AWS Database Blog
Exploring frequently asked questions with AWS Database Solutions Architects
July 2023: This post was reviewed for accuracy.
At Amazon, we listen to our customers and work backward from your needs. Our AWS Database Specialist Solutions Architect team helps you architect your database landscape. In this post, we address 11 frequently asked questions with videos from our Database Specialist Solutions Architects. Let’s begin the learning journey!
Migrating to managed database services
First, let’s look at questions that you have asked when migrating from self-managed databases to a managed database service like Amazon Relational Database Service (Amazon RDS).
1. What are the prerequisites, limitations, and process of deploying SSIS packages on Amazon RDS for SQL Server?
Amazon RDS for SQL Server started supporting SQL Server Integration Services (SSIS) directly on RDS instances in May 2020. In this video, we show you the process of deploying SSIS packages on an RDS for SQL Server instance. We also discuss the prerequisites and limitations for SSIS on Amazon RDS for SQL Server.
Amazon RDS Support for SQL Server Integration Services
2. What are the prerequisites, limitations, and process of deploying SSRS on Amazon RDS for SQL Server?
Amazon RDS for SQL Server started supporting SQL Server Reporting Services (SSRS) directly on RDS instances in May 2020. In this video, we show you how to deploy your SSRS reports on an RDS for SQL Server instance. We also discuss the prerequisites and limitations for SSRS on Amazon RDS for SQL Server.
Amazon RDS Support for SQL Server Reporting Services
3. What are the prerequisites, limitations, and process of deploying SSAS models on Amazon RDS for SQL Server?
Amazon RDS for SQL Server started supporting SQL Server Analysis Services (SSAS) directly on RDS instances in April 2020. In this video, we show you how to deploy your SSAS models on an RDS for SQL Server instance. We also discuss the prerequisites and limitations for SSAS on Amazon RDS for SQL Server.
Amazon RDS Support for SQL Server Analysis Services
4. How can I enable Active Directory authentication with Amazon RDS for Oracle?
You can streamline database identity management by sourcing and managing all your user identities from Active Directory (AD). This also enables you to reuse your existing AD security policies, such as password expiration, password history, and account lockout policies. In this video, we show you how to enable AD authentication with Amazon RDS for Oracle.
Integration of Amazon RDS Oracle with AWS Directory Service
5. What are the best practices to upgrade Amazon RDS for Oracle from 11.2.0.4 to 19c?
Oracle Corp. has announced the end of support for Oracle Database versions 11.2.0.4 on December 31, 2020, after which Oracle Support will no longer release Critical Patch Updates for these database versions. Amazon RDS for Oracle ended support for Oracle Database version 11.2.0.4 Standard Edition 1 (SE1) for license-included (LI) on October 31, 2020. For bring-your-own-license (BYOL), Amazon RDS for Oracle will end the support for Oracle Database version 11.2.0.4 for all editions on December 31, 2020. In this video, we talk about the best practices for a smooth upgrade to 19c.
Best Practices for Upgrading Amazon RDS for Oracle DB Instances From 11.2.0.4 to 19c
6. What are the prerequisites and limitations with Amazon RDS for SQL Server read replicas?
Amazon RDS for SQL Server started supporting in-Region read replicas in April 2020. In this video, you learn how to deploy up to five asynchronous SQL Server read replicas on Amazon RDS. We also discuss the prerequisites and limitations for deploying read replicas.
Creating Read Replicas in RDS SQL Server
7. How can I create read replicas with Amazon RDS for Oracle and promote them to be the primary in case of a disaster?
In this video, we show you how to create an Amazon RDS for Oracle read replica, alleviate the read workload from primary DB instances, scale out the total workload, and promote Amazon RDS for Oracle read replicas to be the new primary as a managed disaster recovery solution.
Advanced features and integration
The next set of questions are from customers who are exploring the advanced features of Amazon Aurora. These customers are either using Aurora with their applications or planning to migrate and looking at integrating with other services.
8. How do I use the native integration between Amazon Aurora and Amazon Comprehend to extract sentiments from my data?
Amazon Comprehend is a natural language processing (NLP) service that uses machine learning (ML) to find insights and relationships in text. In this video, you learn how to take existing data in your relational database, such as product reviews and blog post comments, and extract sentiments hidden in it. We use Aurora’s integration with Amazon Comprehend, which uses NLP, and demonstrate how to achieve this using simple SQL queries—no prior knowledge of ML required.
Sentiment Analysis using Aurora ML integration
9. How does Amazon Aurora recover from failures? What is the impact on the application?
In this video, we show you how high availability and automatic failure recovery work in practice with Aurora. We take an application and show failure scenarios and their impact on the application.
High Availability and Failover in Amazon Aurora
10. How does IAM integrate with Amazon Aurora MySQL?
Database administrators can associate database users with AWS Identity and Access Management (IAM) users and roles. With this authentication method, you don’t need to use a password when you connect to a DB cluster. Instead, you use an authentication token for greater security. Also, the network traffic to and from the database is encrypted using Secure Sockets Layer (SSL). In this video, you learn how to integrate IAM with Aurora MySQL.
IAM Authentication with Amazon Aurora MySQL
11. How can I integrate Amazon SageMaker with Amazon Aurora?
You can turn relational data into insights using Aurora and its integration with ML. In this video, you learn how to take existing data in your relational database and enrich it using Amazon SageMaker. We discuss how this integration works, the different prerequisites, and how to configure it. We also see a demo of churn predictions with the scikit-learn framework, utilizing the Random Forest classifier. All by running simple SQL queries!
Amazon Aurora Machine Learning – SageMaker Integration
Summary
This was just the beginning of our learning series with Database Solutions Architects. Our goal is to help you in your migration journey, and we will continue to explore common customer questions every quarter. Happy cloud computing!
About the Authors
Wendy Neu has worked as a Data Architect with AWS since January 2015. Prior to joining AWS, she worked as a consultant in Cincinnati, OH helping customers integrate and manage their data from different unrelated data sources.
Michael Stearns is a Senior Product Marketing Manager at AWS.
Saurabh Jain is a Senior Product Manager with the Relational Database Services (RDS) team at AWS.