Acts as a Senior Database Engineer / DBA with 10+ years of experience managing production databases at scale. Use proactively when designing database schemas, selecting databases, configuring replication, tuning performance, planning migrations, setting up monitoring, hardening security, troubleshooting slow queries, capacity planning, or implementing database infrastructure as code. Covers 230+ databases across relational, document, key-value, columnar, graph, time-series, NewSQL, vector, streaming, OLAP, embedded, multi-model, serverless, and search categories.

Acts as a Senior Database Engineer / DBA with 10+ years of experience managing production databases at scale. Use proactively when designing database schemas, selecting databases, configuring replication, tuning performance, planning migrations, setting up monitoring, hardening security, troubleshooting slow queries, capacity planning, or implementing database infrastructure as code. Covers 230+ databases across relational, document, key-value, columnar, graph, time-series, NewSQL, vector, streaming, OLAP, embedded, multi-model, serverless, and search categories.

Backup strategies and disaster recovery across all database engines. Full, incremental, differential backups. PITR (Point-in-Time Recovery). PostgreSQL (pg_dump, pgBackRest, Barman, WAL archiving), MySQL (mysqldump, XtraBackup, Clone Plugin), MongoDB (mongodump, Atlas backup), Redis (RDB, AOF). RPO/RTO planning, backup verification, cloud-native backup. Use when designing backup strategies, implementing disaster recovery, or troubleshooting data recovery.

Database capacity and growth planning. Storage growth estimation, IOPS requirements, memory sizing (buffer pool, shared_buffers), connection count estimation, sharding triggers, read replica scaling, cost estimation per cloud provider. Load testing (pgbench, sysbench, YCSB, HammerDB). When to shard vs scale up vs read replicas. Use when planning database capacity, sizing infrastructure, or evaluating scaling strategies.

Deep operational guide for 12 columnar/wide-column databases. Apache Cassandra (compaction, consistency, SAI, nodetool), ScyllaDB (shard-per-core, Alternator), HBase, Bigtable, ClickHouse (MergeTree, materialized views), Druid, StarRocks, Kudu, MonetDB, Vertica, Pinot. Use when configuring, tuning, or operating columnar databases for analytics or high-write workloads.

Connection pooling and management across all engines. PgBouncer (transaction/session/statement mode), pgcat (Rust-based, load balancing, sharding), ProxySQL (MySQL query routing, caching), HikariCP (Java), application-level pooling (Prisma, SQLAlchemy, GORM, database/sql). Serverless connection strategies (Prisma Accelerate, RDS Proxy, Neon pooler). Connection limits, pool sizing, leak detection. Use when configuring connection pools, optimizing connection usage, or troubleshooting connection issues.

Data modeling methodologies and patterns across paradigms. Entity-Relationship modeling (Chen, Crow's Foot, UML), document modeling (embedding vs referencing, polymorphic, bucket, outlier patterns), graph modeling (labeled property graph, RDF), time-series modeling, event sourcing, dimensional modeling (star/snowflake schema, SCD), Data Vault (hubs, links, satellites), polyglot persistence. Use when designing data models, choosing between modeling approaches, or mapping domain models to database schemas.

Deep operational guide for 14 data warehouse/OLAP databases. Snowflake (warehouses, clustering, Snowpark, cost), BigQuery (slots, BQML, BI Engine), Databricks (Delta Lake, Unity Catalog, Photon), Redshift (distribution, Spectrum, Serverless), DuckDB (in-process, Parquet), Trino, Hive, Doris, Firebolt. Use when implementing data warehouses, analytics pipelines, or OLAP workloads.

Infrastructure-as-code and automation for databases. Terraform modules (RDS, Cloud SQL, Atlas, Azure Database), Kubernetes operators (CloudNativePG, Percona, CrunchyData PGO, Vitess, MongoDB, Redis), Helm charts, GitOps for database config. Schema migration in CI/CD pipelines. Database testing in CI (Testcontainers, docker-compose). Chaos engineering for databases. Use when automating database provisioning, integrating databases into CI/CD, or managing database infrastructure as code.

Schema and data migration patterns across all engines. Migration tools (Flyway, Liquibase, Alembic, Prisma Migrate, Atlas, golang-migrate, Knex, Ecto, EF Core, Diesel, ActiveRecord). Zero-downtime migration (expand-contract, blue-green, shadow writes). Cross-engine migration (MySQL→PG, Oracle→PG, MongoDB→PG). CDC-based migration (Debezium, DMS). Use when planning schema changes, migrating between databases, or implementing zero-downtime deployments.

Database monitoring, alerting, and observability across all engines. PostgreSQL (pg_stat_statements, pg_stat_activity, pg_stat_user_tables), MySQL (Performance Schema, sys schema, slow query log), MongoDB (mongostat, mongotop, profiler), Redis (INFO, SLOWLOG, LATENCY). Monitoring tools (Datadog, PMM, pganalyze, Grafana). Key metrics, alert thresholds, dashboard templates. Use when setting up database monitoring, troubleshooting performance, or configuring alerting.

Security hardening across all database engines. Authentication (SCRAM, certificate, LDAP, Kerberos), authorization (RBAC, RLS, column-level), encryption at rest and in transit (TDE, TLS/SSL), audit logging (pgAudit, MySQL audit, MongoDB audit), SQL injection prevention, network security, data masking, compliance (PCI DSS, HIPAA, GDPR, SOX). Use when hardening database security, implementing access controls, or meeting compliance requirements.

Deep operational guide for 12 document databases. MongoDB (aggregation pipeline, sharding, Atlas, CSFLE, Vector Search), Elasticsearch/OpenSearch (ILM, mapping, query DSL, tiering), CouchDB (multi-master, PouchDB), Couchbase (N1QL, XDCR, Capella), RavenDB, DocumentDB, Cosmos DB, Firestore, FerretDB. Use when configuring, tuning, or operating document databases in production.

Deep operational guide for 12 embedded databases. SQLite (PRAGMA, WAL, FTS5, multi-threaded), RocksDB (LSM-tree, compaction, column families), LevelDB, LMDB, BoltDB/bbolt, BadgerDB, Realm, ObjectBox, libSQL, H2, HSQLDB. Use when selecting or configuring embedded databases for mobile apps, desktop applications, edge computing, or as storage engines.

Deep operational guide for 12 graph databases. Neo4j (Cypher, APOC, GDS, Aura, vector indexes), Neptune (Gremlin/SPARQL), Dgraph (DQL/GraphQL), JanusGraph, TigerGraph (GSQL), Memgraph, TypeDB, Apache AGE, NebulaGraph, Blazegraph, Stardog. Use when implementing graph data models, knowledge graphs, recommendation engines, or fraud detection.

Deep operational guide for 15 key-value stores. Redis/Valkey (cluster, Sentinel, Streams, Lua, Stack modules), DynamoDB (single-table design, GSI/LSI, DAX, Global Tables), Memcached, etcd (Raft, K8s), FoundationDB, KeyDB, Dragonfly, Ignite, Hazelcast, Aerospike, Garnet. Use when configuring, tuning, or operating key-value databases in production.

Deep operational guide for 8 multi-model databases. ArangoDB (AQL, SmartGraphs, Foxx), SurrealDB (SurrealQL, LIVE SELECT), FaunaDB (FQL v10, distributed ACID), Cosmos DB (5 consistency levels, multi-API), OrientDB, MarkLogic, InterSystems IRIS. Use when a single database must support document, graph, key-value, or relational access patterns simultaneously.

Deep operational guide for 12 NewSQL/distributed SQL databases. CockroachDB (multi-region, geo-partitioning, CDC), YugabyteDB (YSQL/YCQL, DocDB, xCluster), TiDB (TiKV/TiFlash HTAP), Spanner (TrueTime), Vitess (sharding, VSchema), PlanetScale, Citus, SingleStore, OceanBase. Use when implementing globally distributed SQL, horizontal scaling, or HTAP workloads.

Database performance troubleshooting across all engines. PostgreSQL (pg_stat_statements, auto_explain, pg_locks, EXPLAIN ANALYZE BUFFERS), MySQL (EXPLAIN FORMAT=TREE, Performance Schema, InnoDB status, optimizer trace), MongoDB (explain executionStats, currentOp, profiler), Redis (SLOWLOG, LATENCY, MEMORY DOCTOR, bigkeys). Lock contention, deadlocks, I/O bottlenecks, memory pressure, benchmarking (pgbench, sysbench, YCSB). Use when troubleshooting slow queries, diagnosing lock contention, or investigating database performance issues.

Cross-engine query optimization guide. EXPLAIN/EXPLAIN ANALYZE across PostgreSQL, MySQL, MongoDB, Cassandra, ClickHouse. Index strategies (B-tree, hash, GIN, GiST, BRIN, partial, covering, composite). N+1 detection, cursor-based pagination, materialized views, query plan analysis, slow query diagnosis. Use when optimizing slow queries, designing indexes, or analyzing query performance.

Deep operational guide for 20 relational/SQL databases. PostgreSQL tuning (VACUUM, WAL, partitioning, extensions, PgBouncer), MySQL/MariaDB (InnoDB, Vitess, Galera, ProxySQL), Oracle (RAC, Data Guard), MS SQL Server (AlwaysOn AG, columnstore), SQLite, Db2, HANA, and managed cloud options (Aurora, AlloyDB, Azure SQL, Neon, Supabase). Use when configuring, tuning, operating, or troubleshooting relational databases in production.

High availability and replication topologies across all database engines. Synchronous/asynchronous/semi-synchronous replication, primary-secondary, multi-primary, quorum-based. PostgreSQL streaming/logical replication, MySQL GTID/Group Replication, MongoDB replica sets, Redis Sentinel/Cluster, Cassandra multi-DC. Consensus protocols (Raft, Paxos, Gossip). Failover automation, split-brain prevention. Use when designing HA architectures, configuring replication, or troubleshooting failover.

Database schema design principles and patterns. Normalization (1NF through 5NF/BCNF), strategic denormalization for read performance, multi-tenancy schema patterns (shared schema, schema-per-tenant, database-per-tenant), primary key strategies (UUID v7, ULID, KSUID, Snowflake ID, BIGSERIAL, CUID2, NanoID), soft delete patterns, temporal tables (SCD Type 1/2/3/4/6), audit trails, schema evolution (expand-contract pattern), naming conventions, data type best practices, and anti-patterns to avoid (EAV, polymorphic associations, god tables). Use when designing new schemas, reviewing existing schema design, planning schema migrations, or choosing PK strategies.

Deep operational guide for 10 search engines. Solr (SolrCloud, analyzers), Typesense (typo-tolerant, instant), Meilisearch (AI-powered search), Algolia (hosted, A/B testing), Zinc, Manticore Search, Sonic, plus cross-references to Elasticsearch, OpenSearch, Atlas Search. Use when implementing full-text search, autocomplete, faceted navigation, or search-as-a-service.

Deep operational guide for 10 serverless databases. Neon (branching, scale-to-zero), Turso (edge SQLite, embedded replicas), Supabase (PG + Auth + Realtime), Cloudflare D1 (edge SQLite), Xata, Upstash (serverless Redis/Kafka), Aurora Serverless v2, Cosmos DB Serverless. Use when implementing pay-per-use database architectures, edge computing, or development workflows with branching.

Deep operational guide for 14 streaming databases and platforms. Kafka (KRaft, Streams, Connect, Schema Registry), Pulsar (multi-tenant, geo-replication), Redpanda (Kafka-compatible, no JVM), NATS/JetStream, Flink (streaming SQL), Materialize, RisingWave, Kinesis, Event Hubs, Pub/Sub, EventStoreDB. Use when implementing event streaming, CDC, real-time analytics, or event sourcing.

Deep operational guide for 14 time-series databases. InfluxDB (Flux, 3.0 Arrow/Parquet, Telegraf), Prometheus (PromQL, Thanos/Mimir), TimescaleDB (hypertables, continuous aggregates), QuestDB, VictoriaMetrics, TDengine, IoTDB, Graphite, KDB+, OpenTSDB, M3DB, CrateDB, Timestream, GridDB. Use when designing time-series storage for metrics, IoT, financial data, or observability.

Deep operational guide for 16 vector databases. Pinecone (serverless, hybrid search), Weaviate (vectorizers, generative search), Milvus/Zilliz (GPU, index types), Qdrant (quantization, filtering), ChromaDB, pgvector (HNSW/IVFFlat), LanceDB, Vespa, Marqo, Turbopuffer. Use when implementing semantic search, RAG pipelines, recommendation engines, or AI/ML embedding storage.