🗄️ DATABASES & MESSAGING

Couche données

Managed PostgreSQL, Redis-compatible cache, distributed KV store, Kafka streaming, and multi-model databases.

MOTEURS DE BASE DE DONNÉES

PostgreSQL

Relational · CloudNativePG

HA clustering, automated failover, PITR, connection pooling

Utilisé par : Keycloak, Backstage, Matomo, Harbor, Supabase

Dragonfly

Key-Value (Redis) · Dragonfly Operator

Redis-compatible, superior performance, modern algorithms

Utilisé par : Caching, sessions, rate-limiting

TiKV

Distributed KV · TiDB Operator

ACID transactions, Raft consensus, horizontal scaling

Utilisé par : SurrealDB backend storage

SurrealDB

Multi-Model · Native

Document + graph + KV, SQL-like queries, TiKV backend

Utilisé par : Applications needing flexible data models

Qdrant

Vector · Native

Similarity search, high-dimensional indexing, AI embeddings

Utilisé par : Semantic search, recommendation, AI/ML workloads

Apache Kafka

Streaming · Strimzi Operator

Event streaming, CRD-based management, TLS + SASL auth

Utilisé par : Async communication, event-driven architecture

Tous les composants

CloudNativePG

production

Kubernetes operator for PostgreSQL with HA clustering, automated failover, and point-in-time recovery.

Rôle : Manages PostgreSQL clusters for 5+ applications (Keycloak, Backstage, Matomo, etc.)

Dragonfly

production

Redis-compatible in-memory data store with superior performance through modern algorithms.

Rôle : High-performance caching layer replacing Redis

Strimzi (Apache Kafka)

production

Kubernetes operator for Apache Kafka with native CRD-based management.

Rôle : Event streaming platform for asynchronous communication

TiKV

production

Distributed transactional key-value store with ACID transactions and Raft consensus.

Rôle : Backend storage engine for SurrealDB with strong consistency

SurrealDB

production

Multi-model database supporting document, graph, and key-value data models.

Rôle : Flexible database for applications needing graph + document queries

Qdrant

production

Vector database for similarity search, powering semantic search and AI applications.

Rôle : Vector embeddings store for AI/ML workloads

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