# `PARAMETER::` Solution 1: Multiple Databases in the Cloud with ETL Tasks
+ Obsoleted (not real-time) data for analytical tasks
+ Hard to upgrade the Database Management System (DBMS)
+ Not real Online Data Definition Language (DDL) operations

![`smart`](./student-guide/diagram/CaseStudyGamingCompanyBGen1_aa_001_20230328.png)
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Multiple separate Amazon Aurora RDS instances are utilized for conducting transactional queries, whereas a single Amazon Redshift database is utilized for performing analytical queries. 

As the data volume increases, when the data volume of a single table increased to 100 million rows, maintenance became difficult. 

The challenges included:
- Additional ETL jobs are needed, and Amazon Redshift is not effective in handling data updates in real-time. Due to the increase in data volume, the solution was unable to meet the real-time advertising requirements.
- Updating and maintaining Aurora RDS is hard. The ops team could only do maintenance in the middle of the night. 
- Online Data Definition Language (DDL) operations with Aurora are inefficient. Online DDL with Aurora usually took hours and sometimes failed due to lack of resources.