DBaasNow
DATABASE DECISION GUIDE 2026
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Which Database Should
Your Organisation Actually Use?

500+ database management systems exist today. This guide covers all 20 DB-Engines categories, mapped to your use case, industry, key technical features, and AI strategy — then shows you how DBaasNow manages the ones you pick.

500+
Database systems tracked across all 20 DB-Engines categories
20
DB-Engines categories covered (21 total including NewSQL & In-Memory)
1
Control plane to manage any of them

✅ No single database wins every use case — but one platform can manage all of them.

Database Type Selector
Answer 4 questions in 60 seconds and get a personalised database category recommendation — including AI workload guidance.
Which database is right for your use case?
4 questions · 60 seconds · personalised recommendation
QUESTION 1 OF 4
RECOMMENDED FOR YOU
🆕 The DBaasNow Philosophy: Choose the Right Database. Let DBaasNow Manage It.
DBaasNow is not tied to any single database vendor or engine. Our orchestration and lifecycle management framework is built on a plug-and-play architecture — any database technology can be onboarded into DBaasNow's automated control plane. The engines we support today are chosen based on the highest enterprise consumption globally. As new engines rise in adoption, DBaasNow's framework is designed to absorb them without rebuilding the control plane from scratch. Your database selection is driven by your use case. DBaasNow's job is to automate everything that comes after that decision.
PROVISIONGOVERNMIGRATEAUTOMATEOBSERVE Any Engine → Any Cloud → Any Environment
Quick Decision Framework
Match your primary need to the right database category. Covers all 20 DB-Engines categories plus NewSQL and In-Memory — 21 categories total. Click any card to jump to the full details.
📋
Need
Need transactions, joins, structured data
→ Relational (SQL)
📄
Need
Need flexible schema, JSON, fast iteration
→ Document Database
⚡
Need
Need sub-millisecond speed, caching, sessions
→ Key-Value Store
📊
Need
Need petabyte-scale, high write throughput
→ Wide-Column Store
🕷
Need
Need relationships, fraud detection, recommendations
→ Graph Database
🔍
Need
Need full-text search, ranked relevance
→ Search Engine Database
📈
Need
Need metrics, monitoring, IoT trends over time
→ Time-Series Database
🤖
Need
Need AI embeddings, semantic search, RAG pipelines
→ Vector Database
🏭
Need
Need analytics, BI, data warehousing, OLAP
→ Columnar / OLAP
🗺
Need
Need location data, geofencing, mapping
→ Spatial / GeoSpatial
🌎
Need
Need SQL + global scale + ACID
→ NewSQL / Distributed SQL
🧠
Need
Need real-time computation, leaderboards, queues
→ In-Memory Database
📡
Need
Need event sourcing, audit trail, CQRS
→ Event Store
🔀
Need
Need multiple data models, one platform
→ Multi-Model Database
💻
Need
Need to store objects directly — no ORM, no table mapping
→ Object Oriented DBMS
🔗
Need
Need linked data, knowledge graphs, semantic reasoning
→ RDF Store
📄
Need
Need to store and query native XML documents
→ Native XML DBMS
🗒
Need
Maintaining legacy Pick / ERP / MultiValue systems
→ Multivalue DBMS
📦
Need
Need document management, versioning, digital asset storage
→ Content Store
📌
Need
Maintaining mainframe IMS or IDMS legacy systems
→ Navigational DBMS
📻
Need
Need unified metrics, logs, and traces for observability
→ Time Series, Observability
All 21 Database Categories
Covering all 20 DB-Engines categories plus NewSQL and In-Memory. Each card includes: use case, key technical features, specific use cases, industry adoption, AI/ML role, and top engines.
📋
Relational (SQL)
SQL
"The enterprise standard. Underpins 60%+ of all production workloads globally."
When to use
Structured data with clear relationships. ACID transactions, complex multi-table joins, schema enforcement. Any system where correctness is non-negotiable: financial ledgers, ERP, CRM, inventory, HR systems, regulatory reporting.
Key Technical Features
  • ACID transactions with full referential integrity and rollback
  • Complex multi-table joins with foreign key constraints and cascades
  • Schema enforcement — data validated at write time, not read time
Specific Use Cases
Core Banking Systems ERP & Inventory CRM Platforms Regulatory Reporting Order Management HR & Payroll Systems
Industry adoption
FinServ & Banking Healthcare & Pharma Insurance Government Retail & ERP Manufacturing
AI & data generation role
🤖 Primary source of structured training data. Relational databases feed ML feature stores, data warehouses, and LLM fine-tuning pipelines. In RAG architectures, structured metadata lives in a relational store alongside vector indexes. PostgreSQL + pgvector enables vector search without a separate system.
Top engines
PostgreSQL ✓ MariaDB ✓ Oracle (Roadmap) SQL Server (Roadmap) MySQL (Roadmap) IBM Db2 SAP HANA
✓ DBaasNow manages PostgreSQL and MariaDB today. Oracle, SQL Server, and MySQL onboarding is in progress — the control plane framework is engine-agnostic.
📄
Document Database
NoSQL
"Schema flexibility for applications where data structure evolves constantly."
When to use
Semi-structured, JSON-like data that varies by record. Content management, product catalogues, patient records, user profiles, event logs. Ideal when your schema changes frequently and SQL migrations are a bottleneck.
Key Technical Features
  • Flexible JSON-like documents with dynamic schemas — no migration needed
  • Aggregation frameworks for real-time analytics and data pipelines
  • Horizontal sharding and replica sets for linear horizontal scale
Specific Use Cases
Content Management Systems Product Catalogues IoT Data Storage Patient Records (EHR) Real-time Analytics Mobile App Backends
Industry adoption
Media & Publishing E-commerce Healthcare (EHR) Gaming SaaS Platforms Real Estate
AI & data generation role
🤖 AI content storage and output persistence. LLM-generated content (articles, product descriptions, AI chat history) is naturally JSON — document databases store it without schema migration. Also used for RAG document chunks, AI model metadata, and experiment results.
Top engines
MongoDB ✓ Firestore CouchDB Amazon DocumentDB Realm
✓ DBaasNow manages MongoDB today across AWS, Azure, GCP, and on-prem — full lifecycle including provisioning, patching, backup, failover, and observability.
⚡
Key-Value Store
NoSQL
"When speed is the only requirement."
When to use
Sub-millisecond read/write on simple lookups. Session tokens, shopping carts, leaderboards, feature flags, distributed locks, rate limiting. Not for complex queries — this is a pure speed-optimised lookup store.
Key Technical Features
  • In-memory storage for sub-millisecond read/write with no disk I/O
  • Rich data structures: strings, hashes, lists, sets, sorted sets, streams
  • Replication, clustering, and Sentinel/AOF persistence for durability
Specific Use Cases
Real-time Leaderboards Session Management Message Queuing Systems Rate Limiting Shopping Cart Cache Feature Flag Storage
Industry adoption
E-commerce (cart) Gaming (scores) AdTech (bidding) Fintech (rate limits) SaaS (sessions)
AI & data generation role
🤖 AI inference caching layer. LLM inference is expensive. Key-value stores cache repeated prompt-response pairs to reduce API costs 40–70%. Also used for online ML feature stores — real-time feature values fed to model inference at sub-millisecond latency.
Top engines
Redis Amazon DynamoDB Memcached etcd RocksDB Aerospike
⚠ Never use a key-value store as your primary database. No query language, no joins — always pair with a relational or document database as the source of truth.
📊
Wide-Column Store
NoSQL
"Planet-scale writes. No single point of failure."
When to use
Petabyte-scale write workloads, high availability across regions, time-series-like access patterns. IoT sensor telemetry, clickstream, call records at carrier scale, audit logs at financial scale.
Key Technical Features
  • Tunable consistency — trade consistency for availability per query at runtime
  • High write throughput via peer-to-peer architecture with no single master node
  • Sparse data support — columns only stored when values exist, saving storage at scale
Specific Use Cases
Sensor Data Collection Fraud Detection Systems Call Detail Records (CDR) Log Aggregation Financial Transaction Ledgers Social Media Activity Feeds
Industry adoption
Telco (CDRs) IoT & Utilities FinServ (tick data) Social Media Logistics
AI & data generation role
🤖 Petabyte-scale AI training data persistence. IoT sensor streams, telco CDRs, and clickstream — the raw material for predictive ML models — live here before processing into feature stores.
Top engines
Apache Cassandra Apache HBase ScyllaDB Google Bigtable DataStax Enterprise
⚠ No joins, no ACID across partitions. Requires upfront data modelling around query patterns. Schema mistakes are expensive to reverse at petabyte scale.
🕷
Graph Database
NoSQL
"When relationships between data are as important as the data itself."
When to use
Social networks, fraud detection, recommendation engines, knowledge graphs, identity and access management, supply chain networks. Any problem where traversing relationships (3+ hops) is core — SQL JOINs become unacceptably slow at this depth.
Key Technical Features
  • ACID transactions on graph operations with full commit and rollback
  • Graph traversal algorithms — shortest path, PageRank, community detection, centrality
  • Property graphs with typed nodes, directed relationships, and custom labels
Specific Use Cases
Fraud Detection Networks Knowledge Graphs Recommendation Engines Identity & Access Management Supply Chain Networks Social Network Analysis
Industry adoption
FinServ (fraud) Insurance (claims) Cybersecurity Healthcare (pathways) Retail (recommendations) Telecoms
AI & data generation role
🤖 Knowledge graph layer for LLM grounding. Graph databases power the knowledge graphs that prevent LLM hallucination by providing structured factual context. In FinServ, entity relationship graphs enable AI-driven fraud pattern detection.
Top engines
Neo4j ✓ Amazon Neptune ArangoDB TigerGraph JanusGraph
✓ DBaasNow manages Neo4j today — automated cluster management, Cypher-aware backup, failover, and cross-environment lifecycle management.
📈
Time-Series Database
Analytics
"When every data point has a timestamp and trends matter more than records."
When to use
Infrastructure monitoring, IoT sensor telemetry, financial tick data, application performance metrics. Data that arrives as a continuous stream of timestamped measurements queried by time range, aggregation, or anomaly detection.
Key Technical Features
  • Timestamped data model optimised for append-only sequential high-volume writes
  • Automatic data downsampling and configurable retention policy management
  • Range queries, roll-ups, and built-in anomaly detection functions
Specific Use Cases
Infrastructure Monitoring IoT Sensor Telemetry Financial Tick Data Predictive Maintenance Energy Grid Monitoring (SCADA) Application Performance (APM)
Industry adoption
Energy & Utilities (SCADA) Manufacturing (IIoT) FinServ (tick data) Healthcare (wearables) DevOps (APM)
AI & data generation role
🤖 Time-series AI and predictive maintenance. The primary data source for anomaly detection models, predictive maintenance ML, and forecasting algorithms. Industrial IoT sensor data feeds ML models that predict equipment failure before it happens.
Top engines
InfluxDB Prometheus TimescaleDB QuestDB Graphite Kdb+
⚠ TimescaleDB is a PostgreSQL extension — it gives you time-series capabilities without leaving the SQL ecosystem and without adding a new operational system to manage.
🤖
Vector Database
AI / ML
"The infrastructure layer for the AI era. Fastest-growing database category 2024–2026."
When to use
Storing and querying high-dimensional ML embeddings for semantic search, RAG pipelines, recommendation systems, image similarity, and LLM memory layers. When you need "find me things similar to this" rather than "find me things that exactly match this."
Key Technical Features
  • Approximate Nearest Neighbour (ANN) search on high-dimensional embedding vectors
  • Cosine, dot-product, and Euclidean distance similarity metrics per query
  • Hybrid search — dense vector + sparse keyword (BM25) in one unified query
Specific Use Cases
RAG Pipelines (LLM) Semantic Search Image & Video Similarity Product Recommendations Fraud Pattern Detection Clinical NLP Search
Industry adoption
AI Startups FinServ (document search) Healthcare (clinical NLP) Legal (contract analysis) Retail (visual search) Enterprise AI
AI & data generation role
🤖 THE core storage layer for every RAG and LLM application. When a user queries a chatbot, the system converts the query to a vector, searches the vector database for semantically similar content, and injects that context into the LLM prompt. Without a vector database, RAG does not function.
Top engines
Pinecone Milvus Qdrant Weaviate Chroma pgvector (PostgreSQL ext.)
⚠ For fewer than 100K vectors, pgvector (PostgreSQL extension) often outperforms standalone vector databases and eliminates a separate operational system. Evaluate before adopting a dedicated vector database.
🏭
Columnar / OLAP
Analytics
"Built for analytical reads across billions of rows — not transactional writes."
When to use
Data warehousing, business intelligence, complex aggregations at scale. When your workload is read-heavy, analytical, and needs fast column scans rather than row-level transactional updates.
Key Technical Features
  • Columnar storage — reads only the columns queried, not full rows
  • Vectorised query execution engine (Photon, ClickHouse) for 10× faster SQL analytics
  • Massively parallel processing (MPP) with auto-scaling compute separated from storage
Specific Use Cases
Data Warehousing Regulatory Capital Reports Sales & Revenue Analytics ML Feature Engineering Real-time Business Dashboards Ad-hoc BI Queries
Industry adoption
FinServ (regulatory) Retail (sales analytics) Healthcare (population health) Telco (network analytics) Government (census)
AI & data generation role
🤖 The analytical foundation for enterprise AI. ML feature engineering, model training data preparation, and AI performance analytics all run on OLAP systems. The Databricks Lakehouse Medallion architecture (Bronze/Silver/Gold) sits on top of columnar Delta Lake storage.
Top engines
Snowflake Databricks ClickHouse BigQuery Amazon Redshift DuckDB Apache Druid
⚠ OLAP databases are not designed for OLTP. Use a relational database as your operational store and feed the warehouse via ETL/ELT pipelines. Never write application transactions directly to a data warehouse.
🗺
Spatial / GeoSpatial
Special Purpose
"When your data has coordinates, standard indexes get slow fast."
When to use
Mapping, routing, proximity search, geofencing, location-based services. Finding "all branches within 5km" requires specialised spatial indexes. Also used in GIS, urban planning, logistics optimisation, and fleet management.
Key Technical Features
  • Spatial indexes (R-tree, GiST) for fast proximity, bounding-box, and polygon queries
  • Support for geometry types — points, polygons, linestrings, and multi-geometry collections
  • Geographic functions — ST_Distance, ST_Within, ST_Intersects, ST_Buffer, ST_Centroid
Specific Use Cases
Fleet & Delivery Routing Proximity Search ("near me") Geofencing Alerts Risk Zone Mapping Land & Property Registry Autonomous Vehicle Navigation
Industry adoption
Logistics & Delivery Retail (location) Insurance (risk mapping) Government (GIS) Real Estate Agriculture
AI & data generation role
🤖 Geospatial AI and autonomous systems. Self-driving vehicles, drone delivery routing, and AI-powered logistics optimisation all depend on spatial databases for real-time geofencing and route computation. Computer vision models use spatial databases to tag training patches by geography.
Top engines
PostGIS (PostgreSQL ext.) ✓ SpatiaLite MongoDB (geo) Elasticsearch (geo)
✓ PostGIS runs as a PostgreSQL extension — DBaasNow manages PostgreSQL including PostGIS configurations, spatial indexes, and extension lifecycle.
🌎
NewSQL / Distributed SQL
SQL
"SQL semantics at global scale without sacrificing ACID."
When to use
You need full SQL with ACID guarantees at a scale that traditional RDBMS cannot handle — millions of transactions per second across regions. Global FinTech, multi-region SaaS, e-commerce at extreme scale.
Key Technical Features
  • Distributed ACID transactions across multiple nodes and regions simultaneously
  • PostgreSQL-compatible SQL interface — no application rewrite required for migration
  • Automatic sharding, rebalancing, and failover with zero manual DBA intervention
Specific Use Cases
Global Payment Processing Multi-region SaaS Platforms Real-time Fraud Scoring Instant Credit Decisioning High-scale E-commerce Gaming Live Operations
Industry adoption
Global FinTech Multi-region SaaS E-commerce (peak traffic) Gaming (live ops)
AI & data generation role
🤖 Global transactional AI applications. AI-powered financial products (real-time fraud scoring, instant credit decisioning) operating globally need ACID transactions across regions. NewSQL serves as the transactional backbone where consistency is legally required.
Top engines
CockroachDB TiDB Google Spanner YugabyteDB PlanetScale
⚠ Significant operational complexity. Evaluate a well-tuned PostgreSQL setup with read replicas first — most teams find it handles far more scale than expected before needing distributed SQL.
🧠
In-Memory Database
NoSQL
"When microseconds matter and disk I/O is too slow."
When to use
Real-time computation that cannot tolerate disk latency: live leaderboards, real-time bidding, financial risk calculation, multiplayer game state, pub/sub messaging. Data with a short TTL that is frequently recomputed.
Key Technical Features
  • All data stored in RAM — microsecond latency with zero disk I/O overhead
  • Pub/Sub messaging, Lua scripting, and atomic operations for complex real-time logic
  • Optional persistence modes: RDB snapshots and AOF append-only logging for durability
Specific Use Cases
Real-time Leaderboards Live Bidding Systems Gaming State Management ML Online Feature Store LLM Response Caching Real-time Risk Calculation
Industry adoption
AdTech (bidding) Gaming (state) FinServ (risk calc) Telco (signalling) Streaming platforms
AI & data generation role
🤖 Real-time AI inference serving layer. Online feature stores for ML models — the real-time features that feed model inference at request time — are almost always backed by an in-memory database for sub-millisecond latency.
Top engines
Redis Memcached Hazelcast Apache Ignite VoltDB
⚠ In-memory databases lose data on restart without persistence configuration. Never use as the sole copy of important data without a durable backup strategy.
📡
Event Store / Streaming
Special Purpose
"Every state change is a first-class record you can replay and audit."
When to use
Event sourcing and CQRS architectures where you store the history of state changes rather than current state. Immutable audit logs for compliance. Microservice event-driven communication. Financial transaction ledgers.
Key Technical Features
  • Immutable append-only event log — every write is a timestamped, permanent fact
  • Event replay — reconstruct any past system state from the event history at any point
  • Projections and subscriptions for real-time event stream processing and notifications
Specific Use Cases
Financial Audit Trails Order Lifecycle Tracking HIPAA Activity Logs CQRS Architecture Real-time Anomaly Detection AI Explainability Records
Industry adoption
FinServ (audit) Insurance (claims trail) Healthcare (HIPAA audit) Retail (order events) Manufacturing (change log)
AI & data generation role
🤖 AI training data with full provenance. Event streams provide immutable timestamped records — the highest-quality training data for sequential ML models. Real-time event streams power anomaly detection AI. Also provides complete audit trail for AI explainability requirements.
Top engines
EventStoreDB Apache Kafka Amazon Kinesis Apache Flink Apache Pulsar
⚠ Event sourcing requires full architectural commitment from day one. It is not a drop-in replacement for a relational database. Ensure your team understands the pattern before adopting.
🔀
Multi-Model Database
Polyglot
"One engine, multiple data models — and the tradeoffs that come with it."
When to use
You need two or more data models (document + graph, key-value + document) without operating separate systems. Good fit for teams with limited DBA capacity needing versatility over peak performance.
Key Technical Features
  • Single engine supporting document, key-value, and graph models simultaneously
  • AQL / SQL-like unified query language across all supported data models
  • Unified transactions across model types — no cross-engine consistency issues
Specific Use Cases
Startup MVP (pre-workload clarity) Content + Social Graph Hybrid Azure-native AI Apps (CosmosDB) Digital Health Records PropTech Platforms SME Unified Data Layer
Industry adoption
SaaS Startups Enterprise (Azure shops) Digital Health PropTech SME IT teams
AI & data generation role
🤖 Unified data layer for small AI teams. Startups building AI products without dedicated database engineers benefit from a single multi-model system. CosmosDB integrates natively with Azure OpenAI — a common choice for Microsoft-ecosystem AI applications.
Top engines
ArangoDB Microsoft CosmosDB Couchbase OrientDB
⚠ Multi-model databases are typically "good enough" at each model rather than best-in-class. For high-performance graph workloads, Neo4j outperforms. For high-performance document workloads, MongoDB outperforms. Choose deliberately.
💻
Object Oriented DBMS
Niche
"Store objects directly — no mapping, no translation, no impedance mismatch."
When to use
Your application is built in an object-oriented language (Java, C#, Python) and you need to persist complex objects with inheritance, relationships, and behaviour intact — without mapping to tables. Most common in engineering, CAD, telecommunications, and scientific applications where relational mapping would destroy data fidelity.
Key Technical Features
  • Objects stored with full class hierarchy, inheritance, and relationships preserved
  • No object-relational mapping (ORM) layer required — eliminates impedance mismatch
  • Native query languages (OQL) operate directly on object graphs
  • Supports complex nested structures and binary data natively
Specific Use Cases
CAD and Engineering Design Systems Scientific Data (Physics, Biology) Telecommunications Network Models Healthcare Object Persistence
Industry adoption
Engineering Telecommunications Life Sciences Government
AI & data generation role
🤖 Limited direct AI adoption. Object databases are rarely chosen for AI workloads. However, they remain relevant in domains where AI models must interact with complex engineering or scientific objects that cannot be flattened into rows without data loss.
Top engines  — 22 systems ranked on DB-Engines
InterSystems IRIS InterSystems Caché ObjectDB Db4o Objectivity/DB
⚠ Object databases are a niche and declining category. For most enterprise use cases, document databases (MongoDB) or relational databases with ORM layers (PostgreSQL + Hibernate) offer better tooling, community support, and hiring pools. Choose OODBMS only when object fidelity is a genuine architectural requirement.
🔗
RDF Stores
Semantic
"Link data across systems using a universal, standards-based graph model."
When to use
You need to store, query, and reason over linked data using semantic web standards (RDF, SPARQL, OWL). Common in knowledge graphs for government, life sciences, publishing, and any domain requiring interoperability across data sources that use different schemas and vocabularies.
Key Technical Features
  • Data stored as subject-predicate-object triples — the universal linked data format
  • SPARQL query language for graph pattern matching across triple stores
  • OWL and RDFS support for ontology-based reasoning and inference
  • W3C standards-compliant — enables federated queries across external RDF sources
Specific Use Cases
Government Linked Open Data Life Sciences Knowledge Graphs Publishing and Cultural Heritage Enterprise Knowledge Management
Industry adoption
Government Life Sciences Academic Research Publishing
AI & data generation role
🤖 Growing role in knowledge graph AI. RDF stores underpin many enterprise knowledge graphs that feed large language models with structured, verifiable facts. SPARQL endpoints serve as structured retrieval layers for RAG (Retrieval Augmented Generation) architectures in regulated industries.
Top engines  — 21 systems ranked on DB-Engines
Virtuoso GraphDB Stardog Apache Jena TDB
⚠ RDF stores are specialist technology requiring familiarity with semantic web concepts, SPARQL, and ontology design. For general graph use cases (social networks, fraud detection, recommendations), property graph databases like Neo4j offer better developer experience and broader ecosystem support.
📄
Native XML DBMS
Legacy
"Store and query XML documents natively — without converting to another format."
When to use
You have large volumes of XML documents that must be stored, queried, and retrieved with full structural fidelity using XPath and XQuery — without converting to relational tables or JSON. Most relevant for government document archives, legal content management, and legacy enterprise content systems that are XML-native by design.
Key Technical Features
  • Native XML storage — documents stored and retrieved without schema decomposition
  • XQuery and XPath as the primary query languages
  • Full-text search over XML content with structural awareness
  • Versioning and transaction support for document collections
Specific Use Cases
Government Document Archives Legal and Regulatory Content Publishing and Digital Libraries Healthcare Clinical Documents (HL7 CDA)
Industry adoption
Government Legal Publishing Healthcare
AI & data generation role
🤖 Minimal AI adoption. Native XML DBMS is a declining category — most new workloads choose document databases (MongoDB) which handle semi-structured data with better tooling, horizontal scalability, and modern AI integrations. XML databases remain in production primarily for legacy system maintenance.
Top engines  — 7 systems ranked on DB-Engines
BaseX MarkLogic eXist-db
⚠ This is a declining category. DB-Engines tracks only 7 systems. For new projects, consider document databases (MongoDB, Couchbase) or relational databases with native XML support (PostgreSQL, Oracle). XML DBMS is most relevant when you are maintaining existing XML-native systems rather than starting fresh.
🗒
Multivalue DBMS
Legacy
"Store multiple values in a single field — the original NoSQL, from the 1960s."
When to use
You are maintaining or modernising a legacy ERP, point-of-sale, or business application built on a Multivalue or Pick-based platform. Multivalue databases allow fields to contain lists of values and nested sub-records, eliminating the need for join tables in certain data models. Almost exclusively a legacy system maintenance choice rather than a greenfield decision.
Key Technical Features
  • Fields can contain multiple values — lists and nested sub-records within a single record
  • Hierarchical data model derived from the Pick operating system (1960s)
  • Proprietary query languages (RetrieVe, UniQuery) for record retrieval
  • BASIC-derived programming languages (UniVerse BASIC, UniBasic) for business logic
Specific Use Cases
Legacy ERP and Business Applications Retail Point-of-Sale Systems Distribution and Logistics Platforms Vertical Software ISVs (legacy)
Industry adoption
Retail Distribution Healthcare (legacy) Manufacturing
AI & data generation role
🤖 No meaningful AI adoption. Multivalue databases are not chosen for AI workloads. If you are operating a Multivalue system and need AI capabilities, the standard approach is to extract data into a modern database layer (PostgreSQL, MongoDB) for AI processing while keeping the Multivalue system for legacy operations.
Top engines  — 10 systems ranked on DB-Engines
UniData / UniVerse (Rocket U2) jBASE D3 (Rocket Software)
⚠ Multivalue databases are a legacy category with no meaningful new adoption. DB-Engines tracks only 10 systems. If you are evaluating this category, you are almost certainly maintaining an existing system. Migration to modern relational or document databases is the long-term path for most organisations.
📦
Content Stores
Content
"Manage digital content with metadata, versioning, and full-text search built in."
When to use
You need to store, manage, and retrieve digital content — documents, images, videos — with rich metadata, hierarchical organisation, versioning, access control, and full-text search as first-class features. Content stores implement the Java Content Repository (JCR) standard and are used as the data layer for content management platforms across enterprise and government environments.
Key Technical Features
  • JCR-compliant hierarchical content repository (JSR-170, JSR-283)
  • Built-in versioning, access control, locking, and observation
  • Full-text search via Lucene-based indexing across all content types
  • Structured and unstructured content coexist in a single repository
Specific Use Cases
Enterprise Content Management Digital Asset Management Government Document Repositories Healthcare Document Archives Legal Records Management
Industry adoption
Government Healthcare Legal Enterprise IT Publishing
AI & data generation role
🤖 Emerging AI integration. Content stores are increasingly used as document sources for RAG (Retrieval Augmented Generation) pipelines. Enterprise AI systems that answer questions from policy documents, legal contracts, or clinical records rely on content repositories as structured, version-controlled document backends.
Top engines  — 2 systems ranked on DB-Engines
Apache Jackrabbit Alfresco
⚠ This is a small category — DB-Engines currently tracks only 2 systems. Many modern content management needs are now addressed by object storage (S3), document databases (MongoDB), or enterprise platforms that bundle their own content layer. Evaluate whether a dedicated content store or a modern document database better fits your use case.
🔗
Navigational DBMS
Legacy
"Hierarchical and network databases — the original enterprise data systems, still running today."
When to use
You are maintaining mission-critical mainframe systems that were built on hierarchical (IMS) or network (IDMS) database architectures. Navigational databases access data through linked record chains rather than SQL queries. They remain in production at major banks, insurance companies, and government agencies — not by choice, but because migrating decades of mission-critical business logic carries extreme risk and cost.
Key Technical Features
  • Hierarchical model (IMS): data accessed via parent-child tree traversal
  • Network model (IDMS): data accessed via owner-member linked set relationships
  • Extremely high throughput — IMS processes billions of transactions daily at major banks
  • Tight IBM mainframe integration — z/OS, CICS, and batch processing environments
Specific Use Cases
Core Banking Transaction Processing Insurance Policy Administration Government Benefits and Social Security Airline Reservation Systems (legacy)
Industry adoption
Banking Insurance Government Healthcare (legacy)
AI & data generation role
🤖 Data source for AI — not an AI platform. Navigational databases are never chosen for AI workloads directly. However, the transaction data they hold is highly valuable for AI models in banking and insurance. The standard approach is to extract and replicate navigational data to modern analytical platforms (Snowflake, Databricks) for AI processing.
Top engines  — 2 systems ranked on DB-Engines
IBM IMS IDMS (Broadcom)
⚠ DB-Engines tracks only 2 systems in this category. This is not a category you choose for new systems — it is a category you inherit. If you are encountering navigational databases, you are working on mainframe modernisation, data extraction, or legacy system integration. Modern alternatives exist for all use cases, but migration timelines are measured in years, not months.
📊
Time Series, Observability
Emerging
"A new DB-Engines category for observability platforms that combine metrics, logs, and traces."
When to use
You are building a full-stack observability platform that goes beyond pure time series metrics — combining metrics, logs, traces, and profiling in a unified data store. This category represents the convergence of time series data management with the three pillars of observability: metrics (Prometheus-compatible), logs (Loki-compatible), and traces (OpenTelemetry-compatible).
Key Technical Features
  • Unified storage for metrics, logs, and traces — no separate backends required
  • OpenTelemetry-native ingestion — vendor-neutral instrumentation standard
  • High-cardinality label support for Kubernetes, microservices, and cloud environments
  • Prometheus-compatible query layer (PromQL) for metrics
Specific Use Cases
Cloud-Native Application Monitoring Kubernetes Infrastructure Observability SRE and Platform Engineering Database Performance Monitoring
Industry adoption
Technology Financial Services E-Commerce SaaS
AI & data generation role
🤖 AI-powered anomaly detection. Observability platforms increasingly integrate AI for automated anomaly detection, root cause analysis, and alert correlation — reducing mean time to resolution (MTTR) without requiring manual investigation. OpenTelemetry is becoming the standard instrumentation layer for AI workload monitoring in cloud-native environments.
Top engines  — New DB-Engines category — 1 system currently ranked
Grafana LGTM Stack (Loki, Grafana, Tempo, Mimir) Prometheus VictoriaMetrics OpenTelemetry Collector
⚠ This is a newly established DB-Engines category and currently tracks only 1 system. The observability tooling landscape (Grafana, Prometheus, VictoriaMetrics, OpenTelemetry) is maturing rapidly. For DBaasNow specifically: Grafana and Prometheus are already integrated into the DBaasNow platform for database monitoring dashboards.
Once you choose the right database — DBaasNow manages it.
DBaasNow is not a database. It is the control plane that sits above all your databases — regardless of engine, cloud, or environment. Our plug-and-play orchestration framework is designed to onboard any database technology into automated lifecycle management. The engines listed below represent the highest-consumption database technologies in enterprise environments today.
What DBaasNow does
After you select the right database category, DBaasNow automates everything that follows: provisioning, governance policies, patching, backup, zero-downtime migrations, and unified observability — across any cloud, any environment, for any supported engine.
How the framework works
The DBaasNow control plane is engine-agnostic by design. Each database engine is an adapter that plugs into the orchestration layer. When your organisation adopts a new database technology, DBaasNow can absorb it without replacing the platform.
Engines currently supported & on roadmap — chosen by highest enterprise consumption
PostgreSQL
Now
Relational · Spatial
🔌 Live on DBaasNow
MongoDB
Now
Document · Multi-model
🔌 Live on DBaasNow
Neo4j
Now
Graph Database
🔌 Live on DBaasNow
MariaDB
Coming Soon
Relational (SQL)
🔌 Adapter in progress
MySQL
Roadmap
Relational (SQL)
🔌 H2 2026
Oracle
Roadmap
Relational · Multi-model
🔌 H2 2026
SQL Server
Roadmap
Relational · Multi-model
🔌 H2 2026
Assess Your Database Maturity ↗
The Maturity Scorecard opens in a new tab. It assesses how well your current database estate is being operated — a natural next step after choosing which database categories belong in your architecture.
Disclaimer
This Database Decision Guide is provided by DBaasNow for general informational and educational purposes only. The information contained in this guide does not constitute professional technical, legal, or business advice. Database selection decisions should be made in consultation with qualified database architects, engineers, and advisors who have full knowledge of your specific organisational requirements, regulatory obligations, and technical environment.

DBaasNow makes no representations or warranties, express or implied, regarding the accuracy, completeness, fitness for purpose, or suitability of the information provided herein for any specific use case, workload, or organisation. Database technology capabilities, market rankings, and vendor offerings change frequently; information in this guide reflects publicly available data as of April 2026 and may not reflect subsequent developments.

Database engine popularity data is sourced from DB-Engines (db-engines.com), a third-party ranking service. DBaasNow has no affiliation with DB-Engines and does not warrant the accuracy of third-party data. Reference to any specific database product, vendor, or technology does not constitute an endorsement by DBaasNow.

DBaasNow engine availability timelines are subject to change. Contact jana@dbaasnow.com for current availability and roadmap information.

Content last reviewed: April 2026. Engine rankings and database counts are updated quarterly. Database technology evolves rapidly — if you notice an omission or update, contact jana@dbaasnow.com.

© 2026 DBaasNow. All rights reserved. This document may not be reproduced or distributed for commercial purposes without prior written consent of DBaasNow.
DBaasNow · Database Decision Guide 2026 · dbaasnow.com · jana@dbaasnow.com
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