Virtual EventFree
November 26, 2026
Databases for Modern Applications
How modern applications use databases — data models, access patterns, connection management, and the architecture behind fast, scalable backends.
PythonJavaScriptSQLAlchemyasyncpgNode.jsPrismaDrizzleORMConnection PoolingHigh-Throughput
About This Event
A summit for engineers building the data layer of modern applications. Sessions cover how today's applications actually work with databases: choosing data models and access patterns that scale with your product, managing connections and pooling across cloud, containerized, and serverless environments, sidestepping the ORM pitfalls that quietly degrade performance, efficient bulk and batch operations, and the architectural decisions — caching, async I/O, read/write splitting — that separate fast backends from slow ones, whatever your language or stack.
Key Topics
Async I/O: asyncpg & SQLAlchemy (Python) vs Prisma & Drizzle (JS)
Connection pooling: PgBouncer, pgBouncer alternatives, and app-level pools
ORM pitfalls: N+1 queries, over-fetching, eager vs lazy loading
Bulk insert, upsert, and batch update patterns
Event loop blocking and async anti-patterns in Node.js
Query optimization and EXPLAIN ANALYZE for high-throughput workloads
Who Should Attend
Backend & platform engineers
AI/ML engineers building data-intensive products
Tech leads and heads of engineering
DevOps & SRE on data-heavy systems
Speakers
Luigi Nardi
Founder & CEO · DBtune
A Benchmark Study on the Impact of PostgreSQL Server Parameter Tuning
Autotuning PostgreSQL: A Deep Dive into Database Optimization with Machine Learning
Cloud DatabasesDevOps / SRE
Nikolai Iusiumbeli
Principal Software Engineer · 1Feed
Privacy-by-Design: Implementing Multi-Tenant Vector Isolation for AI Agents Using PostgreSQL RLS
AI InfrastructureCloud Databases
Satej Kumar Sahu
Principal Data Engineer · Zalando SE
The Checkpointing Crisis: Solving the ACID Bottleneck in Stateful AI Agent Architectures
From Spark to SQLite: A Pragmatic Migration Path for Agentic Workloads
Data EngineeringBig Data
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