- BryteFlow with AWS enables Horizon Power to build a continually refreshed, transformed AWS Data Lake
- BryteFlow automatically extracts, transforms, and loads large volumes of complex enterprise data including IoT data in real-time on the AWS Data Lake
- Data is now a single source of truth for data consumers and analysts
- Increases accessibility of data and provides faster time-to-market for customer-centric innovations
- Enables better maintenance of equipment using data models for predictive analytics from IoT data
- Provided delivery of data in just 2 weeks, 70% to 80% faster than a previous implementation
How BryteFlow enabled Horizon Power to unify data on AWS Cloud, creating a single source of truth for the organization’s data consumers and analysts. The solution extracts, loads and transforms huge volumes of IoT data and other enterprise data locked in Oracle and SQL Server silos for meaningful, actionable insights.
Webinar: How BryteFlow on AWS is helping Horizon Power Accelerate Data Driven Outcomes
Client: Horizon Power
Horizon Power is a government trading entity that serves regional and remote Western Australia. It services a vast 2.3 million sq km area with only one customer per 53.5 sq km. Horizon Power has vertical integration and handles power generation, transmission, distribution, and retail. It manages the complete cycle -right from equipment maintenance to customer service.
Horizon Power delivers electricity to more than 47,000 connections, supplying to more than 100,000 residents and more than 10,000 businesses in regional towns and remote communities. As such, the organization needs to manage highly distributed equipment in difficult environmental conditions and needs efficient maintenance practices to prevent costs getting out of hand.
Challenge: Adapting to the Shift to Smart Energy
The power industry is changing rapidly. Across the world electricity companies are shifting to a culture of sustainable, efficient, smart energy. This shift consists of rapid growth in distributed energy resources like modular, decentralized power generation and use of stored renewable energy.
These initiatives need utility companies to increase the number of power sources they manage. Horizon Power decided to invest in new data management capabilities and analytics to pave the way for smart energy initiatives in the future. Says Suresh Parimi, manager of digital and data solutions at Horizon Power, “Improved analytics, especially predictive maintenance models, will help us prioritize maintenance activities and optimize our long-term maintenance strategy. Additionally, better demand forecasting will help increase utilization of distributed energy resources.”
Solution: Integrating Data with a Continually Refreshed, Transformed AWS Data Lake
Horizon Power selected Amazon Web Services (AWS) as its new data platform and sought the help of BryteFlow as an AWS Advanced Technology Partner (Data & Analytics Competency) to implement data ingestion and transformation.
BryteFlow enabled Change Data Capture from Horizon Power’s relational databases to continuously load and merge changes on the Amazon S3 Data Lake without impacting the original databases. BryteFlow merged the captured data with data from other sources including IoT data to provide insightful reports for Horizon Power. “BryteFlow leverages native AWS capabilities but does not require us to manage them directly, freeing time for value-added data analysis rather than coding,” says Parimi.
AWS Services and BryteFlow products used to build Horizon Power’s AWS Data Lake
Two BryteFlow products were used to create Horizon Power’s platform: BryteFlow Ingest for seamless real-time replication of data from multiple sources including on-premise relational databases to the Amazon S3 Data Lake. The second product was BryteFlow Blend, used to automate the ETL (Extract, Transform, and Load) process on Amazon S3 to prepare data for analytics and modeling.
Among the AWS Services, Amazon Elastic Compute Cloud (Amazon EC2) was used for compute capacity while Amazon EMR was used for data processing. Amazon Simple Notification Service (Amazon SNS) was used to drive information flows within the system.
Results: Fast Deployment and Real-Time Insights for Horizon Power
The deployment of BryteFlow was done in just 2 weeks which was at least 70% to 80% faster than the previous data ingestion. The previous implementation had taken Horizon Power months to deploy and configure. BryteFlow provided bulk data ingestion at extremely fast speeds using partitioning and parallel multi-thread loading, allowing Horizon Power to load huge volumes of data fast for immediate analytics.
Today Horizon Power enjoys the benefits of having a single source of truth across the organization and can implement all the data-driven initiatives they need. By unifying and centralizing their data, Horizon Power can face a future of rapidly changing energy resources and demands confidently.