Database Development

Choosing the Right Database — when to pick a relational, document, search, vector, graph, key-value, wide-column, time-series or analytical store, how MongoDB and Couchbase differ in practice, and how to keep several databases consistent with CQRS.

Pagination: Offset vs. Keyset — why large OFFSET/SKIP values degrade query performance, the keyset (seek method) alternative, and how it maps onto SQL, MongoDB, Couchbase, Solr, Elasticsearch, GraphQL and Spring Data.

SQL Reference — a standard SQL reference covering DDL, DML, DCL, functions, and triggers/stored procedures, plus a downloadable one-page cheat sheet.

MongoDB Reference — the document database: the BSON document model, the Query API and aggregation, indexing and search, schema design, transactions and change streams, replication and sharding, storage and security, plus a downloadable one-page cheat sheet.

Couchbase Reference — the distributed JSON document and key-value database: buckets, scopes and collections, the key-value and sub-document APIs, SQL++ querying and transactions, Global Secondary Indexes, Full-Text and Vector Search, Analytics and Eventing, clustering, replication and XDCR, storage, security and the SDKs, plus a downloadable one-page cheat sheet.

Elasticsearch Reference — the distributed, Lucene-based search and analytics engine: the document and index model, mapping and text analysis, indexing and the Query DSL, relevance and aggregations, joins, geospatial and vector/semantic search, the distributed model, index lifecycle and scaling, performance tuning, administration, snapshots and security, plus a downloadable one-page cheat sheet.

Apache Solr Reference — the Lucene-based search server: cores and collections, schema and analysis, indexing and querying, the JSON Request/Facet APIs, relevance, SolrCloud, security, deployment and the SolrJ client, plus a downloadable one-page cheat sheet.

Apache Lucene Reference — the embedded Java search library that Elasticsearch, OpenSearch and Solr are built on: the segment/codec index model, the indexing chain, text analysis, the Query API and parsers, points and BKD range search, BM25 and custom scoring, collectors and concurrent search, kNN vector search, faceting, highlighting, suggesters, grouping and joins, near-real-time search and performance tuning, plus a downloadable one-page cheat sheet.

Neo4j Reference — the native graph database: the property graph model, Cypher fundamentals through advanced querying, data modeling, importing data, indexes and constraints, transactions and drivers, APOC, security and administration, clustering and multi-database, the Graph Data Science library (pathfinding, centrality, community detection, similarity, embeddings and ML pipelines), vector search and GraphRAG, worked use cases, and a comparison with relational and other graph databases, plus a downloadable one-page cheat sheet.

Evolving the Database Model — safe, consistent schema evolution with Liquibase, Mongock and Flamingock: changelogs, changesets, changeunits and changes, preconditions/contexts/labels, rollback, distributed locking, target systems, stages and pipelines, testing, and the expand/contract pattern.