Database administration
Installation, performance tuning, backup/recovery, and high availability.
Databases, pipelines, and what actually breaks when you run them for real.
I’ve spent more than five years keeping other people’s databases alive. Now I’m documenting the move into data engineering: production incidents, real benchmarks, and the parts nobody puts in a tutorial.
replication-check.sh
explain-analyze.sql
kafka-lag.sh
vacuum-status.sh
What we write about.
What’s coming
Data engineering
ETL/ELT pipelines, batch vs. streaming, data quality, and cloud platforms.
Data architecture
Data modeling, warehouse/lake/lakehouse design, and choosing the right database.
DevSecOps for data
CI/CD for schema migrations, infrastructure as code, and policy-as-code.
Data security
Encryption, access control, masking, auditing, and database hardening.
Data governance and data guardianship
Classification, lineage, stewardship, retention, and compliance.
Labs
Benchmarks with methodology, home-lab experiments, and real cost breakdowns.
Career and practice
Incident post-mortems, tools and checklists, and the DBA-to-data-engineer roadmap.
Frequently asked questions
When does the first post ship?
November 2026.
What topics will be covered?
Database administration
- Installation, upgrades and patching (Oracle, PostgreSQL, MySQL, SQL Server)
- Performance tuning: query optimization, indexing, execution plans, wait events
- Backup, recovery, and point-in-time restore
- High availability and disaster recovery: replication, clustering, Data Guard, Always On
- Monitoring, alerting and capacity planning
- Automating DBA tasks with SQL, Shell and Python
Data engineering
- ETL/ELT pipeline design and orchestration (Airflow, dbt)
- Batch vs. streaming (Kafka, Spark)
- Data quality checks and testing
- Cloud data platforms (Snowflake, BigQuery, Redshift, Databricks)
- Data migration strategies
Data architecture
- Data modeling: relational, dimensional, Data Vault
- Data warehouse, data lake and lakehouse design
- Data mesh and domain-oriented ownership
- Choosing the right database: OLTP vs. OLAP, SQL vs. NoSQL
- Scalability, partitioning and sharding
DevSecOps for data
- CI/CD for databases and schema migrations (Flyway, Liquibase)
- Infrastructure as Code (Terraform, Ansible)
- Containers and Kubernetes for data workloads
- Secrets management (Vault, cloud KMS)
- Security scanning and policy-as-code in pipelines
Data security
- Encryption at rest and in transit, TDE, key management
- Access control: least privilege, RBAC/ABAC, privileged access
- Data masking, tokenization and anonymization
- Auditing, activity monitoring and threat detection
- Database hardening and vulnerability management
Data governance and data guardianship
- Data classification and sensitive data (PII) discovery
- Data catalogs, lineage and metadata management
- Data ownership, stewardship and accountability
- Data retention, lifecycle and secure disposal
- Compliance: GDPR, India’s DPDP Act, HIPAA, PCI DSS, SOC 2, ISO 27001
Labs
- Benchmark methodology and reproducible test setups
- Home-lab experiments: hardware, configs, real numbers
- Cost breakdowns: self-hosted vs. managed
Career and practice
- Real incident post-mortems and lessons learned
- Tools, scripts and checklists
- Career paths across DBA, data engineering and data security