Case Study
Scaling Big Data Infrastructure for Energy Market Analytics
The challenge
Enerlytica analyzes energy-market data. As datasets grew and more customers came on board, processing became harder to scale. The team also had limited visibility into its data pipelines—the sequences of steps that move and process data—making issues harder to identify and troubleshoot.
What we did
Das Meta moved the data-processing system to AWS and packaged the workflows in containers, portable units that hold an application and what it needs to run. Kubernetes manages those containers across computing resources. The team used Prefect to schedule and coordinate data workflows and added monitoring and logging so operators could see what was happening.
Where that left the team
The published case describes automated workflows and autoscaling, which adjusts computing capacity as demand changes. It does not give a measured improvement in processing time or cost. Read about cloud migration and cloud infrastructure management.
Frequently Asked Questions
What kind of workloads are covered?
The case study concerns energy market data processing and analytics workloads for Enerlytica.
Which cloud provider is discussed?
The case study describes the use of AWS for the data infrastructure.
What operational challenge prompted the work?
Growing data volumes and limited visibility into processing workflows created infrastructure and monitoring challenges.