Gone are the days of one-size-fits-all analytics solutions. Today’s tech landscape calls for a more dynamic, cost-conscious approach. Bridging the gap between theory and practice, this article pivots from the conventional analytics platform debate to a hands-on guide for harnessing the power of Kubernetes in creating a budget-friendly and high-performing analytics environment. We’re focusing on practical, impactful strategies that mold cloud analytics to fit not just your financial constraints but also the unique tempo of your business data, ensuring you get the most bang for your buck in the world of cloud analytics. We’ll also explore how Kubernetes, as part of the modern analytic stack, provides a powerful alternative to proprietary cloud services, promoting cost-efficiency and agility in analytics operations.
Choosing the Right Hosting Model
The hosting model you pick can make or break the bank in analytics. Each hosting model for analytic databases has unique cost implications. Here’s a snapshot of the options:
- ‘Buy the Box’ Model: Ideal for unpredictable customer analytics. It offers cost-effective computing but tends to have higher storage costs due to block storage usage.
- Snowflake’s Virtual Data Warehouse Model: This model suits enterprises looking for a comprehensive, all-in-one analytics solution. It’s known for higher compute costs but offers a robust, general-purpose database.
- BigQuery’s On-Demand Query Model: BigQuery is particularly cost-effective for sporadic query loads but can become expensive with extensive data scans. Its on-demand nature makes it suitable for varying analytic demands.
If you’re interested in reading a more detailed analysis of the cost structure and dynamics of each model, especially regarding compute expenses, you should check out this Hackernoon feature published by Altinity Inc.
How to Get a Good Deal on Cloud Analytics: Advanced Cost-Optimization Strategies
A reasonable cloud analytics pricing should be affordable and scalable in line with your business growth. It should be devoid of charges for unused resources and free of hidden costs like data transfer fees. Beyond the basic platform choices, the following advanced strategies can help in optimizing your cloud expenses:
- Decouple and Scale: Opt for services that offer separate storage and compute to ensure flexible scaling and cost management, especially critical for persistent analytics workloads.
- Compressed Storage Billing: Choose providers like Snowflake and ClickHouse that bill for compressed storage, allowing you to harness cost efficiencies. If you are not quite familiar with Clickhouse then check out this gentle introduction.
- Query Optimization: On platforms like BigQuery, refine your query design to minimize data scans, which can lead to significant cost savings.
- Hybrid Storage: Employ a blend of block and object storage solutions to strike the right balance between performance and cost.
- Auto-Scaling: Utilize auto-scaling compute resources to align performance with the ebb and flow of your operational demands without overspending.
- Economical Long-Term Storage: For seldom-accessed data, turn to cost-saving long-term storage options like Amazon Glacier or Google Coldline.
- Negotiate Discounts: Proactively seek out discounts for substantial monthly expenditures, focusing on compute resources where possible.
- Leverage Marketplaces: Make purchases through cloud marketplaces to potentially reduce overall costs in line with your service agreements.
How to Get an Even Better Deal: Build with Open-Source
When default cloud services don’t quite fit the bill, for example, when you need a GDPR-compliant analytics solution, a custom Kubernetes-based approach is a smarter strategic pivot. This method forms the foundation of what’s called a Modern Analytics Stack, which is highly adaptable for stringent compliance and specific operational demands.
You can harness Kubernetes, a powerhouse for orchestrating containerized applications, to construct a robust, scalable foundation for your modern analytics stack. This isn’t just about infrastructure; it’s about crafting a toolset that bends to your will, not the other way around. By using open-source databases optimized for specific tasks, such as ClickHouse for real-time analytics, you can tailor your stack to your application’s requirements.
Step 1: Choose Managed Kubernetes
Jumpstart your journey with a managed Kubernetes service. It’s like having a team of experts running the background operations so you can concentrate on your app. And it’s affordable – take Amazon EKS, which is about $72 a month.
Step 2: Select the Right Database
Next, you’re selecting an open-source database. For analyzing data on the fly, ClickHouse is your go-to. It’s purpose-built for speed and efficiency, especially if you’re dealing with real-time data.
Step 3: Use a Kubernetes Operator
Now, you’re choosing the right tool for the job, ensuring your database can keep up with the speed of your data. With Kubernetes, managing your database becomes a breeze when you utilize an operator. Time to meet the Altinity Operator for ClickHouse on GitHub. This isn’t just a tool; it’s your command center for database deployment and maintenance. You just feed it a simple YAML file – a set of instructions – and it sets up your database just like that.
Step 4: Set Up Observability
Monitoring and observability aren’t just afterthoughts. You integrate Prometheus to keep tabs on your operations and Grafana to visualize the story your data tells. They work together to let you see what’s happening under the hood of your app, with detailed graphs and real-time data.
Step 5: Implement GitOps with Argo CD
Argo CD is your bridge between the code in your GitHub and your live app. With Argo CD, you’re not just deploying code; you’re deploying confidence. Your infrastructure becomes as manageable as a git repository. It takes your changes and updates your app across Kubernetes clusters automatically or with a simple command.
And that is it! You’ve got a modern, agile analytics stack. It’s a setup that’s easy to change, easy to scale, and easy to keep an eye on – all while being light on your wallet. Plus, with tools like Argo CD, you can update your app with just a push to GitHub. Following these steps, you’re not just building a stack; you’re architecting a solution. Kubernetes‘ adaptability meets the precision of open-source tools, all orchestrated through the rhythm of GitOps.
In short, this is a cost-effective, scalable way to build an analytics app that grows with you, powered by the community-driven innovation of Kubernetes and ClickHouse.
We have an excellent hands-on demo by Robert Hodges showcased in the webinar which this article is derived from. If you’re specifically interested to see the demo, then go straight to the timestamp 40:30 
Conclusion
Kubernetes might seem daunting, but it’s actually a clear-cut way to a solid app foundation. Managed services like Amazon EKS streamline its complexity. ClickHouse excels in real-time analytics, and with the ClickHouse Operator, deployment becomes a breeze. Tools like Prometheus and Grafana give you a window into your system’s health, while Argo CD and GitOps practices link your codebase directly to deployment, automating updates across environments.
If you hit a snag or need to expand your stack, Altinity’s ClickHouse support and the Altinity.Cloud platform offer the guidance and resources to simplify the process, ensuring your project’s success with less hassle.
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