Kubernetes: An Introduction
In the ever-evolving world of engineering, scalability isn’t just a feature—it’s a necessity. As businesses and data continue to grow, the ability to scale applications efficiently becomes critical. At Datactics, we are at the forefront of integrating cutting-edge AI/ML functionality that enhances our Augmented Data Quality solutions. To align with current standards and ensure optimal AI/ML scalability with Kubernetes, our AI/ML team has integrated K8s into our infrastructure and deployment strategies.
What is Kubernetes?
Kubernetes, also known as K8s, is an open-source platform designed to automate the deployment, scaling, and management of containerised applications. It adjusts the number of containerised applications to match incoming traffic, ensuring adequate resources to handle requests seamlessly.
Docker containers, managed through an API layer often using FastAPI, function like fully equipped packages of software, including all necessary dependencies. Kubernetes enables ‘horizontal scaling’—increasing or decreasing the number of container instances based on demand—using various load balancing and rollout strategies to make the process appear seamless. This method helps evenly spread traffic among containers, preventing overload and optimising resources.
Kubernetes for Data Management
Every day, companies handle a lot of complicated data from different sources, at different velocities, and scales. This includes important tasks like cleaning, combining, matching, and resolving errors. It’s crucial to suggest and enforce Data Quality (DQ) rules in your data pipelines and efficiently identify DQ issues, ensuring these processes are automated, scalable, and responsive to fluctuating demands.
Many organisations use Kubernetes (K8s) to automate deploying, scaling, and managing applications in containers across multiple machines. With features like service discovery, load balancing, self-healing, automated rollouts, and rollbacks, Kubernetes has become a standard for managing applications that are essential for handling complex data—both in the cloud and on-premise. Implementing AI/ML scalability with Kubernetes allows these organisations to process large volumes of data efficiently and respond quickly to changes in data flow and processing demands.
Real-World Scenario: The Power of Kubernetes
It’s Friday at 5pm, and just as you’re about to leave the office, your boss informs you that transaction data for last month has been uploaded to the network share in a CSV document and it needs to be profiled immediately. The CSV file is massive—about a terabyte of data—and trying to open it in Excel would be disastrous. This is where Datactics and Kubernetes come to the rescue.
You could run a Python application that might take all weekend to process, meaning you’d have to keep checking its progress and your weekend would be ruined. Instead, you could use Kubernetes to scale out Datactics’ powerful Profiling tools and complete the profiling before you even leave the building. Company saved. Weekend saved.
Application of Kubernetes
The world has grown progressively faster, and speed in the digital realm is king: speed in service delivery, speed in recovery in the event of a failure, and speed to production. We believe that the AI/ML features offered by Datactics should adhere to the same high standards. No matter how much data your organisation handles or how many data sources there are, it’s important to adjust resources to meet demand and reduce waste during the most critical moments.
At Datactics, AI/ML features are deployed as Docker containers and FastAPI. Depending on your particular environment, we might run these containers on a single machine like AWS EC2 and deploy a single instance of each AI/ML feature, which is suitable for experiments and proof of concepts. However, for a fully operational infrastructure capable of supporting a large organisation, Kubernetes is essential.
Kubernetes helps deploy Docker containers by providing a blueprint with deployment details, necessary resources, and any dependencies like external storage. This blueprint facilitates horizontal scaling to support additional instances of each AI/ML feature.
Conclusion
Kubernetes proved to be a game-changer for scaling Datactics’ AI/ML services, ultimately leading to a robust solution that ensures our AI/ML features can dynamically scale according to client needs. We tailor our deployment strategies to meet the diverse needs of our clients. Whether the requirement is a simple installation or a complex, scalable infrastructure, our commitment is to provide solutions that ensure our clients’ applications are efficient, reliable, and scalable.
We aim to meet any specific requirements, always exploring various potential deployment setups preferred by our clients. If your organisation is looking to enhance its data processing capabilities, get in touch with us here. Let us help you optimise your data management strategies with the power of Kubernetes and our innovative AI/ML solutions.
