Category: Learning Levels

In this post, we discuss an automotive manufacturing information management use case where we store information about components within a vehicle as well as the hierarchy between each of the components. For our automotive use case, we use Amazon DynamoDB to deliver transactional queries, such as component attribute lookups. We will also show you how to use DynamoDB for larger responses such as a recursive query for all the components in a vehicle. While recursive object relationships can be represented in graph databases and possibly traditional RDBMS (with complex joins), these deeper queries can also be represented in DynamoDB.

In this post, we demonstrate how to use the DBMS_CLOUD package to transfer files between S3 buckets and directories in an RDS Custom for Oracle database. We also show how you can access data from Amazon S3 directly using Oracle features such as external tables and hybrid partition tables. The features provided by DBMS_CLOUD could vary between different Oracle releases, so pay close attention to the steps in the post and make sure you reference DBMS_CLOUD in the Oracle Database 19c documentation. To avoid confusion, the option discussed in this post is for RDS Custom for Oracle, not for RDS for Oracle. RDS for Oracle offers S3 integration.

Cosmos is a decentralized network of interoperable blockchain networks that serves as an open and highly scalable environment on which to build blockchain applications. With effective support for cross-chain interaction between homogeneous and heterogeneous blockchains, Cosmos aims to extend interoperability to a broader landscape. In this post, we discuss the value and technical architecture of Cosmos and provide a detailed tutorial on the quick deployment of the Cosmos enterprise framework (IRITA) within the AWS environment.

This is a two-part series. In this post, we explain three archival solutions that allow you to archive Oracle data into Amazon Simple Storage Service (Amazon S3). In Part 2 of this series, we explain three archival solutions using native Oracle products and utilities. All of these options allow you to join current Oracle data with archived data.

This post is a continuation of Archival solutions for Oracle database workloads in AWS: Part 1. Part 1 explains three archival solutions that allow you to archive Oracle data into Amazon Simple Storage Service (Amazon S3). In this post, we explain three archival solutions using native Oracle products and utilities.

Amazon Timestream is a fast, scalable, and serverless time-series database service that makes it easier to store and analyze trillions of events per day. In this post, we guide you through the essential concepts of Timestream and demonstrate how to use them to make critical data modeling decisions. We walk you through how data modeling helps for query performance and cost-effective usage. We explore a practical example of modeling video streaming data, showcasing how these concepts are applied and the resulting benefits. Lastly, we provide more best practices that directly or indirectly relate to data modeling.

In this post, we introduce the key functionalities, architecture, and configurations of the AWS DMS diagnostic support AMI. Then, we show you how to launch the AMI with proper networking configurations and AWS Identity and Access Management (IAM) permissions using AWS CloudFormation. Last, we demonstrate an example of how network latency results in significant replication lag and how to use the AMI to diagnose the issue.

Amazon Neptune is a fully managed graph database service built for the cloud that makes it easier to build and run graph applications that work with highly connected datasets. Neptune provides built-in security, continuous backups, serverless compute, and integrations with other AWS services. Neptune supports in-place upgrades of cluster and database instances. Upgrade of a Neptune cluster can be done either manually or automatically (during the database maintenance window).

Amazon DocumentDB (with MongoDB compatibility) is a fast, reliable, and fully managed database service. Amazon DocumentDB makes it easy to set up, operate, and scale MongoDB API-compatible databases in the cloud. With Amazon DocumentDB, you can run the same application code and use the same drivers and tools that you use with MongoDB API. Performance Insights adds to the existing Amazon DocumentDB monitoring features to illustrate your cluster performance and help you analyze any issues that affect it. With the Performance Insights dashboard, you can visualize the database load and filter the load by waits, query statements, hosts, or application. Performance Insights is included with Amazon DocumentDB instances and stores seven days of performance history in a rolling window at no additional cost.

Amazon Relational Database Service (Amazon RDS) Custom for SQL Server allows you to use a custom engine version (CEV) by providing an Amazon Machine Image (AMI), which includes specific customizations and database media installed on it. In this post we provide you guidance and best practices to build, test, and distribute AMIs using an EC2 Image Builder pipeline.