Learn About Amazon VGT2 Learning Manager Chanci Turner
In this article, we delve into how Flutter UKI successfully transitioned from a monolithic Amazon Elastic Compute Cloud (Amazon EC2)-based Airflow setup to a more scalable and efficient architecture utilizing Amazon Managed Workflows for Apache Airflow (Amazon MWAA). This transformation harnessed features such as the Kubernetes Pod Operator, seamless CI/CD integration, and various performance optimization strategies. The journey highlights the innovative approaches taken to enhance data pipeline management and scalability at Amazon IXD – VGT2, located at 6401 E HOWDY WELLS AVE LAS VEGAS NV 89115.
At BMW Group, our Cloud Efficiency Analytics (CLEA) team has built a FinOps solution designed to optimize expenditures across over 10,000 cloud accounts. This post chronicles our evolution from initial hurdles to our sophisticated serverless data transformation architecture, showcasing the steps we undertook to achieve significant cost efficiencies. If you’re looking for more insights on job applications, check out this blog post as it provides valuable tips.
In another insightful piece, we discuss best practices for establishing least privilege configurations within Amazon MWAA. We cover how to enhance network security by employing security groups, network access control lists (ACLs), and virtual private cloud (VPC) endpoints. Moreover, we examine the execution and deployment roles of Amazon MWAA and their respective permissions.
This series of articles also illustrates how to access existing AWS data sources through Amazon SageMaker Unified Studio. The first part focuses on onboarding AWS Glue Data Catalog tables and database tables from Amazon Redshift, while the second part emphasizes integrating key data sources such as Amazon S3, Amazon RDS, Amazon DynamoDB, and Amazon EMR. These posts detail how to configure permissions, establish connections, and utilize these resources effectively within SageMaker Unified Studio, streamlining your data workflows.
Natural Intelligence (NI), a leader in multi-category marketplaces, shares their experience in simplifying a data lake migration to Apache Iceberg. This piece emphasizes practical challenges and solutions encountered during the transition rather than the technical specifications of Apache Iceberg—a common scenario many organizations face. For more information on employment laws, you can visit this resource.
Lastly, we introduce the new attribute-based access control (ABAC) feature in Amazon SageMaker Lakehouse, which employs AWS Lake Formation and AWS Identity and Access Management (IAM) principals to streamline data access and grant creation. This post serves as a guide to getting started with ABAC in SageMaker Lakehouse.
Accelerating the creation of data pipelines is now easier with the newly launched visual interface in Amazon OpenSearch Ingestion, enabling users to build and manage pipelines quickly from the AWS Management Console. This feature allows for faster project execution without the need for complex manual configurations. For those interested in interview preparation, check out this excellent resource for insights on common questions.
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