The document discusses Amazon Web Services (AWS) Batch and how it can help customers run batch computing workloads on AWS. It notes that AWS Batch automatically provisions the optimal quantity and type of compute resources (e.g., EC2 instances) required to run jobs efficiently. It also allows customers to integrate their own scheduling and application code with AWS Batch through simple API calls or SDKs.
The document discusses various topics relating to web development including 12Factor methodology, continuous integration/delivery, setting up build environments using tools like Jenkins on Windows, Linux and MacOS, testing APIs with curl, building Docker images, and deploying applications to servers. It provides guidance and considerations for setting up development and deployment workflows and infrastructure for web applications.
This document summarizes a microservices meetup hosted by @mosa_siru. Key points include:
1. @mosa_siru is an engineer at DeNA and CTO of Gunosy.
2. The meetup covered Gunosy's architecture with over 45 GitHub repositories, 30 stacks, 10 Go APIs, and 10 Python batch processes using AWS services like Kinesis, Lambda, SQS and API Gateway.
3. Challenges discussed were managing 30 microservices, ensuring API latency below 50ms across availability zones, and handling 10 requests per second with nginx load balancing across 20 servers.
This document provides an overview and agenda for an AWS webinar on AWS Glue. It introduces AWS Glue as a fully managed and serverless ETL service that can manage metadata for various data sources. The webinar will cover the background of AWS Glue, its key features including being serverless and enabling secure development in notebooks, use cases, pricing, and a conclusion. It also provides details on the components and functions of AWS Glue like the data catalog, orchestration, and serverless engines.
This document discusses Amazon SageMaker, an AWS service that allows users to build, train, and deploy machine learning models. It provides an overview of SageMaker's key capabilities like the SageMaker SDK, hosted Jupyter notebooks, built-in algorithms, and integration with other AWS services. Examples of using SageMaker with frameworks like Chainer and TensorFlow are also presented.
This document discusses the need for a service mesh and introduces AWS App Mesh as a service mesh solution. It explains that as applications become more distributed, microservices-based, and utilize different technologies, a common way to handle communication between services is needed to ensure reliability, security, and observability across the system. A service mesh provides this by managing traffic at the infrastructure level rather than requiring each application to implement its own communication logic.
The document discusses Amazon Web Services (AWS) Batch and how it can help customers run batch computing workloads on AWS. It notes that AWS Batch automatically provisions the optimal quantity and type of compute resources (e.g., EC2 instances) required to run jobs efficiently. It also allows customers to integrate their own scheduling and application code with AWS Batch through simple API calls or SDKs.
The document discusses various topics relating to web development including 12Factor methodology, continuous integration/delivery, setting up build environments using tools like Jenkins on Windows, Linux and MacOS, testing APIs with curl, building Docker images, and deploying applications to servers. It provides guidance and considerations for setting up development and deployment workflows and infrastructure for web applications.
This document summarizes a microservices meetup hosted by @mosa_siru. Key points include:
1. @mosa_siru is an engineer at DeNA and CTO of Gunosy.
2. The meetup covered Gunosy's architecture with over 45 GitHub repositories, 30 stacks, 10 Go APIs, and 10 Python batch processes using AWS services like Kinesis, Lambda, SQS and API Gateway.
3. Challenges discussed were managing 30 microservices, ensuring API latency below 50ms across availability zones, and handling 10 requests per second with nginx load balancing across 20 servers.
This document provides an overview and agenda for an AWS webinar on AWS Glue. It introduces AWS Glue as a fully managed and serverless ETL service that can manage metadata for various data sources. The webinar will cover the background of AWS Glue, its key features including being serverless and enabling secure development in notebooks, use cases, pricing, and a conclusion. It also provides details on the components and functions of AWS Glue like the data catalog, orchestration, and serverless engines.
This document discusses Amazon SageMaker, an AWS service that allows users to build, train, and deploy machine learning models. It provides an overview of SageMaker's key capabilities like the SageMaker SDK, hosted Jupyter notebooks, built-in algorithms, and integration with other AWS services. Examples of using SageMaker with frameworks like Chainer and TensorFlow are also presented.
This document discusses the need for a service mesh and introduces AWS App Mesh as a service mesh solution. It explains that as applications become more distributed, microservices-based, and utilize different technologies, a common way to handle communication between services is needed to ensure reliability, security, and observability across the system. A service mesh provides this by managing traffic at the infrastructure level rather than requiring each application to implement its own communication logic.