------------------------------- 1 | ## Como Criar e Quanto Custa o Azure Synapse Analytics 2 | 3 | Como Criar e Quanto Custa o Azure Synapse Analytics 4 | 5 | Apresentamos nesse vídeo como criar, e quanto custa o recurso Azure Synapse Analytics, disponibilizando um ambiente integrado com ingestão de dados e computação em nuvem para aumentar a produtividade reduzindo a curva de aprendizado. 6 | 7 | Iremos conhecer as técnicas: 8 | 🔹Localizar o recurso Azure Synapse Analytics no portal do Azure (CREATE A RESOURCE); 9 | 🔹Definir as configurações dos Grupos de Recursos (RESOURCE GROUP); 10 | 🔹Definir a criação automática do Azure Datalake e do sistema de arquivo (AZURE DATA LAKE GEN2); 11 | 🔹Definir as configurações de acesso ao Azure Synapse Analytics (AUTHENTICATION, FIREWALL); 12 | 🔹Conhecer os tipos de computação em nuvem disponíveis (T-SQL, APACHE SPARK, AZURE DATA EXPLORER); 13 | 🔹Conhecer os custos agregados aos recursos. (SERVERLESS, DEDICATED POOL). 14 | 15 | ▶️ Acesse o vídeo no link abaixo: 16 | https://www.fabioms.com.br/?url=synapse-analytics-como-criar-quanto-custa 17 | 18 | 📁 Arquivos disponíveis no GitHub, me segue por lá: 19 | https://www.fabioms.com.br/?url=github 20 | 21 | 😉 Gostou do conteúdo? Inscreva-se também no canal: 22 | https://www.fabioms.com.br/?url=youtube-subscribe 23 | 24 | 🎁 Banco de Dados para Estudo SQL: 25 | Se você quer uma base para estudar liguagem #SQL ou #BusinessIntelligence, elaborar seu curso ou treinamento, aqui você encontrará material. 26 | Contribua e cite o projeto para fortalecê-lo! 27 | https://www.fabioms.com.br/?url=github-estudados 28 | 29 | 30 | #microsoft #dataplatform #azure #synapseanalytics #computação #produtividade #sql #DicaDoFabinho -------------------------------------------------------------------------------- /azure-synapse-analytics-criar-lake-database.md: -------------------------------------------------------------------------------- 1 | ## Como criar Lake Database no Azure Synapse Analytics 2 | 3 | Como criar Lake Database no Azure Synapse Analytics 4 | 5 | Apresentamos nesse vídeo como criar o Lake Database, uma estrutura mais intuitiva que integra o melhor do Datalake com o banco de dados SQL do Azure Synapse Analytics. 6 | 7 | Iremos conhecer as técnicas: 8 | 🔹 Converter arquivo CSV para PARQUET (PYTHON, AZURE DATA STUDIO); 9 | 🔹 Upload de arquivos para o Azure Data Lake (AZURE STORAGE EXPLORER); 10 | 🔹 Criar Lake Database na estrutura do Azure Synapse Analytics (LAKE DATABASE); 11 | 🔹 Adicionar Tabela com os dados vinculados a arquivos PARQUET (LINKED SERVICE, DATALAKE, PARQUET); 12 | 🔹 Criar Script SQL de consulta da tabela (SELECT, TOP); 13 | 🔹 Alterar o Tipo de Dados para as colunas (COLUMNS, DATA TYPE); 14 | 🔹 Criar relacionamento entre as tabelas (RELATIONSHIPS, FROM TABLE, FOREIGN KEY); 15 | 🔹 Consumir os dados utilizando o Power BI (GET DATA, SQL SERVERLESS, IMPORT); 16 | 🔹 Visualizar Modelo dos dados e relacionamento entre as tabelas (POWER BI). 17 | 18 | ▶️ Acesse o vídeo no link abaixo: 19 | https://www.fabioms.com.br/?url=azure-synapse-analytics-criar-lake-database 20 | 21 | 📁 Arquivos disponíveis no GitHub: 22 | https://www.fabioms.com.br/?url=github 23 | 24 | 😉 Gostou do conteúdo? Inscreva-se também no canal: 25 | https://www.fabioms.com.br/?url=youtube-subscribe 26 | 27 | 🎁 Banco de Dados para Estudo SQL: 28 | Se você quer uma base para estudar liguagem #SQL ou #BusinessIntelligence, elaborar seu curso ou treinamento, aqui você encontrará material. 29 | Contribua e cite o projeto para fortalecê-lo! 30 | https://www.fabioms.com.br/?url=github-estudados 31 | 32 | #microsft #dataplatform #azure #synapseanalytics #lakedatabase #sql #data #datatype #DicaDoFabinho -------------------------------------------------------------------------------- /azure-synapse-analytics-integrar-power-bi.md: -------------------------------------------------------------------------------- 1 | ## Como integrar o Power BI Serviço no Azure Synapse Analytics 2 | 3 | Como integrar o Power BI Serviço no Azure Synapse Analytics 4 | 5 | Nesse vídeo apresentamos como integrar o Power BI Serviço no Azure Synapse Analytics utilizando a publicação de relatório existente e criando um novo dentro dessa estrutura. 6 | 7 | Iremos conhecer as técnicas: 8 | 🔹Criar o Serviço Vinculado do Power BI (LINKED SERVICE); 9 | 🔹Selecionar a área de trabalho Power BI Serviço (WORKSPACE); 10 | 🔹Exibir a estrutura do Power BI Serviço no Azure Synapse Analytics (DEVELOP); 11 | 🔹Publicar relatório Power BI Desktop no Power BI Serviço (PUBLISH); 12 | 🔹Utilizar o conjunto de dados e relatório Power BI Serviço no Azure Synapse (DATASETS, REPORTS); 13 | 🔹Criar tabela externa (CREATE EXTERNAL TABLE); 14 | 🔹Criar novo conjunto de dados Power BI pelo Azure Synapse Analytics; 15 | 🔹Definir modo de conectividade do Power BI Desktop (IMPORT, SQL POOL SERVERLESS); 16 | 🔹Criar novo relatório Power BI pelo Azure Synapse Analytics (REPORT). 17 | 18 | ▶️ Acesse o vídeo no link abaixo: 19 | https://www.fabioms.com.br/?url=azure-synapse-analytics-integrar-power-bi 20 | 21 | 📁 Arquivos disponíveis no GitHub: 22 | https://www.fabioms.com.br/?url=github 23 | 24 | 😉 Gostou do conteúdo? Inscreva-se também no canal: 25 | https://www.fabioms.com.br/?url=youtube-subscribe 26 | 27 | 🎁 Banco de Dados para Estudo SQL: 28 | Se você quer uma base para estudar liguagem #SQL ou #BusinessIntelligence, elaborar seu curso ou treinamento, aqui você encontrará material. 29 | Contribua e cite o projeto para fortalecê-lo! 30 | https://www.fabioms.com.br/?url=github-estudados 31 | 32 | #microsoft #dataplatform #azure #synapseanalytics #datacompute #powerbi #businessreports #datasets #sql #DicaDoFabinho -------------------------------------------------------------------------------- /azure-synapse-analisar-cotacao-dolar.md: -------------------------------------------------------------------------------- 1 | ## Como fazer análise de Cotação do dolar no Azure Synapse Analytics 2 | 3 | Como fazer análise de Cotação do dolar no Azure Synapse Analytics 4 | 5 | Apresentamos nesse vídeo como analisar a cotação do dolar utilizando o ambiente centralizado de funcionalidades do Azure Synapse Analytics. 6 | 7 | Iremos conhecer as técnicas: 8 | 🔹Obter dados da página do Banco Central do Brasil (URL de DOWNLOAD); 9 | 🔹Criar no Synapase novo processo de copiar dados; 10 | 🔹Definir a fonte de dados de origem (HTTP); 11 | 🔹Configurar a formatação do arquivo (Delimitador da colunas); 12 | 🔹Definir a fonte de dados de destino (Azure Data Lake Gen2); 13 | 🔹Executar Pipeline (Gerenciador das atividades de cópia dos dados); 14 | 🔹Obter a URL do arquivo no Azure Datalake; 15 | 🔹Utilizar Script T-SQL para análise dos dados; 16 | 🔹Definir o formato dos dados para UTF-8; 17 | 🔹Calcular a média móvel das cotações do dolar (AVG, OVER); 18 | 🔹Converter o tipo de dados (CONVERT, SUBSTRING, NUMERIC); 19 | 🔹Visualizar o resultado em gráfico para análise Golden Cross/Death Cross. 20 | 21 | 👍Deixe seu Like para valorizar o conteúdo. 22 | 23 | ▶️ Acesse o vídeo no link abaixo: 24 | https://www.fabioms.com.br/?url=azure-synapse-analisar-cotacao-dolar 25 | 26 | 📁 Arquivos disponíveis no GitHub: 27 | https://www.fabioms.com.br/?url=github 28 | 29 | 😉 Gostou do conteúdo? Inscreva-se também no canal: 30 | https://www.fabioms.com.br/?url=youtube-subscribe 31 | 32 | 🎁 Banco de Dados para Estudo SQL: 33 | Se você quer uma base para estudar liguagem #SQL ou #BusinessIntelligence, elaborar seu curso ou treinamento, aqui você encontrará material. 34 | Contribua e cite o projeto para fortalecê-lo! 35 | https://www.fabioms.com.br/?url=github-estudados 36 | 37 | #microsoft #dataplatform #azure #synapseanalytics #analytics #cotacaodolar #DicadoFabinho -------------------------------------------------------------------------------- /02-01-analisar-cotacao-dolar.sql: -------------------------------------------------------------------------------- 1 | /* Uso de Expressão de Tabela Comum (CTE) */ 2 | 3 | WITH DATA_VALUES AS ( 4 | SELECT * 5 | FROM 6 | OPENROWSET( 7 | /* 01. Definir o caminho do arquivo CSV */ 8 | BULK 'https://fabiomsdatalake.dfs.core.windows.net/fabiomsfilesys/bcb-dolar-cotacoes.csv', 9 | FORMAT = 'CSV', 10 |         PARSER_VERSION = '2.0', 11 | FIELDTERMINATOR = ';' 12 | ) 13 | WITH ( 14 | 15 | /* 02. Definir formato dos dados COLLATE */ 16 | [DATA] VARCHAR(10) COLLATE Latin1_General_100_BIN2_UTF8, 17 | MOEDA_COD VARCHAR(5) COLLATE Latin1_General_100_BIN2_UTF8, 18 | TIPO VARCHAR(2) COLLATE Latin1_General_100_BIN2_UTF8, 19 | SIGLA VARCHAR(3) COLLATE Latin1_General_100_BIN2_UTF8, 20 | VALOR VARCHAR(255) COLLATE Latin1_General_100_BIN2_UTF8 21 | 22 | ) 23 | AS [result] 24 | ), 25 | 26 | /* 03. Converter Tipo de Dados e uso Funções de Janela */ 27 | DATA_ANALYSIS AS ( 28 | 29 | SELECT 30 | *, 31 | -- Média móvel 50 dias 32 | AVG([Valor]) -- Definir o cálculo 33 | OVER(PARTITION BY Sigla ORDER BY [Data] -- Definir Over 34 | ROWS BETWEEN 49 PRECEDING AND CURRENT ROW)-- Definir Período 35 | AS dia50_media_movel, 36 | 37 | -- Média móvel 200 dias 38 | AVG([Valor]) -- Definir o cálculo 39 | OVER(PARTITION BY Sigla ORDER BY [Data] -- Definir Over 40 | ROWS BETWEEN 199 PRECEDING AND CURRENT ROW)-- Definir Período 41 | AS dia200_media_movel 42 | 43 | FROM ( 44 | SELECT 45 | 46 | SIGLA, 47 | -- String para Datetime 48 | CONVERT(DATETIME, CONCAT(SUBSTRING([DATA],1,2),'-',SUBSTRING([DATA],3,2),'-',SUBSTRING([DATA],5,4)), 103) AS [DATA], --DD-MM-YYYY 49 | -- String para Numeric 50 | CONVERT(NUMERIC(18,4),REPLACE([VALOR],',','.')) AS VALOR 51 | 52 | FROM DATA_VALUES 53 | ) AS RS 54 | 55 | ) 56 | /* 04. Executar a consulta */ 57 | SELECT * 58 | FROM DATA_ANALYSIS 59 | 60 | -------------------------------------------------------------------------------- /azure-synapse-analytics-criar-apache-spark.md: -------------------------------------------------------------------------------- 1 | ## Como criar Apache Spark no Azure Synapse Analytics 2 | 3 | Como criar Apache Spark no Azure Synapse Analytics 4 | 5 | Apresentamos nesse vídeo como criar o Apache Spark no Azure Synapse Analytics para aumentar a capacidade de computação em nuvem nas análises dos dados. 6 | 7 | Iremos conhecer as técnicas: 8 | 🔹Definir computação Isolada das análises (ISOLATED COMPUTE); 9 | 🔹Definir o tipo de arquitetura e quantidade de processanto do instância principal (NODE SIZE, MEMORY OPTIMIZED, HARDWARE ACCELERATED); 10 | 🔹Definir dimensionamento automático (AUTOSCALE); 11 | 🔹Definir a quantidade de nós (EXECUTORS); 12 | 🔹Definir pausar automaticamente (AUTOMATIC PAUSING); 13 | 🔹Definir versão da instância Apache Spark (VERSION, PREVIEW); 14 | 🔹Habilitar a instalação de pacotes em nível de sessão (ALLOW SESSION LEVE PACKAGES); 15 | 🔹Definir etiquetas (TAGS); 16 | 🔹Visualizar e alterar configuração da instância criada (SCALE SETTINGS); 17 | 🔹Criar novo notebook anexando a instância Apache Spark (ATTACH TO); 18 | 🔹Definir a linguagem de programação do notebook (LANGUAGE); 19 | 🔹Criar células de codigo e de texto formatado (CODE, MARKDOWN); 20 | 🔹Executar notebook (RUN ALL, SESSION START); 21 | 🔹Monitorar instância Apache Spark (ALLOCATED vCores, Memory, ACTIVE APPLICATIONS) 22 | 🔹Parar a execução da sessão (STOP SESSION). 23 | 24 | ▶️ Acesse o vídeo no link abaixo: 25 | https://www.fabioms.com.br/?url=azure-synapse-analytics-criar-apache-spark 26 | 27 | 😉 Gostou do conteúdo? Inscreva-se também no canal: 28 | https://www.fabioms.com.br/?url=youtube-subscribe 29 | 30 | 📁 Arquivos disponíveis no GitHub, não esqueça de seguir: 31 | https://www.fabioms.com.br/?url=github 32 | 33 | 🎁 Banco de Dados para Estudo SQL: 34 | Se você quer uma base para estudar liguagem #SQL ou #BusinessIntelligence, elaborar seu curso ou treinamento, aqui você encontrará material. 35 | Contribua e cite o projeto para fortalecê-lo! 36 | https://www.fabioms.com.br/?url=github-estudados 37 | 38 | #microsft #dataplatform #azure #synapseanalytics #apache #spark #data #DicaDoFabinho -------------------------------------------------------------------------------- /azure-synapse-analytics-web-scraping.md: -------------------------------------------------------------------------------- 1 | ## Como Fazer Web Scraping no Azure Synapse Analytics 2 | 3 | Como Fazer Web Scraping no Azure Synapse Analytics 4 | 5 | Apresentamos nesse video como utilizar a técnica de Web Scraping para extrair dados de páginas da internet utilizando o Python no Azure Synapse Analytics. 6 | 7 | Web Scraping é um processo simples de coleta de dados existentes em páginas na internet. 8 | 9 | Com o Azure Synapse Analytics, você pode coletar facilmente dados de todas as suas páginas da Web de maneira segura e escalonável. 10 | 11 | Iremos conhecer: 12 | 🔹Utilizar notebook com instância Apache Spark anexado e linguagem de programação Python (ATTACH TO, LANGUAGE, PYSPARK) 13 | 🔹Visualizar os recursos associados a instância Apache Spark (CONFIGURE SESSION) 14 | 🔹Identificar os pacotes instalados da instância Apache Spark (PKG_RESOURCES, WORKING_SET, PRINT) 15 | 🔹Importar pacotes Python (PANDAS, REQUESTS, BEAUTIFULSOUP) 16 | 🔹Obter contéudo de página de internet (REQUESTS, HTML CODE, ELEMENTS, TABLES, ROWS, COLUMNS); 17 | 🔹Converter elementos do código HTML em lista Array (BEAUTIFULSOUP, HTML5LIB); 18 | 🔹Interagir e identificar elementos na lista Array (FIND_ALL, TABLE, FOR, ENUMERATE); 19 | 🔹Criar Dataframe (PANDAS, COLUMNS); 20 | 🔹Adicionar registros ao Dataframe (APPEND); 21 | 🔹Visualizar os dados existentes no Dataframe; 22 | 🔹Salvar os registros do Dataframe em arquivo no formato Parquet (PANDAS, TO_PARQUET, AZURE DATA LAKE STORAGE); 23 | 🔹Consultar os registros em script SQL; 24 | 25 | ▶️ Acesse o vídeo no link abaixo: 26 | https://www.fabioms.com.br/?url=azure-synapse-analytics-web-scraping 27 | 28 | 😉 Gostou do conteúdo? Inscreva-se também no canal: 29 | https://www.fabioms.com.br/?url=youtube-subscribe 30 | 31 | 🎁 Banco de Dados para Estudo SQL: 32 | Se você quer uma base para estudar liguagem #SQL ou #BusinessIntelligence, elaborar seu curso ou treinamento, aqui você encontrará material. 33 | Contribua e cite o projeto para fortalecê-lo! 34 | https://www.fabioms.com.br/?url=github-estudados 35 | #microsft #dataplatform #azure #synapseanalytics #apache #spark #python #DicaDoFabinho -------------------------------------------------------------------------------- /02-analisar-cotacao-dolar-pipeline.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "p01_BCB_Dolar_Cotacoes", 3 | "properties": { 4 | "description": "Obter os valores de cotações do dolar do Banco Central do Brasil", 5 | "activities": [ 6 | { 7 | "name": "Copy_o1e", 8 | "type": "Copy", 9 | "dependsOn": [], 10 | "policy": { 11 | "timeout": "7.00:00:00", 12 | "retry": 0, 13 | "retryIntervalInSeconds": 30, 14 | "secureOutput": false, 15 | "secureInput": false 16 | }, 17 | "userProperties": [ 18 | { 19 | "name": "Source", 20 | "value": "consultaBoletim.do?method=gerarCSVFechamentoMoedaNoPeriodo&ChkMoeda=61&DATAINI=01/01/2021&DATAFIM=11/04/2022" 21 | }, 22 | { 23 | "name": "Destination", 24 | "value": "fabiomsfilesys//bcb-dolar-cotacoes.csv" 25 | } 26 | ], 27 | "typeProperties": { 28 | "source": { 29 | "type": "DelimitedTextSource", 30 | "storeSettings": { 31 | "type": "HttpReadSettings", 32 | "requestMethod": "GET" 33 | }, 34 | "formatSettings": { 35 | "type": "DelimitedTextReadSettings", 36 | "skipLineCount": 0 37 | } 38 | }, 39 | "sink": { 40 | "type": "DelimitedTextSink", 41 | "storeSettings": { 42 | "type": "AzureBlobFSWriteSettings" 43 | }, 44 | "formatSettings": { 45 | "type": "DelimitedTextWriteSettings", 46 | "quoteAllText": true, 47 | "fileExtension": ".txt" 48 | } 49 | }, 50 | "enableStaging": false 51 | }, 52 | "inputs": [ 53 | { 54 | "referenceName": "SourceDataset_o1e", 55 | "type": "DatasetReference" 56 | } 57 | ], 58 | "outputs": [ 59 | { 60 | "referenceName": "DestinationDataset_o1e", 61 | "type": "DatasetReference" 62 | } 63 | ] 64 | } 65 | ], 66 | "annotations": [], 67 | "lastPublishTime": "2022-04-12T00:04:48Z" 68 | }, 69 | "type": "Microsoft.Synapse/workspaces/pipelines" 70 | } -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Azure Synapse Analytics 2 | ### **Aprender análise de dados no Azure Synapse Analytics** 3 | **Keywords:** Data Platform, Azure Synapse Analytics, Synapse Analytics, Azure Synapse, analytics service, big data, limitless analytics service, Big Data analytics, data warehousing, 4 | 5 | 😉 Gostou do conteúdo? Inscreva-se também no canal: [http://www.fabioms.com.br/?url=youtube-subscribe](http://www.fabioms.com.br/?url=youtube-subscribe) 6 | 7 | > **Youtube playlist**: [https://youtube.com/playlist?list=PL3CylihEP9UT6enQzrtzzgYWSXfAXizu7](https://youtube.com/playlist?list=PL3CylihEP9UT6enQzrtzzgYWSXfAXizu7) 8 | 9 | 10 | ## [1. Como Criar e Quanto Custa o Azure Synapse Analytics](/synapse-analytics-como-criar-quanto-custa.md) 11 | Apresentamos nesse vídeo como criar, e quanto custa o recurso Azure Synapse Analytics, disponibilizando um ambiente integrado com ingestão de dados e computação em nuvem para aumentar a produtividade reduzindo a curva de aprendizado. 12 | > Assista ao vídeo: [http://www.fabioms.com.br/?url=synapse-analytics-como-criar-quanto-custa](http://www.fabioms.com.br/?url=synapse-analytics-como-criar-quanto-custa) 13 | 14 | ## [2. Como ler dados em arquivo CSV no Azure Synapse Analytics](/synapse-analytics-ler-arquivo-csv.md) 15 | Diminuímos a curva de aprendizando utilizando o Azure Synapse Analytics para ler dados em arquivo CSV de forma simples e rápida. 16 | > Assista ao vídeo: [http://www.fabioms.com.br/?url=synapse-analytics-ler-arquivo-csv](http://www.fabioms.com.br/?url=synapse-analytics-ler-arquivo-csv) 17 | 18 | ## [3. Como fazer análise de Cotação do dolar no Azure Synapse Analytics](/azure-synapse-analisar-cotacao-dolar.md) 19 | Apresentamos nesse vídeo como analisar a cotação do dolar utilizando o ambiente centralizado de funcionalidades do Azure Synapse Analytics. 20 | > Assista ao vídeo: [http://www.fabioms.com.br/?url=azure-synapse-analisar-cotacao-dolar](http://www.fabioms.com.br/?url=azure-synapse-analisar-cotacao-dolar) 21 | 22 | ## [4. Como criar banco de dados SQL no Azure Synapse Analytics](/azure-synapse-analytics-criar-sql-database.md) 23 | Como utilizar a estrutura do Azure Synapse Analytics para criar o banco de dados SQL Serverless e disponibilizar os dados no Power BI 24 | > Assista ao vídeo: [http://www.fabioms.com.br/?url=azure-synapse-analytics-criar-sql-database](http://www.fabioms.com.br/?url=azure-synapse-analytics-criar-sql-database) 25 | 26 | ## [5. Como ler dados no formato JSON no Azure Synapse Analytics](/azure-synapse-obter-municipios-formato-parquet.md) 27 | Apresentamos nesse vídeo como obter os munícipios do Brasil utilizando o Azure Synapse Analytics com extração dos dados em formato JSON na internet e armazenando no Azure Datalake em format Parquet. 28 | 29 | > Assista ao vídeo: [http://www.fabioms.com.br/?url=azure-synapse-obter-municipios-formato-parquet](http://www.fabioms.com.br/?url=azure-synapse-obter-municipios-formato-parquet) 30 | 31 | ## [6. Como integrar o Power BI Serviço no Azure Synapse Analytics](/azure-synapse-analytics-integrar-power-bi.md) 32 | Como integrar o Power BI Serviço no Azure Synapse Analytics utilizando a publicação de relatório dentro dessa estrutura 33 | > Assista ao vídeo: [http://www.fabioms.com.br/?url=azure-synapse-analytics-integrar-power-bi](http://www.fabioms.com.br/?url=azure-synapse-analytics-integrar-power-bi) 34 | 35 | ## [7. Como criar Lake Database no Azure Synapse Analytics](/azure-synapse-analytics-criar-lake-database.md) 36 | Apresentamos nesse vídeo como criar o Lake Database, uma estrutura mais intuitiva que integra o melhor do Datalake com o banco de dados SQL do Azure Synapse Analytics. 37 | > Assista ao vídeo: [http://www.fabioms.com.br/?url=azure-synapse-analytics-criar-lake-database](http://www.fabioms.com.br/?url=azure-synapse-analytics-criar-lake-database) 38 | 39 | ## [8. Como criar Apache Spark no Azure Synapse Analytics](/azure-synapse-analytics-criar-apache-spark.md) 40 | Apresentamos nesse vídeo como criar o Apache Spark no Azure Synapse Analytics para aumentar a capacidade de computação em nuvem nas análises dos dados. 41 | > Assista ao vídeo: [http://www.fabioms.com.br/?url=azure-synapse-analytics-criar-apache-spark](http://www.fabioms.com.br/?url=azure-synapse-analytics-criar-apache-spark) 42 | 43 | ## [9. Como Fazer Web Scraping no Azure Synapse Analytics](/azure-synapse-analytics-web-scraping.md) 44 | Apresentamos nesse video como utilizar a técnica de Web Scraping para extrair dados de páginas da internet utilizando o Python no Azure Synapse Analytics. 45 | 46 | > Assista ao vídeo: [http://www.fabioms.com.br/?url=azure-synapse-analytics-web-scraping](http://www.fabioms.com.br/?url=azure-synapse-analytics-web-scraping) 47 | 48 | ## [10. Como fazer controle de versão no Azure Synapse Analytics](/azure-synapse-analytics-controle-versao.md) 49 | Como habilitar o controle de versão dos objetos existentes do Azure Synapse Analytics associando ao repositório do Azure DevOps 50 | > Assista ao vídeo: [http://www.fabioms.com.br/?url=azure-synapse-analytics-controle-versao](http://www.fabioms.com.br/?url=azure-synapse-analytics-controle-versao) 51 | -------------------------------------------------------------------------------- /04-web-scraping.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "nbformat": 4, 3 | "nbformat_minor": 2, 4 | "metadata": { 5 | "kernelspec": { 6 | "name": "synapse_pyspark", 7 | "display_name": "Synapse PySpark" 8 | }, 9 | "language_info": { 10 | "name": "python" 11 | }, 12 | "save_output": true, 13 | "synapse_widget": { 14 | "version": "0.1", 15 | "state": {} 16 | } 17 | }, 18 | "cells": [ 19 | { 20 | "cell_type": "markdown", 21 | "metadata": { 22 | "nteract": { 23 | "transient": { 24 | "deleting": false 25 | } 26 | } 27 | }, 28 | "source": [ 29 | "# Identificar os pacotes " 30 | ] 31 | }, 32 | { 33 | "cell_type": "code", 34 | "execution_count": 10, 35 | "outputs": [], 36 | "metadata": {}, 37 | "source": [ 38 | "#identificar os pacotes\r\n", 39 | "import pkg_resources\r\n", 40 | "for nome in pkg_resources.working_set:\r\n", 41 | " print(nome)" 42 | ] 43 | }, 44 | { 45 | "cell_type": "markdown", 46 | "metadata": { 47 | "nteract": { 48 | "transient": { 49 | "deleting": false 50 | } 51 | } 52 | }, 53 | "source": [ 54 | "# Importar pacotes" 55 | ] 56 | }, 57 | { 58 | "cell_type": "code", 59 | "execution_count": 11, 60 | "outputs": [], 61 | "metadata": { 62 | "jupyter": { 63 | "source_hidden": false, 64 | "outputs_hidden": false 65 | }, 66 | "nteract": { 67 | "transient": { 68 | "deleting": false 69 | } 70 | } 71 | }, 72 | "source": [ 73 | "import pandas as pd # importar para criar o dataframe\r\n", 74 | "import requests # importar para consulta web\r\n", 75 | "from bs4 import BeautifulSoup as bs # importar para ler html" 76 | ] 77 | }, 78 | { 79 | "cell_type": "markdown", 80 | "metadata": { 81 | "nteract": { 82 | "transient": { 83 | "deleting": false 84 | } 85 | } 86 | }, 87 | "source": [ 88 | "# Ler página html" 89 | ] 90 | }, 91 | { 92 | "cell_type": "code", 93 | "execution_count": 12, 94 | "outputs": [], 95 | "metadata": { 96 | "jupyter": { 97 | "source_hidden": false, 98 | "outputs_hidden": false 99 | }, 100 | "nteract": { 101 | "transient": { 102 | "deleting": false 103 | } 104 | } 105 | }, 106 | "source": [ 107 | "url = \"https://www.macrotrends.net/stocks/charts/MSFT/microsoft/stock-price-history\"\r\n", 108 | "html_data = requests.get(url).text # consulta para a página de internet\r\n", 109 | "beautiful_soup = bs(html_data, \"html5lib\") # ler e converter html em objetos" 110 | ] 111 | }, 112 | { 113 | "cell_type": "markdown", 114 | "metadata": { 115 | "nteract": { 116 | "transient": { 117 | "deleting": false 118 | } 119 | } 120 | }, 121 | "source": [ 122 | "# Identificar tabela de dados" 123 | ] 124 | }, 125 | { 126 | "cell_type": "code", 127 | "execution_count": 13, 128 | "outputs": [], 129 | "metadata": { 130 | "jupyter": { 131 | "source_hidden": false, 132 | "outputs_hidden": false 133 | }, 134 | "nteract": { 135 | "transient": { 136 | "deleting": false 137 | } 138 | } 139 | }, 140 | "source": [ 141 | "tabelas = beautiful_soup.find_all(\"table\")\r\n", 142 | "\r\n", 143 | "for id, tabela in enumerate(tabelas):\r\n", 144 | " if (\"Historical Annual Stock Price Data\" in str(tabela)):\r\n", 145 | " tabela_id = id # obter o índice da tabela" 146 | ] 147 | }, 148 | { 149 | "cell_type": "markdown", 150 | "metadata": { 151 | "nteract": { 152 | "transient": { 153 | "deleting": false 154 | } 155 | } 156 | }, 157 | "source": [ 158 | "# Criar Dataframe" 159 | ] 160 | }, 161 | { 162 | "cell_type": "code", 163 | "execution_count": 14, 164 | "outputs": [], 165 | "metadata": { 166 | "jupyter": { 167 | "source_hidden": false, 168 | "outputs_hidden": false 169 | }, 170 | "nteract": { 171 | "transient": { 172 | "deleting": false 173 | } 174 | } 175 | }, 176 | "source": [ 177 | "table_data = pd.DataFrame(columns=[\"Ticker\",\"Year\", \"Average\", \"Open\", \"High\", \"Low\", \"Close\" ]) #Inserir colunas no dataframe" 178 | ] 179 | }, 180 | { 181 | "cell_type": "markdown", 182 | "metadata": { 183 | "nteract": { 184 | "transient": { 185 | "deleting": false 186 | } 187 | } 188 | }, 189 | "source": [ 190 | "# Inserir registros no Dataframe" 191 | ] 192 | }, 193 | { 194 | "cell_type": "code", 195 | "execution_count": 15, 196 | "outputs": [], 197 | "metadata": { 198 | "jupyter": { 199 | "source_hidden": false, 200 | "outputs_hidden": false 201 | }, 202 | "nteract": { 203 | "transient": { 204 | "deleting": false 205 | } 206 | } 207 | }, 208 | "source": [ 209 | "for linha in tabelas[tabela_id].tbody.find_all(\"tr\"):\r\n", 210 | " coluna = linha.find_all(\"td\")\r\n", 211 | " if (coluna !=[]):\r\n", 212 | " year = coluna[0].text\r\n", 213 | " average = coluna[1].text\r\n", 214 | " open = coluna[2].text\r\n", 215 | " high = coluna[3].text\r\n", 216 | " low = coluna[4].text\r\n", 217 | " close = coluna[5].text\r\n", 218 | "\r\n", 219 | " table_data = table_data.append(\\\r\n", 220 | " {\"Ticker\": \"MSFT\" ,\\\r\n", 221 | " \"Year\" : year, \\\r\n", 222 | " \"Average\" : average, \\\r\n", 223 | " \"Open\" : open, \\\r\n", 224 | " \"High\" : high, \\\r\n", 225 | " \"Low\" : low, \\\r\n", 226 | " \"Close\" : close}, ignore_index=True)" 227 | ] 228 | }, 229 | { 230 | "cell_type": "markdown", 231 | "metadata": { 232 | "nteract": { 233 | "transient": { 234 | "deleting": false 235 | } 236 | } 237 | }, 238 | "source": [ 239 | "# Visualizar os Dados" 240 | ] 241 | }, 242 | { 243 | "cell_type": "code", 244 | "execution_count": 16, 245 | "outputs": [], 246 | "metadata": { 247 | "jupyter": { 248 | "source_hidden": false, 249 | "outputs_hidden": false 250 | }, 251 | "nteract": { 252 | "transient": { 253 | "deleting": false 254 | } 255 | } 256 | }, 257 | "source": [ 258 | "table_data" 259 | ] 260 | }, 261 | { 262 | "cell_type": "markdown", 263 | "metadata": { 264 | "nteract": { 265 | "transient": { 266 | "deleting": false 267 | } 268 | } 269 | }, 270 | "source": [ 271 | "# Salvar em Arquivo Parquet" 272 | ] 273 | }, 274 | { 275 | "cell_type": "code", 276 | "execution_count": 17, 277 | "outputs": [], 278 | "metadata": { 279 | "jupyter": { 280 | "source_hidden": false, 281 | "outputs_hidden": false 282 | }, 283 | "nteract": { 284 | "transient": { 285 | "deleting": false 286 | } 287 | } 288 | }, 289 | "source": [ 290 | "table_data.to_parquet('abfss://fabiomsfilesys@fabiomsdatalake.dfs.core.windows.net/webscraping/cotacoes.parquet')" 291 | ] 292 | } 293 | ] 294 | } -------------------------------------------------------------------------------- /05-obter-municipios-formato-parquet.pipeline.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "p02_IBGE_Municipios", 3 | "properties": { 4 | "activities": [ 5 | { 6 | "name": "Municipio", 7 | "type": "Copy", 8 | "dependsOn": [], 9 | "policy": { 10 | "timeout": "7.00:00:00", 11 | "retry": 0, 12 | "retryIntervalInSeconds": 30, 13 | "secureOutput": false, 14 | "secureInput": false 15 | }, 16 | "userProperties": [], 17 | "typeProperties": { 18 | "source": { 19 | "type": "JsonSource", 20 | "storeSettings": { 21 | "type": "HttpReadSettings", 22 | "requestMethod": "GET" 23 | }, 24 | "formatSettings": { 25 | "type": "JsonReadSettings" 26 | } 27 | }, 28 | "sink": { 29 | "type": "ParquetSink", 30 | "storeSettings": { 31 | "type": "AzureBlobFSWriteSettings" 32 | }, 33 | "formatSettings": { 34 | "type": "ParquetWriteSettings" 35 | } 36 | }, 37 | "enableStaging": false, 38 | "translator": { 39 | "type": "TabularTranslator", 40 | "mappings": [ 41 | { 42 | "source": { 43 | "path": "$['id']" 44 | }, 45 | "sink": { 46 | "name": "id", 47 | "type": "String" 48 | } 49 | }, 50 | { 51 | "source": { 52 | "path": "$['nome']" 53 | }, 54 | "sink": { 55 | "name": "nome", 56 | "type": "String" 57 | } 58 | }, 59 | { 60 | "source": { 61 | "path": "$['microrregiao']['id']" 62 | }, 63 | "sink": { 64 | "name": "microrregiao_id", 65 | "type": "String" 66 | } 67 | }, 68 | { 69 | "source": { 70 | "path": "$['microrregiao']['nome']" 71 | }, 72 | "sink": { 73 | "name": "microrregiao_nome", 74 | "type": "String" 75 | } 76 | }, 77 | { 78 | "source": { 79 | "path": "$['microrregiao']['mesorregiao']['id']" 80 | }, 81 | "sink": { 82 | "name": "microrregiao_mesorregiao_id", 83 | "type": "String" 84 | } 85 | }, 86 | { 87 | "source": { 88 | "path": "$['microrregiao']['mesorregiao']['nome']" 89 | }, 90 | "sink": { 91 | "name": "microrregiao_mesorregiao_nome", 92 | "type": "String" 93 | } 94 | }, 95 | { 96 | "source": { 97 | "path": "$['microrregiao']['mesorregiao']['UF']['id']" 98 | }, 99 | "sink": { 100 | "name": "microrregiao_mesorregiao_UF_id", 101 | "type": "String" 102 | } 103 | }, 104 | { 105 | "source": { 106 | "path": "$['microrregiao']['mesorregiao']['UF']['sigla']" 107 | }, 108 | "sink": { 109 | "name": "microrregiao_mesorregiao_UF_sigla", 110 | "type": "String" 111 | } 112 | }, 113 | { 114 | "source": { 115 | "path": "$['microrregiao']['mesorregiao']['UF']['nome']" 116 | }, 117 | "sink": { 118 | "name": "microrregiao_mesorregiao_UF_nome", 119 | "type": "String" 120 | } 121 | }, 122 | { 123 | "source": { 124 | "path": "$['microrregiao']['mesorregiao']['UF']['regiao']['id']" 125 | }, 126 | "sink": { 127 | "name": "microrregiao_mesorregiao_UF_regiao_id", 128 | "type": "String" 129 | } 130 | }, 131 | { 132 | "source": { 133 | "path": "$['microrregiao']['mesorregiao']['UF']['regiao']['sigla']" 134 | }, 135 | "sink": { 136 | "name": "microrregiao_mesorregiao_UF_regiao_sigla", 137 | "type": "String" 138 | } 139 | }, 140 | { 141 | "source": { 142 | "path": "$['microrregiao']['mesorregiao']['UF']['regiao']['nome']" 143 | }, 144 | "sink": { 145 | "name": "microrregiao_mesorregiao_UF_regiao_nome", 146 | "type": "String" 147 | } 148 | }, 149 | { 150 | "source": { 151 | "path": "$['regiao-imediata']['id']" 152 | }, 153 | "sink": { 154 | "name": "regiao-imediata_id", 155 | "type": "String" 156 | } 157 | }, 158 | { 159 | "source": { 160 | "path": "$['regiao-imediata']['nome']" 161 | }, 162 | "sink": { 163 | "name": "regiao-imediata_nome", 164 | "type": "String" 165 | } 166 | }, 167 | { 168 | "source": { 169 | "path": "$['regiao-imediata']['regiao-intermediaria']['id']" 170 | }, 171 | "sink": { 172 | "name": "regiao-imediata_regiao-intermediaria_id", 173 | "type": "String" 174 | } 175 | }, 176 | { 177 | "source": { 178 | "path": "$['regiao-imediata']['regiao-intermediaria']['nome']" 179 | }, 180 | "sink": { 181 | "name": "regiao-imediata_regiao-intermediaria_nome", 182 | "type": "String" 183 | } 184 | }, 185 | { 186 | "source": { 187 | "path": "$['regiao-imediata']['regiao-intermediaria']['UF']['id']" 188 | }, 189 | "sink": { 190 | "name": "regiao-imediata_regiao-intermediaria_UF_id", 191 | "type": "String" 192 | } 193 | }, 194 | { 195 | "source": { 196 | "path": "$['regiao-imediata']['regiao-intermediaria']['UF']['sigla']" 197 | }, 198 | "sink": { 199 | "name": "regiao-imediata_regiao-intermediaria_UF_sigla", 200 | "type": "String" 201 | } 202 | }, 203 | { 204 | "source": { 205 | "path": "$['regiao-imediata']['regiao-intermediaria']['UF']['nome']" 206 | }, 207 | "sink": { 208 | "name": "regiao-imediata_regiao-intermediaria_UF_nome", 209 | "type": "String" 210 | } 211 | }, 212 | { 213 | "source": { 214 | "path": "$['regiao-imediata']['regiao-intermediaria']['UF']['regiao']['id']" 215 | }, 216 | "sink": { 217 | "name": "regiao-imediata_regiao-intermediaria_UF_regiao_id", 218 | "type": "String" 219 | } 220 | }, 221 | { 222 | "source": { 223 | "path": "$['regiao-imediata']['regiao-intermediaria']['UF']['regiao']['sigla']" 224 | }, 225 | "sink": { 226 | "name": "regiao-imediata_regiao-intermediaria_UF_regiao_sigla", 227 | "type": "String" 228 | } 229 | }, 230 | { 231 | "source": { 232 | "path": "$['regiao-imediata']['regiao-intermediaria']['UF']['regiao']['nome']" 233 | }, 234 | "sink": { 235 | "name": "regiao-imediata_regiao-intermediaria_UF_regiao_nome", 236 | "type": "String" 237 | } 238 | } 239 | ] 240 | } 241 | }, 242 | "inputs": [ 243 | { 244 | "referenceName": "IBGEMunicipiosJson", 245 | "type": "DatasetReference" 246 | } 247 | ], 248 | "outputs": [ 249 | { 250 | "referenceName": "IBGEMunicipiosParquet", 251 | "type": "DatasetReference" 252 | } 253 | ] 254 | } 255 | ], 256 | "annotations": [], 257 | "lastPublishTime": "2022-04-23T16:25:42Z" 258 | }, 259 | "type": "Microsoft.Synapse/workspaces/pipelines" 260 | } -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | GNU GENERAL PUBLIC 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You may not convey a covered 525 | work if you are a party to an arrangement with a third party that is 526 | in the business of distributing software, under which you make payment 527 | to the third party based on the extent of your activity of conveying 528 | the work, and under which the third party grants, to any of the 529 | parties who would receive the covered work from you, a discriminatory 530 | patent license (a) in connection with copies of the covered work 531 | conveyed by you (or copies made from those copies), or (b) primarily 532 | for and in connection with specific products or compilations that 533 | contain the covered work, unless you entered into that arrangement, 534 | or that patent license was granted, prior to 28 March 2007. 535 | 536 | Nothing in this License shall be construed as excluding or limiting 537 | any implied license or other defenses to infringement that may 538 | otherwise be available to you under applicable patent law. 539 | 540 | 12. No Surrender of Others' Freedom. 541 | 542 | If conditions are imposed on you (whether by court order, agreement or 543 | otherwise) that contradict the conditions of this License, they do not 544 | excuse you from the conditions of this License. If you cannot convey a 545 | covered work so as to satisfy simultaneously your obligations under this 546 | License and any other pertinent obligations, then as a consequence you may 547 | not convey it at all. For example, if you agree to terms that obligate you 548 | to collect a royalty for further conveying from those to whom you convey 549 | the Program, the only way you could satisfy both those terms and this 550 | License would be to refrain entirely from conveying the Program. 551 | 552 | 13. Use with the GNU Affero General Public License. 553 | 554 | Notwithstanding any other provision of this License, you have 555 | permission to link or combine any covered work with a work licensed 556 | under version 3 of the GNU Affero General Public License into a single 557 | combined work, and to convey the resulting work. The terms of this 558 | License will continue to apply to the part which is the covered work, 559 | but the special requirements of the GNU Affero General Public License, 560 | section 13, concerning interaction through a network will apply to the 561 | combination as such. 562 | 563 | 14. Revised Versions of this License. 564 | 565 | The Free Software Foundation may publish revised and/or new versions of 566 | the GNU General Public License from time to time. Such new versions will 567 | be similar in spirit to the present version, but may differ in detail to 568 | address new problems or concerns. 569 | 570 | Each version is given a distinguishing version number. If the 571 | Program specifies that a certain numbered version of the GNU General 572 | Public License "or any later version" applies to it, you have the 573 | option of following the terms and conditions either of that numbered 574 | version or of any later version published by the Free Software 575 | Foundation. If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. Limitation of Liability. 601 | 602 | IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING 603 | WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS 604 | THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY 605 | GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE 606 | USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF 607 | DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD 608 | PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), 609 | EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF 610 | SUCH DAMAGES. 611 | 612 | 17. Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | 635 | Copyright (C) 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | Copyright (C) 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------