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

Delivers optimized vector search configuration with recall benchmarks, latency tuning, and hybrid search design.

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

Delivers a vector database architecture with index selection, embedding strategy, and scaling plan.

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

Delivers optimized time-series query patterns with aggregation, windowing, and analytical function examples.

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

Produces a time-series database architecture with schema design, retention policies, and downsampling strategy.

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

Delivers optimized graph query patterns with traversal strategies, execution profiling, and performance benchmarks.

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

Produces a graph data model with node/relationship design, property strategy, and query pattern optimization.

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

Delivers a DynamoDB cost optimization plan with specific savings opportunities and implementation priorities.

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

Delivers a DynamoDB migration plan with dual-write strategy, data validation, and cutover procedures.

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

Delivers advanced DynamoDB implementation patterns with code examples, trade-off analysis, and scaling guidance.

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

Delivers a DynamoDB capacity plan with mode selection, auto-scaling configuration, and cost optimization strategy.

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

Produces a DynamoDB data model with partition key design, GSI definitions, and access pattern coverage matrix.

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

Delivers an Elasticsearch relevance tuning strategy with scoring customization, testing methodology, and iteration process.

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

Delivers an optimized Elasticsearch ingestion architecture with pipeline design, throughput tuning, and error handling.

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

Delivers an Elasticsearch cluster architecture with node sizing, role assignment, and capacity planning.

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

Delivers optimized Elasticsearch query patterns with scoring strategy, filter caching, and performance profiling.

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

Produces an Elasticsearch index design with mappings, shard sizing, ILM policies, and template configuration.

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

Delivers a MongoDB sharding architecture with shard key analysis, topology design, and operational procedures.

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

Produces optimized MongoDB aggregation pipelines with stage ordering, index utilization, and performance benchmarks.

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

Delivers a MongoDB indexing strategy with specific index definitions, performance validation, and maintenance plan.

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

Produces a MongoDB schema design with document structure, relationship patterns, and migration strategy.