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World Congress 2022 • Jun 15, 2022

Kubernetes and Microservices with Multi-Model Databases

Wei Hu

Stop deploying separate data stores for every microservice. A converged multi-model database native to Kubernetes eliminates data silos and guarantees exactly-once message delivery without two-phase commits.

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#1 about 3 min

Introduction to converged multi-model data architectures

Using multiple database variants creates management friction whereas a single converged engine supports multiple data types without operational complexity.

#2 about 2 min

Evaluating performance characteristics of multi-model databases

Analyzing operational benchmarks reveals that converged database engines routinely outperform single-function specialized databases across diverse transaction types.

#3 about 3 min

Deploying mission-critical databases within container infrastructures

Transitioning database nodes onto scalable orchestration engines requires containerization strategies enabling developers to easily customize core infrastructure manifests.

#4 about 4 min

Extracting native database metrics within Kubernetes environments

Monitoring distributed database performance effectively utilizes specialized telemetry extensions surfacing detailed storage metrics inside standard visualization tools.

#5 about 5 min

Managing heterogeneous deployment lifecycles via Kubernetes operators

Controlling database instances scattered across hybrid hosting environments simplifies greatly when utilizing infrastructure operators exposing standardized deployment commands.

#6 about 4 min

Providing distributed transaction support in microservices clusters

Deploying critical workflows safely inside Kubernetes necessitates integrated transaction managers mediating strong and eventual consistency protocols natively.

#7 about 5 min

Managing application isolation via pluggable database models

Reducing storage friction for independent microservices requires pluggable layer abstractions virtualizing discrete databases over one unified administrative footprint.

#8 about 1 min

Exposing database structures as standard REST APIs

Creating flexible integration boundaries accelerates application development by automatically exposing internal storage logic via accessible web endpoints.

#9 about 3 min

Loosely coupling workflows using native event queues

Built-in transactional messaging components deliver guaranteed event execution consistency to safely scale distributed operations without lost packets or duplicated transmissions.

#10 about 2 min

Avoiding boilerplate microservices coordination using database design

Embedding state synchronization deeply within the persistence layer systematically simplifies sophisticated integration patterns including transaction outboxes and saga compensations.

#11 about 2 min

Exploring microservices capabilities via interactive cloud workshops

Validating advanced deployment principles becomes easier when working directly with pre-integrated code examples hosted within standardized automated lab environments.

#12 about 1 min

Connecting converged databases with Elasticsearch frontend interfaces

Large scale implementations integrate backend storage efficiently serving directly behind elastic cluster interfaces for optimized distributed search functionality.

Matching moments

1:58 min

Modern improvements driving database adoption in Kubernetes

Denis Souza Rosa · LIVE

4:56 min

Boosting developer productivity via consolidated converged database architectures

Wei Hu Wei Hu · WWC 2023

2:36 min

Modern application stacks and real-time data requirements

Tim Faulkes · LIVE

3:32 min

Transitioning from monolith architectures to microservices and Kubernetes

Liviu Costea · LIVE

3:26 min

Comparing managed database services against native Kubernetes deployments

Denis Wilson Souza Rosa +1 · LIVE

5:18 min

Shifting mindsets on running databases in containers

Denis Wilson Souza Rosa +1 · LIVE

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