DevOps and Platform Engineering

Pain points:

• Any deployment requires active and extensive participation of developers
• Credentials to production instances are not managed securely
• Deployment process is manual, undefіned, not repeatable, and fails frequently
• No health checking, all troubleshooting purely reactive
• Poor communication between Dev and Ops teams

Key milestones:

• Deployment process is defined and repeatable on developer workstations
• Routine deployment tasks are automated
• Faster deployment process
• Manual health checks via API / tools and modified process
• Consolidated logging

Challenges and pitfalls:

• Deployment process is still unstable
• Deployment scripts feature custom dependencies

Remaining pain points:

• Deployment still slow
• Deployment requires specific skills and knowledge
• Logs are difficult to access / analyze

Key milestones:

• Entire flow (build, package, deployment) is automated
• Any dev team member can run deployment
• Health check scripts integrated with CI (still triggered manually)
• Standardized logs complete with monitoring

Challenges and pitfalls:

• No deployment traceability
• Deployment process enabled by a collection of tools with no UI

Remaining pain points:

• Manual rollbacks only
• Scaling is still semi-manual, requires proactive manual intervention (can’t scale automatically in response to load)
• Monitoring can’t recognize patterns and predict issues

Key milestones:

• Any team member has the capability to deploy to any environment
• Permission-based model that restricts deployment
• Completely automated health checks
• Centralized monitoring helps automatically diagnose 99% of issues
• Automatic documenting

Challenges:

• Costly to implement
• Requires high level of skill to maintain and evolve in the long run

Key Milestones:

• Continuous improvement of reliability.
• Translating measurable indicators and metrics to business and decision making level
• Continuous enhanced monitoring and troubleshooting algorithms

Challenges:

• Skills and culture
• Costly to implement continuous improvement process

As a part of its DevOps consulting on continuous integration and continuous delivery, DataArt helps clients maintain strict control throughout the SDLC by automating product tests, deployment, and code management.

Targets of CI/CD DevOps Services:

• Shorten release lifecycle
• Shorten mean time to repair
• Ensure effortless deployment.

Approaches to Accomplishing CI/CD DevOps Services:

• Build config as a code
• Employ disposable, ephemeral agents
• Prevent downtime during releases
• Separate CI and CD
• Manage artifacts.

Frameworks and Tools for CI/CD DevOps Services:

• Scalable CI
• Seamless decomposition and parallel running
• Automatic code review and reporting
• Cloud-agnostic tools (including Jenkins, Bamboo, TeamCity, TravisCI, Concourse, GitLab CI, and Bitbucket Pipelines)
• Cloud-native tools (including AWS CodePipeline, Google Cloud Build, and Azure DevOps).

As a DevOps services company, DataArt integrates automated testing into the product / infrastructure and development processes. This ensures quick report times for inconsistencies, 24/7 uptime, and cost savings. It can also speed up your company’s release cycles.

Automated Testing Targets:

• Perform automated release checks
• Provide consistent and reliable feedback
• Ensure increased checks frequency.

Approaches to Accomplishing Automated Testing:

• Unit, Integration, Functional
• Web, API, mobile, and microservices
• TDD, BDD, and DDT
• Smoke and regression testing
• Benchmark testing, HA testing, and performance testing
• Parallel, scalable testing.

Frameworks and tools for CI/CD DevOps Services:

• Automation adoption and process changes
• JMeter, Artillery, Tsung, and Gatling
• Selenium, Appium, Saucelab, and Browserstack
• Test Complete and QTP.

Infrastructure management involves automatic environment provisioning, a monitoring program that sends relevant alerts, and autoscaling. This process is essential for product success.

As a DevOps consulting services provider, DataArt can help you maintain a healthy workflow and eliminate many critical problems at the source with the help of monitoring and alerting tools.

Infrastructure Management Targets:

• Ensure environment consistency
• Provide testability
• Shorten SDLC.

Approaches to Infrastructure Management:

• Infrastructure as a code
• Alerting & Monitoring
• Capacity Planning.

Infrastructure Management Tools, Frameworks and Technologies:

• Terraform, CloudFormation, ARM Templates, and GCP Deployment Manager
• AWS ECS, EKS, Fargate; GCP GKE; Azure AKS, and Nomad
• Docker, Puppet, Chef, and Ansible
• Prometheus, ELK, Grafana, Zabbix, DataDog, Splunk, and Fluentd
• AppsDynamics, and NewRelic
• AutoScaling and Blue/Green deployments.

As a DevOps solutions and services provider, DataArt concentrates on building security into the product at the earliest stages of the SDLC, instead of slotting it in at the final stage. This approach is called DevSecOps, and it can lead to seamless process integration, better security and compliance, and lower costs.

Security Management Targets:

• Identify vulnerabilities
• Ensure continuous compliance
• Apply security-first approach.

Approaches to Security Management:

• DevSecOps
• Policy as a Code
• Static Application Security Testing (SAST)
• Dynamic Application Security Testing (DAST)
• Software Composition Analysis (SCA)
• Cloud Compliance Monitoring
• Infrastructure and Container Scanning.

Security Management Tools, Frameworks, and Technologies:

• Checkmarx, SonarQube, and Veracode
• Burp Suite, Nessus, and Qualys
• Snyk and Sonatype Nexus
• Cloud-native tools, Scout Suite, Prowler, Azucar, and kubeaudit
• Gitlab Security Center.

As an official partner with AWS, Google Cloud, and Microsoft Azure, DataArt has strong experience working in Cloud. When DataArt’s software development and operations teams work on a Cloud project, we always follow best practices by identifying measurable operational goals, infrastructure management, and solution design patterns.

As a DevOps services provider, DataArt is familiar with cloud-integrated DevOps tools and respective offerings for DevOps automation from major cloud providers. 

Cloud Targets:

• Go global in minutes
• Reduce costs of management
• Improve security and compliance.

In addition to its vast expertise as a DevOps solutions company, DataArt operations teams have hands-on knowledge and experience working with production workloads in Docker and Kubernetes since 2016. DataArt has a number of certified Kubernetes Developers and Administrators.

Docker

• Best practices
• Technologies and tools
• Docker in dev, testing, production workloads.

Kubernetes

• K8S competency center
• Technologies and tools
• Operations
• CI/CD.

DOCKER AND KUBERNETES TARGETS:

• Ensure deployments flexibility by design
• Guarantee hybrid or multi-cloud agility
• Provide efficient resources management.

Kubernetes

K8S Provisioning:

• EKS, AKS, GKE
• Kops, Rancher, OpenShift, and Konvoy
• K8S on Mesos
• OnPrem/Bare metal.

Operations and Management

• Helm
• Istio and Calico
• Portworx
• Styra
• Traefik
• Prometheus, ELK, and Grafana
• Kubefed
• Kudo/Operators
• Kubeless.