ilustration image

Lara

Migration

Saas

LARA Deployment & App Migration for Dataddo

Client

Dataddo

Project type

LARA

As Dataddo was moving to a more mature product and application design, they were looking for a cloud native platform to run their applications, fast. 

Technology Stack

  • AWS EKS, Aurora, ElastiCache, OpenSearch
  • Go, PHP, Vue.js
  • PosrgreSQL, MySQL, MongoDB, Redis, RabbitMQ
  • Terraform, Helm
  • GitlabCI, ArgoCD, Prometheus, Thanos, Grafana

The Client

Dataddo is a cloud-based, no-code data integration and management platform that works with almost any online data service. The Dataddo app can transform and transfer data to a wide range of databases, DWH solutions, cloud storages and dashboards. 

Challenge

Dataddo was looking for a cloud native platform they could trust and build their business around, including everything needed to run a cloud native and highly scalable application. Our LARA helped them get a platform which was:

  • Auto scalable
  • Autohealing
  • Highly available
  • Cost optimal
  • Modular
  • Observable

All of this was done with the emphasis on a quick time to market delivery.

Outcomes

Labyrinth Labs Reference Architecture helped them design and implement better infrastructure patterns and migrated their applications to EKS.

During our work we have implemented:

  • CI/CD
  • Monitoring
  • Infrastructure as Code
  • Account structure
  • Application refactoring

15

microservices

100TB

data transferred monthly

Goals

As Dataddo was moving to a more mature product and application design, they were looking for a better platform to run their applications with many benefits like:

  • autoscaling
  • autohealing
  • higher availability
  • cost optimal solution
  • modular design

Our Process

How we started [implementation]

Implementation of the following account structure with a separate audit account to make sure we can store and audit Cloudtrail logs outside of developer accessible accounts. We have also added 4 accounts for different types of workload:

  • Dev
    • Dev workload
  • Test
    • Test workload
  • Prod
    • Production workload
  • Shared
    • Used for services unrelated with customer workload e.g. monitoring, logging, CI/CD

Process [kubernetes]

Then, with our help Dataddo refactored their applications to work in the highly dynamic world of Kubernetes. Applications were modified to work with Kubernetes:

  • no stateful applications
  • using requests/limits to help application deployment scalability
  • highly available deployments with multiple replicas
  • observable infrastructure with multiple views
  • infrastructure metrics
  • application metrics
  • application logs

Process [cost optimization]

Implementation of different workload cost optimisations:

  • migrated to Spot instances
  • used cloud savings plan for workloads not running on spot instances
  • we haven’t used reserved instances because customer is using MongoDB

 

To better leverage Kubernetes Architecture we have helped the customer to move into a more automated Continuous Delivery model. Where they are using prepared helm charts to deploy application containers images into the cluster.

Continuous Integration pipeline is used for application verifications (test) and container image build.

Finish [utilization EKS]

Utilization EKS as the core services,which affected the customer workload and all of the supporting tools and utilities. 

We use multiple add-ons which enhance the whole experience of using EKS. Using these addons, we are able to automate many tasks like, DNS record provisioning, certificate generation and renewal, secrets management, monitoring, logging, and more.

Solution

To implement initial infrastructure architecture, deploy EKS with tested set of addons (external-dns, argocd, thanos, nginx-ingress controller, cert-manager, external-secrets, aws-loadbalancer-controller, metrics-server, log gathering solution) and migrate different refactored applications in to EKS with proper CI/CD, Monitoring, Automation & Backend Databases. To speed up this process we used our ready made and battle tested set of IaaC modules called Labyrinth Labs Reference Architecture.

Outcomes

By using our Reference Architecture and EKS Dataddo was able to scale their Workload fast and in a cost efficient way. Their existing architecture enables them to deliver more efficient software, faster and cheaper than their previous setup. It’s also much easier to maintain and support with a state of the art architecture and tools like Helm, Terraform, GitlabCI and Argo, which are already familiar among the developers.

Because Kubernetes is a highly dynamic and distributed system we made sure monitoring and observability stack were properly configured to gather as much information as possible about application/infrastructure behavior. This helps us and our customer deliver a much better SLA to their customers who are looking for a stable platform for their business needs.

Tomáš Sedláček

CTO at Dataddo, Inc.

I’d definitely recommend them because they have strong knowledge in the infrastructure field, including AWS. Their team members are very responsive and able to help us when we need it.

Future steps [life-long support]

Labyrinth Labs provides long-term support for the newly built infrastructure, including regular upgrades and maintenance of the platform, as well as continuous improvements of the setup.

Want to achieve
such results?

We are here for you
contact us

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Ivan Sakin

Growth & partnership manager

email [email protected] phone +421 221 020 694

WHAT’S NEXT?

  • AWS EKS
  • Go
  • OpenSearch

LARA Deployment & App Migration for Dataddo

  • Aurora
  • AWS EKS
  • Terraform

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