Data Modeling Best Practices for Scalable Applications
Early data modeling decisions compound over an application’s lifecycle. Here’s practical guidance on normalization, primary keys, soft deletes, and modeling for change.
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Early data modeling decisions compound over an application’s lifecycle. Here’s practical guidance on normalization, primary keys, soft deletes, and modeling for change.
Time-series data has distinct characteristics that general-purpose databases struggle with at scale. Here’s when purpose-built time-series databases are worth adopting.
ACID and BASE represent different consistency philosophies for databases. Here’s what each guarantees, where each fits, and how hybrid approaches combine them.
Database replication solves single points of failure and read scaling, but involves real trade-offs. Here’s how primary-replica, multi-primary, and failover strategies compare.
Data needs protection both at rest and in transit — a complete security posture requires deliberate attention to both. Here’s a practical guide to encryption and key management.
CI/CD pipelines have become a high-value target for supply chain attacks. Here’s how to secure dependencies, credentials, and build artifacts against them.
Ransomware defense spans secure coding, infrastructure hardening, and tested recovery procedures. Here’s a practical strategy for development teams.
Penetration testing simulates real attacks to find vulnerabilities before attackers do. Here’s what it is, when you need it, and how to choose a provider.
HTTP security headers are a high-value, low-effort security improvement. Here’s what CSP, HSTS, and other key headers actually protect against.
XSS remains one of the most common web vulnerabilities despite being well understood for decades. Here’s how to prevent it with encoding, CSP, and proper sanitization.