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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.
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The Training TimescaleDB - Optimizing Time-Series Data at Scale 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 TimescaleDB - Optimizing Time-Series Data at Scale 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 TimescaleDB - Optimizing Time-Series Data at Scale 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.
Advanced installation and configuration of TimescaleDB on PostgreSQL. Creation of multi-dimensional hypertables. Management of chunks and automatic partitioning. Hands-on practice with concrete IoT cases. Professional exercises: ingestion of 1M points/hour. Configuration of optimized time-series schemas. Start of the capstone project: enterprise monitoring database. Initial benchmarks to validate scalability. Integration with Kafka for continuous streams. Resolution of common production pitfalls. (112 words)
Implementation of columnar compression to reduce storage x10. Continuous aggregates for real-time dashboards. Automated retention policies and downsampling. Exercises on complex queries: time-series window functions. Expert index optimization and vacuuming. Real cases: industrial monitoring and finance. Progress on capstone project: aggregates and compression applied. Integration with Grafana and Prometheus. High-availability load testing. Best practices for distributed clusters. Advanced scheduler job management. (118 words)
High-availability deployment with Patroni and replication. Monitoring with TimescaleDB tools and pgBadger. Kernel and PostgreSQL tuning for extreme performance. Advanced security: time-based row-level security. Final exercises: failover and recovery. Completion of capstone project: full deployment in simulated production. Performance audit and final optimization. CI/CD integration with Docker and Kubernetes. DevOps best practices for TimescaleDB in enterprise. Presentation and expert quiz. Multi-node and cloud perspectives. (109 words)
Target audience
Data engineers, DevOps engineers, backend developers, and BI analysts managing IoT or time-series data in enterprise environments for expert-level skills
Prerequisites
Advanced proficiency in PostgreSQL, SQL optimization, Linux cluster administration, and management of large data volumes
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