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Founded by passionate advocates of learning and innovation, Learni set out to make professional training accessible to everyone, everywhere in the world. Our team works in the largest cities such as Paris, Lyon, Marseille, and internationally, to support talents and organizations in their skills development.
10 spots per session maximum — 10 already taken
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30 free minutes with a training advisor — no commitment.
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Don't let this gap widen
Without mastering Amazon Aurora, database teams suffer frequent outages and performance bottlenecks, undermining application reliability and user trust.
Unoptimized instances drive up AWS costs by 40-60% annually—averaging $100K+ in excess spend—while a single hour of downtime costs enterprises $9,000 per minute in lost revenue and productivity.
80% of cloud database incidents trace back to misconfigurations or scaling failures, jeopardizing business continuity, scalability goals, and your role as a DBA or DevOps leader.
Delaying expertise invites escalating risks as workloads grow—act before the next breach hits.
The Training: Mastering Amazon Aurora - Cloud Database Management and Optimization training is delivered in-person or remotely (blended-learning, e-learning, virtual classroom, remote in-person). At Learni, a Qualiopi-certified training organization, each program is designed to maximize skills acquisition, regardless of the training mode chosen.
The trainer alternates between demonstrative, interrogative, and active methods (through practical exercises and/or real-world scenarios). This pedagogical approach ensures concrete and directly applicable learning in the workplace.
To ensure the quality of the Training: Mastering Amazon Aurora - Cloud Database Management and Optimization training, Learni provides the following teaching resources:
For in-house training at a location external to Learni, the client ensures and commits to having all necessary teaching materials (IT equipment, internet connection...) for the proper conduct of the training action in accordance with the prerequisites indicated in the communicated training program.
The assessment of skills acquired during the Training: Mastering Amazon Aurora - Cloud Database Management and Optimization training is carried out through:
Learni is committed to the accessibility of its professional training programs. All our training programs are accessible to people with disabilities. Our teams are available to adapt teaching methods to your specific needs. Do not hesitate to contact us for any accommodation request.
Learni training programs are available for inter-company and intra-company settings, both in-person and remote. Registration is possible up to 48 business hours before the start of training. Our programs are eligible for OPCO, Pôle emploi, and FNE-Formation funding. Contact us to discuss your training project and funding possibilities.
Presentation of the Aurora database, architecture and core concepts, MySQL/PostgreSQL differences, creation and configuration of an Aurora cluster, management of user accounts and access, introduction to the AWS RDS console, exploration of the CLI/AWS SDK interfaces.
Management of automatic backups, manual snapshots, and restorations, implementation of network security (security groups, VPC) and encryption, IAM access management, CloudWatch alarms configuration, scheduling of automatic maintenance and zero-downtime update practices, introduction to automation with AWS Lambda and CloudFormation.
Performance analysis and optimization (parameters, indexes, slow queries), management of cache and buffer pool, load balancing with Aurora endpoints, use of Aurora Serverless and Global Databases, implementation of high availability and cross-region replication, disaster recovery scenarios, continuous monitoring and incident management, diagnostic methodology, best practices for cloud-native architecture.
Target audience
Database administrators, DevOps engineers, developers seeking to optimize database management on AWS
Prerequisites
Mastery of relational databases, fundamental AWS cloud knowledge
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