Customers needing to keep an Amazon Relational Database Service (Amazon RDS) instance stopped for more than 7 days, look for ways to efficiently re-stop the database after being automatically started by Amazon RDS. If the database is started and there is no mechanism to stop it; customers start to pay for the instance’s hourly cost. Moreover, customers with database licensing agreements could incur penalties for running beyond their licensed cores/users. Stopping and starting a DB instance is faster than creating a DB snapshot, and then restoring the snapshot. However, if you plan to keep the Amazon RDS instance stopped for an extended period of time,
This post is written by Kinnar Sen, Senior Solutions Architect, EC2 Spot Apache Spark is an open-source, distributed processing system used for big data workloads. It provides API operations to perform multiple tasks such as streaming, extract transform load (ETL), query, machine learning (ML), and graph processing. Spark supports four different types of cluster managers (Spark standalone, Apache Mesos, Hadoop YARN, and Kubernetes), which are responsible for scheduling and allocation of resources in the cluster. Spark can run with native Kubernetes support since 2018 (Spark 2.3). AWS customers that have already chosen Kubernetes as their container orchestration tool can also choose to run Spark applications in Kubernetes, increasing
This post is courtesy of Tarun Kumar Mall, SDE at AWS. This post shows how to set up a multi-stage pipeline on a Jenkins host for a serverless application, using the AWS Serverless Application Model (AWS SAM). Overview This tutorial uses Jenkins Pipeline plugin. A commit to the main branch of the repository starts and deploys the application, using the AWS SAM CLI. This tutorial deploys a small serverless API application called HelloWorldApi. The pipeline consists of stages to build and deploy the application. Jenkins first ensures that the build environment is set up and installs any necessary tools. Next, Jenkins prepares the build artifacts.
This post was co-written by Anandprasanna Gaitonde, AWS Solutions Architect and John Bickle, Senior Technical Account Manager, AWS Enterprise Support Introduction Many AWS customers have internal business applications spread over multiple AWS accounts and on-premises to support different business units. In such environments, you may find a consistent view of DNS records and domain names between on-premises and different AWS accounts useful. Route 53 Private Hosted Zones (PHZs) and Resolver endpoints on AWS create an architecture best practice for centralized DNS in hybrid cloud environment. Your business units can use flexibility and autonomy to manage the hosted zones for their applications and support multi-region application
This post is courtesy of Markus Ziller, Solutions Architect. Today, git is a de facto standard for version control in modern software engineering. The workflows enabled by git’s branching capabilities are a major reason for this. However, with git’s distributed nature, it can be difficult to reliably remove changes that have been committed from all copies of the repository. This is problematic when secrets such as API keys have been accidentally committed into version control. The longer it takes to identify and remove secrets from git, the more likely that the secret has been checked out by another user. This post shows a solution that
This post is authored by Mike Burbey, Sr. Outposts SA AWS Outposts is a fully managed service that offers the same AWS infrastructure, AWS services, APIs, and tools to any data center, colocation space, or on-premises facility for a consistent hybrid experience. AWS Outposts is ideal for workloads that require low latency, access to on-premises systems, local data processing, data residency, and migration of applications with local system interdependencies. As part of the AWS Shared Responsibility Model, customers are responsible for capacity planning while using AWS Outposts. Customers must forecast compute and storage needs in addition to data center space, power, and HVAC requirements along
Amazon FSx provides AWS customers with the native compatibility of third-party file systems with feature sets for workloads such as Windows-based storage, high performance computing (HPC), machine learning, and electronic design automation (EDA). Amazon FSx automates the time-consuming administration tasks such as hardware provisioning, software configuration, patching, and backups. Since Amazon FSx integrates the file systems with cloud-native AWS services, this makes them even more useful for a broader set of workloads. Amazon FSx for Windows File Server provides fully managed file storage that is accessible over the industry-standard Server Message Block (SMB) protocol. Built on Windows Server, Amazon FSx delivers a wide range of