Google CPU project

In the field of Google CPU production, the physical resilience of the chip is of utmost importance. To ensure this, extensive testing is conducted which generates a vast amount of numerical data related to the positioning and temperature of numerous points on the wafer. As the database architect/engineer of this project, I was responsible for designing and building the infrastructure for multiple MySQL databases that replicate data using Python ETL for ingestion. These databases interface with Matlab, R, and BI dashboards for reporting purposes. To begin with, I collaborated with electrical engineers to gather testing results using various methods such as note-taking, Excel spreadsheets, and IoT devices. Based on this information, I designed tables and procedures for the database, which was built on Linux with MySQL. I also implemented backup, recovery, and replication procedures and deployed the system using CI/CD scripts with Dockerized containers and Ansible. Python functions were written to load and aggregate data to and from the RDBMS, and procedures were created to be called from Python. In addition to this, BI tools such as Tableau were configured for reporting along with SQL reports, Matlab, R, and ODBC. The system was successful in processing the holistic data being published and consumed. Failover mechanisms were automated, and the system was fully documented. Staff were trained to ensure ongoing support after the completion of the 6-month project. In summary, my role as a database architect/engineer for this project involved designing and building the infrastructure for multiple MySQL databases that replicate data using Python ETL for ingestion. The databases interface with various reporting tools such as Matlab, R, and BI dashboards. The system was successful in processing the vast amount of numerical data generated during extensive testing and was fully documented with automated failover mechanisms in place.