Client Success Story: Unleashing the Power of AI and Big Data: Building Kudala's Private Multi-Tenant Cloud with Kubernetes
Client: Kudala IOT
Company: SQUADRON TECHNOLOGY
Problem Statement:
Kudala offers AI IoT solutions to global clients but faces challenges with their current product stack, built on 30+ OpenSource AI and Data Projects. The existing setup is resource-intensive, complex, non-scalable, and costly to maintain and monitor. Kudala seeks a solution that is Cloud Agnostic, providing seamless integration across various cloud platforms, scalable to meet increasing client demands, cost-effective to reduce operational expenses, equipped with proactive monitoring capabilities to detect issues in advance, and featuring one-click auto-deployment for streamlined updates and maintenance.
Project Tenure: 3 Months
Solution:
SQUADRON TECHNOLOGY collaborated with their client, Kudala, to consult Kudala on Cloud Architecture and Implement an innovative Solution to deploy Kudala Product Stack on K8 in a cost effective way. Squadron Architect proposed 2 possible Architectures on K8 that meet the requirements. After collaborative discussion between the Teams, Engineers came up with a hybrid approach consisting of best Solution considering Areas like:
Squadron Engineers have started development of building a Private Cloud-Agonistic Kubernetes Platform on 3 node machines with LDAP based Authentication, NFS based data persistence and NextCloud based User Interface. Within 2 Weeks, Team started onboarding all Kudala Stack on K8 on Platform with proper integration with LDAP and Storage.?
As part of Onboarding, Team has configured Various Open Source as per Projects on K8 Cluster:?
Distributed Data Processing: Hadoop, Hive,? Apache Spark, Collabora, Flink, Kafka SQL, Nifi
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AI Tool: Tensorflow, Keras, Compose, Cortex, H2O, Spark ML
Management Dashboard: Next Cloud
Dashboard: Grafana,Superset, Kibana
Authentication and Authorisation: LDAPs,?
Queue/Streaming: MQTT, Apache Kafka
Database: MariaDB, MindsDB,, Nextcloud, Node-RED, Prometheus, Redis, Timescale, ElasticSearch
Automation: Terraform and Python
K8 Management and Monitoring: Rancher?
As part of the onboarding process, the team expertly configured? required open-source tools and technologies, catering to the specific needs of Kudala's projects. These included distributed data processing tools such as Hadoop, Hive, Apache Spark, Collabora, Flink, Kafka SQL, and Nifi, as well as AI tools like TensorFlow, Keras, Compose, Cortex, H2O, and Spark ML. Additionally, the solution encompassed management dashboards like NextCloud, monitoring dashboards with Grafana, Superset, and Kibana, and authentication and authorization systems with LDAPs. Furthermore, queue/streaming capabilities were provided through MQTT and Apache Kafka, while databases such as MariaDB, MindsDB, Nextcloud, Node-RED, Prometheus, Redis, Timescale, and ElasticSearch ensured efficient data management. The entire system was made more agile and automated through the integration of Terraform and Python, while K8 management and monitoring were facilitated through Rancher.
Conclusion:
With this comprehensive solution now in place, Kudala is equipped to better serve its global clients with seamless AI IoT solutions. The transformation from a resource-intensive, complex, and non-scalable product stack to a Cloud Agnostic, highly scalable, and cost-effective solution will undoubtedly enhance Kudala's capabilities, reduce operational expenses, and enable the company to stay proactive in detecting and resolving issues. The one-click auto-deployment feature ensures streamlined updates and maintenance, making it easier for Kudala to keep their product stack up-to-date.
In conclusion, the successful completion of this project marks a significant milestone for both Kudala and Squadron Technology. The collaboration between the teams has resulted in a cutting-edge solution that sets Kudala on a path of growth and success in the rapidly evolving landscape of AI IoT solutions.