Yaroslav has been working in IT industry since 2008, delivering more than 50 projects in different domains across the globe. In the last few years, he’s been cooperating with high profile clients to provide guidance and support on their organizations’ journey to digital transformation. He worked with the biggest retailers in North America and tech giants such as Cisco, Dell, Suse, and Canonical. His most significant recent project is Data Science Platform (ML) developed in collaboration with Dell, Canonical, SUSE, and Intel (officially announced on February 2020). Today, he is a head of the IT Advisory Group at the Intellias Technology Office. Speech Overview: We will discuss the reasons why Kubernetes won’t change a lot outside the infrastructure providers’ world and why it’s not the best option for you. Why is it more important to understand the concept behind this platform rather than use Kubernetes itself? How Kubernetes managed to become the platform for computation, while OpenStack didn’t? What other alternatives might work better for you? When to use and not to use Kubernetes?

1. You don’t need Kubernetes.
How Kubernetes is changing the world, but we are looking at the wrong
part. And why it is ok.
2020 | intellias.com

2. 18TOP 101500+ 40%
engineers in-house Biggest IT company
in Ukraine
Years of software
Engineering experience
Year to year growth
Intellias Quick Facts
United States
Germany
Poland
Ukraine
Israel
Saudi Arabia

3. Education / Certificates Key Industries Professional Focus
Relevant Experience
Yaroslav Ravlinko
Information technology and
services
Banking and Finance
E-commerce
Infrastructure(Cloud) and
Hardware Providers
Conducted Digital and IT transformation programs for global multi-billion
enterprise businesses
Have been working with the biggest retailers in North America since 2013.
Conducted IT Operations assessment and consultancy for medium and
enterprise organizations (more than ten enterprises and 30+ overall)
Built a few dozen complex solutions for business functions (PaaS, IaaS,
Cloud adoption, on-premises, etc.)
Built many Data Processing and Machine Learning platforms for Enterprise
organizations.
A most notable project is Data Science Platform (ML) developed in
collaboration with Dell, Canonical, SUSE, and Intel (official announcement
on February 2020)
Head of IT Advisory Group at Intellias
Technology Office
MS in Specialized Computer
System, Lviv Polytechnic
National University
System architecture design
Digital Transformation and IT
transformation guidance
Internal and external consulting
activities
“Big” Data processing and
modern DW
Data Science and ML Platform
Development
Complex projects management
Leading Intellias IT Advisory
Group

4. Kubernetes is great!

5. The guy

6. Containers are the future
https://twitter.com/kelseyhightower/status/943495161413324800?s=20

7. Kubernetes is the future
https://twitter.com/kelseyhightower/status/1202010135213666304?s=20

8. Docker and Kubernetes are great for development
https://twitter.com/kelseyhightower/status/981212019687751681?s=20

9. Kubernetes is great paltfrom for all workloads
https://twitter.com/kelseyhightower/status/963413508300812295

10. Microservices
• Usage of the different technological stack to
develop different parts of the ecosystem
• Separate deployment and management of
components (as services)
• Ability to scale a specific part of the system
without the scaling system overall
https://www.youtube.com/watch?v=GBTdnfD6s5Q

11. But they all wrong! Somethimes …
https://twitter.com/kelseyhightower/status/940259898331238402?s=20

12. We should blame someone
Docker Docker
Kubernetes/Mesos
libnetwork
Rolling Deployment
Monitoring
Suppervision
Config Changes
Peer Discovery
Container Hosting
Code Quality
Security
PRODUCTIONDEVELOPMENT
The “Learning Cliff”

13. What is my point?

14. Bad start
https://kubernetes.io/docs/concepts/overview/what-is-kubernetes/

15. Very bad start
https://cloud.google.com/kubernetes-engine/kubernetes-comic/

16. Kubernetes Components
https://kubernetes.io/docs/concepts/overview/components/

17. Bad usage

18. Good start: Kubernetes is not just API-driven, but is API-centric.
The Kubernetes Resource Model (KRM)

19. Declarative control
• In Kubernetes, declarative abstractions are primary, rather than layered
on top of the system. The Kubernetes control plane is analogous to cloud-
provider declarative resource-management systems, but presents higher-
level (e.g., containerized workloads and services), portable abstractions.
• Kubernetes supports declarative control by recording user intent as the
desired state in its API resources.
• Controllers continuously strive to make the observed state match the
desired state, and report back their status to the apiserver
asynchronously. All of the state, desired and observed, is made visible
through the API to users and to other controllers. The API resources serve
as coordination points, common intermediate representation, and shared
state.
observe
diff
act

20. Chef, Pappet, Ansible

21. CSV files on Disk SQL DataBase
Imperative vs Declarative (abstraction)

22. Complexity
Governance Layer: Automation and Policy Enforcement (APIs optional and pluggable)
Application Layer: Deployment and Routing (APIs required and pluggable)
Nucleus: API and Execution (APIs required and not pluggable)
CronJob
batch/v2alpha1
Job
batch/v1
Deployment
apps/v1
DaemonSet
apps/v1
Pod
core/v1
ReplicaSet
apps/v1
StatefulSet
apps/v1
ReplicationController
core/v1
Endpoints
core/v1
Ingress
extensions/v1beta1
Service
core/v1
ConfigMap
core/v1
Secret
core/v1
PersistentVolumeClaim
core/v1
StorageClass
storage/v1
ControllerRevision
apps/v1
Event
core/v1
LimitRange
core/v1
ValidatingWebHookConfiguration
admissionregistration/v1alpha1
HorizontalPodAutoscaler
autoscaling/v1
APIService
apiregistration/v1beta1
PodDisruptionBudget
policy/v1beta1
PodPreset
settings/v1alpha1
PodSecurityPolicy
extensions/v1beta1
CertificateSigningRequest
certificates/v1beta1
ClusterRole
rbac/v1beta1
ClusterRoleBinding
rbac/v1beta1
LocalSubjectAccessReview
authorization/v1
Namespace
core/v1
Node
core/v1
PersistentVolume
core/v1
ResourceQuota
core/v1
Role
rbac/v1beta1
RoleBinding
rbac/v1beta1
SelfSubjectAccessReview
authorization/v1
ServiceAccount
core/v1
SubjectAccessReview
authorization/v1
NetworkPolicy
networking/v1
ComponentStatus
core/v1
PriorityClass
scheduling/v1alpha1
ClusterServiceBroker
servicecatalog/v1beta1
ClusterServiceClass
servicecatalog/v1beta1
ClusterServicePlan
servicecatalog/v1beta1
ServiceInstance
servicecatalog/v1beta1
ServiceBinding
servicecatalog/v1beta1
MutatingWebHookConfiguration
admissionregistration/v1alpha1
SelfSubjectRulesReview
authorization/v1
TokenReview
authentication/v1
CustomResourceDefinition
apiextensions/v1beta1
https://docs.google.com/presentation/d/1oPZ4rznkBe86O4rPwD2CWgqgMuaSXguIBHIE7Y0TKVc/edit#slide=id.p

23. Extention (CR and Operators)
• Users need to learn some API schema details, though we believe operators
will want to learn them, anyway.
• The API schema does contain a fair bit of boilerplate, though it could be auto-
generated and generally increases clarity.
• The API introduces a significant number of concepts, though they exist for
good reasons.
• The API has no direct representation of common generation steps (e.g.,
generation of ConfigMap or Secret resources from source data), though
these can be described in a declarative format using API conventions, as we
do with component configuration in Kubernetes.
• It is harder to fix warts in the API than to paper over them. Fixing “bugs” may
break compatibility (e.g., as with changing the default imagePullPolicy).
However, the API is versioned, so it is not impossible, and fixing the API
benefits all clients, tools, UIs, etc.
https://www.oreilly.com/library/view/kubernetes-operators/9781492048039/ch01.html

24. Good use case: distributed databases

25. Good use case: ”big” data processing
https://www.youtube.com/watch?feature=youtu.be&v=Pt_L1m94Ze4

26. Good use case: Machine Learning Systems
Master Components
etcd
apiserver
Controllers
Operators and CR
Prometheus
ArgoCD
Fluentd
Serving SpaceKubeflow Operators and CR
JupyterHub
Minio
Katib Argo
Istio
tf-job
tf-dashboard Katib UI Pipeline UI Jupyter
Seldon
Clipper
TF Serving
Kubernetes API
PVC
KFServing
Dex
ML Operators and Services
ChainerMPI pytorch
“Big” data operators
MLFlow
Spark
Flink
Kafka
Dask
Metadata

27. Alternatives
https://raw.githubusercontent.com/hashicorp/terraform-aws-nomad/master/_docs/architecture-nomad-consul-separate.png

28. Thank you!
Learn more about Intellias
Thank you!
Learn more about Intellias
2020