Este é o primeiro de uma série de três cursos sobre processamento de dados sem servidor com o Dataflow. Nele, vamos relembrar o que é o Apache Beam e qual é a relação entre ele e o Dataflow. Depois, falaremos sobre a visão do Apache Beam e os benefícios do framework de portabilidade desse modelo de programação. Com esse processo, o desenvolvedor pode usar a linguagem de programação favorita com o back-end de execução que quiser. Em seguida, mostraremos como o Dataflow permite a separação entre a computação e o armazenamento para economizar dinheiro. Além disso, você vai aprender como as ferramentas de identidade, acesso e gerenciamento interagem com os pipelines do Dataflow. Por fim, vamos ver como implementar o modelo de segurança ideal para seu caso de uso no Dataflow.
This course is part of the Serverless Data Processing Dataflow em Português Brasileiro Specialization
Serverless Data Processing with Dataflow: Foundations em Português Brasileiro
About this Course
What you will learn
Demonstrar como o Apache Beam e o Cloud Dataflow trabalham juntos para atender às necessidades de processamento de dados de sua organização
Resumir os benefícios do Beam Portability Framework e habilitá-lo para seus dutos Dataflow
Habilitar o Shuffle & Streaming Engine para pipelines batch & streaming respectivamente para o máximo desempenho
Permitir a Programação Flexível de Recursos para um desempenho mais eficiente em termos de custos
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
Syllabus - What you will learn from this course
Neste módulo, apresentamos os detalhes do curso, além de um resumo rápido sobre o modelo de programação Apache Beam e o serviço gerenciado do Dataflow do Google.
Portabilidade do Beam
Este módulo é dividido em quatro seções: portabilidade do Beam, Runner v2, ambientes de contêiner e transformações entre linguagens.
Como separar a programação do armazenamento com o Dataflow
Neste módulo, ensinaremos como separar a programação do armazenamento com o Dataflow. Ele é dividido em quatro seções: Dataflow, serviço do Dataflow Shuffle, Dataflow Streaming Engine e programação flexível de recursos.
IAM, cotas e permissões
Neste módulo, veremos os diferentes papeis, cotas e permissões do IAM necessárias para executar o Dataflow.
Neste módulo, veremos como implementar o modelo de segurança ideal para seu caso de uso no Dataflow.
Neste curso, começamos com um resumo sobre o que é o Apache Beam e qual é a relação dele com o Dataflow.
About the Serverless Data Processing Dataflow em Português Brasileiro Specialization
Está se tornando cada vez mais difícil manter uma pilha de tecnologia que possa acompanhar as crescentes demandas de um negócio orientado a dados. Todo praticante de Big Data está familiarizado com os três V’s do Big Data: volume, velocidade e variedade. E se houvesse uma tecnologia à prova de escala projetada para atender a essas demandas? Entre no Google Cloud Dataflow. O Google Cloud Dataflow simplifica o processamento de dados unificando o processamento em lote e fluxo e fornecendo uma experiência sem servidor que permite que os usuários se concentrem na análise, não na infraestrutura. Essa especialização destina-se a clientes e parceiros que desejam aprofundar sua compreensão do Dataflow para aprimorar seus aplicativos de processamento de dados. Esta especialização contém três cursos: Foundations, que explica como o Apache Beam e o Dataflow trabalham juntos para atender às suas necessidades de processamento de dados sem o risco de dependência do fornecedor Developing Pipelines, que aborda como você converte nossa lógica de negócios em aplicativos de processamento de dados que podem ser executados no Dataflow Operations, que analisa as lições mais importantes para operar um aplicativo de dados no Dataflow, incluindo monitoramento, solução de problemas, teste e confiabilidade.
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Last Updated: September 1, 2015
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