Big Data Infrastructure Automation: Why You’re Wasting Time Without It
We recently worked with a growing machine learning startup tackling a very real challenge in the energy sector. Their mission? To analyze real-time consumption data from smart meters and offer actionable insights—helping end users and utility providers optimize usage, spot inefficiencies, and prevent outages.
In short: turning raw energy data into intelligent decisions.
Their product was good. Traction was better. But infrastructure?
It was about to collapse under the weight of their own success.
Deployed on IONOS Cloud and a legacy host, with no elasticity
No containerization, just fixed machines
Data pipelines = cronjobs, running Python and R scripts manually stitched together
Analytics stored in the same MariaDB as the application
No monitoring, no CI/CD, no infrastructure as code
Legacy frameworks like old PHP versions forced ancient runtime environments
Mixed tech: Javascript, PHP, Python, R — all uncoordinated
One environment per customer — no multi-tenancy
The startup’s data volume was growing daily. New smart meter integrations, more real-time pipelines, and analytics needs per customer.
But with every new customer:
The worst part? The analytical workload was hammering their main database. It slowed everything down and drove up costs. Mistakes in queries weren’t just slow—they were expensive.
In the world of big data, where volume, velocity, and variety are core realities, this was a recipe for disaster.
The startup reached out to our full-stack cloud engineering team because they simply couldn’t move anymore. Our team—comprising cloud architects, DevOps specialists, and data engineers—identified critical bottlenecks and implemented a scalable, resilient architecture.
Quick wins that became long-term liabilities
Tech built for the MVP still powering the live product
Infrastructure glued together by a handful of smart people—now hitting their limit
The CTO, thankfully, was forward-thinking. He saw the cracks and welcomed the change. He asked good questions, challenged assumptions, and valued automation once the trade-offs were clear.
Our engineering team began by addressing foundational infrastructure gaps—implementing containerization, orchestration, and monitoring solutions to enhance stability and scalability.
Containerization of all components (Node.js, PHP, Python workloads)
Moved infrastructure to AWS, using EKS for orchestration
Introduced Bitbucket Pipelines for CI/CD
Set up monitoring and observability with CloudWatch and Grafana
Replaced cronjobs with a proper Prefect-based orchestration system
This gave the team visibility, consistency, and the ability to deploy and fix things quickly.
Our data engineering experts transitioned analytics workloads from MariaDB to ClickHouse, drastically reducing query times and costs, and enabling more efficient data processing.
Built an Apache Superset dashboarding layer for better reporting
Introduced multi-tenant-aware components where possible
Designed an API gateway layer to help decouple services
This wasn’t about re-writing everything. It was about choosing battle-tested tools and incrementally modernizing.
With a stable multi-tenant base, our DevOps specialists focused on automating the provisioning of new environments, ensuring rapid and consistent onboarding for new customers:
Automated provisioning of new environments
Shared infrastructure for data collection and preprocessing
Easier config-driven setup to onboard new customers quickly
The outcome? A platform that could grow without growing the op
🚀 Faster feature releases — no more pipeline fragility
🔍 Improved observability — issues were found and fixed before users noticed
💰 Lower per-customer cost — infra scaled, team size didn’t
💡 Improved team morale — no more burnout from late-night debugging
📊 Better analytics — with ClickHouse and Superset, insights were faster and cleaner
With our cloud engineering team managing the infrastructure, the CTO could finally focus on product strategy, confident that the platform's scalability and reliability were in expert hands.
They think automation and cloud infrastructure is “Phase 2”.
But by the time they hit scaling problems, it’s already late.
Big data makes small inefficiencies very expensive.
You don’t need to spend months building custom infra.
Use AWS startup credits to offset infra costs
Get a containerized, scalable base in a day
Onboard your team to modern observability tools in a week
Avoid repeating the same infrastructure mistakes 100 other companies have made
Push ClickHouse earlier. The moment we split analytics from production, everything got better.
Consolidate redundant services earlier. We left a few overlapping tools that could’ve been merged sooner.
These small changes could’ve saved weeks and even more cost.
