Scaling Big Data Infrastructure for Energy Market Analytics

Enerlytica is a leader in data analysis for the energy market. With over a decade of experience, they help energy companies unlock the potential of their data. However, as data volumes and complexity grew, Enerlytica encountered critical challenges in scaling their data infrastructure. To continue offering cutting-edge analytics and insights, they needed a robust, scalable, and efficient system. That’s where we came in.

Case Study

Scaling Big Data Infrastructure for Energy Market Analytics

Enerlytica is a leader in data analysis for the energy market. With over a decade of experience, they help energy companies unlock the potential of their data. However, as data volumes and complexity grew, Enerlytica encountered critical challenges in scaling their data infrastructure. To continue offering cutting-edge analytics and insights, they needed a robust, scalable, and efficient system. That’s where we came in.

Orders.co

Challenge

As data workloads increased, Enerlytica faced performance bottlenecks, leading to delays and inefficiencies in processing large datasets. Without autoscaling, the infrastructure couldn’t adapt to fluctuating demand, resulting in under-utilized resources during low demand and overwhelmed systems during peak loads. As Enerlytica onboarded more clients and processed larger datasets, they experienced limitations that hindered their ability to scale operations. The lack of monitoring made it difficult to track the health and performance of data pipelines. This led to challenges in identifying and resolving issues, prolonging debugging times and reducing overall reliability.

What We Did

We moved Enerlytica’s entire data processing system to Amazon Web Services (AWS). By leveraging AWS, we were able to provide a more flexible and resilient infrastructure, ensuring that Enerlytica could handle increased data loads efficiently.

We containerized the data processing workflows using Kubernetes. This enabled Enerlytica to deploy, manage, and scale their applications more effectively, distributing workloads across multiple nodes. Prefect allowed us to automate, schedule, and monitor data workflows efficiently.

With built-in monitoring and logging, Enerlytica’s team could now gain real-time insights into their data pipelines, making it easier to detect and fix issues quickly. We optimized Enerlytica’s database management system by introducing ClickHouse for high-performance, real-time analytics on large datasets. ClickHouse, a columnar database, enabled faster data querying and analysis.

Results

Enerlytica’s migration to AWS, coupled with the implementation of Kubernetes and Prefect, dramatically improved their ability to scale and manage complex data workflows. Autoscaling capabilities allowed their infrastructure to dynamically adjust based on real-time demand, eliminating bottlenecks and ensuring optimal resource utilization. With the integration of ClickHouse, data querying and analytics performance significantly increased, enabling Enerlytica to process large datasets faster and deliver insights more efficiently.

As data workloads increased, Enerlytica faced performance bottlenecks, leading to delays and inefficiencies in processing large datasets.

Without autoscaling, the infrastructure couldn’t adapt to fluctuating demand, resulting in under-utilized resources during low demand and overwhelmed systems during peak loads. As Enerlytica onboarded more clients and processed larger datasets, they experienced limitations that hindered their ability to scale operations.

The lack of monitoring made it difficult to track the health and performance of data pipelines. This led to challenges in identifying and resolving issues, prolonging debugging times and reducing overall reliability.

The Challenge

Conclusion

Enerlytica is now equipped with a modern, scalable infrastructure that supports its growing client base and data processing demands. The introduction of automated workflows, enhanced monitoring, and high-performance analytics tools has empowered them to maintain a competitive edge in the energy market by offering faster, more reliable insights to their clients.

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