Prompts

Columnar Database Design

Design columnar database architectures with ClickHouse, DuckDB, or Redshift for analytics and OLAP workloads.

SQLite in Production

Deploy SQLite in production with WAL mode, connection handling, backup strategies, and scaling patterns.

Multi-Model Database Strategy

Design multi-model database architectures combining relational, document, graph, and search in unified or polyglot setups.

Vector DB for RAG Systems

Design vector storage for RAG systems with chunking, embedding, retrieval strategies, and context window management.

Vector Search Optimization

Optimize vector search with index tuning, quantization, filtering strategies, and hybrid search for recall and speed.

Vector Database Architecture

Design vector database architecture with Pinecone, Weaviate, pgvector, or Qdrant for similarity search workloads.

Time-Series Query Patterns

Design efficient time-series queries for aggregation, anomaly detection, forecasting, and cross-series correlation.

Time-Series Database Design

Design time-series data storage with InfluxDB, TimescaleDB, or Prometheus for metrics, IoT, and event data.

Graph Query Optimization

Optimize graph queries in Cypher, Gremlin, or SPARQL with traversal strategies, profiling, and index utilization.

Graph Database Data Modeling

Design graph database models with nodes, relationships, and properties for connected data using Neo4j or similar.

DynamoDB Cost Optimization

Optimize DynamoDB costs with capacity mode tuning, GSI reduction, TTL, caching, and storage class selection.

DynamoDB Migration Strategy

Plan migrations to or from DynamoDB with zero-downtime data transfer, schema evolution, and validation procedures.

DynamoDB Advanced Patterns

Implement DynamoDB advanced patterns including adjacency lists, write sharding, and change data capture with Streams.

DynamoDB Capacity Planning

Plan DynamoDB capacity with on-demand, provisioned, and auto-scaling modes for cost-effective throughput management.

DynamoDB Data Modeling

Design DynamoDB single-table and multi-table data models with access pattern-driven key design and GSI strategy.

Elasticsearch Relevance Tuning

Tune Elasticsearch search relevance with BM25 scoring, boosting, function scores, and relevance testing frameworks.

Elasticsearch Data Ingestion

Design Elasticsearch ingestion pipelines with bulk indexing, ingest nodes, and Logstash for high-throughput data loading.

Elasticsearch Cluster Architecture

Design Elasticsearch cluster topology with node roles, shard allocation, and scaling for production workloads.

Elasticsearch Query Optimization

Optimize Elasticsearch queries with proper query context, filters, aggregations, and profile API analysis.

Elasticsearch Index Design

Design Elasticsearch indexes with optimal mappings, sharding, and lifecycle policies for search and analytics.

MongoDB Sharding Design

Design MongoDB sharding with shard key selection, chunk balancing, and zone configuration for horizontal scaling.

MongoDB Aggregation Pipeline

Design MongoDB aggregation pipelines with optimized stage ordering, expressions, and performance techniques.

MongoDB Index Strategy

Design MongoDB indexing with compound, multikey, text, and wildcard indexes for optimal query performance.

MongoDB Schema Design Patterns

Design MongoDB schemas using embedding, referencing, and polymorphic patterns for document-oriented data modeling.