Our Client is a global mining leader that manages resources like diamonds, platinum, and copper. It relies on large amounts of data to improve energy efficiency and make its supply chain run smoothly. Their goal is to rethink mining to enhance lives while reducing environmental impact. To achieve this, they constantly update data strategies, optimising how real-time data become insights that help their teams in daily tasks.
By 2030, the client aims to cut greenhouse gas emissions and improve energy efficiency by 30%. They also plan to reduce water use by 50% in dry regions and create five off-site jobs for every on-site job in local communities. Ambitious plans like these require skilled, dependable teams and a mix of smart, data-driven technologies.
In the mining industry, data isn't a luxury; it's a lifeline. It helps companies handle changing prices, track equipment performance, and monitor environmental impact, all while keeping their people safe. To meet these objectives, our client gathers data from hundreds of sources, including geographic information systems (GIS), material flow optimisation solutions, environmental monitoring apps, and dozens of business platforms.
With time, structured and unstructured data at the company grew out of control and getting clear insights became difficult. Client's operations suffered, data quality deteriorated, and governance risks increased without a comprehensive, flexible data management solution.
Because of the obsolete, inflexible data architecture and coding challenges, the system often experienced slowdowns and delayed insights. This affected productive work. The company wanted to replace the existing data solution with a modern approach that would offer efficient, standardised global access to information across various sources. With their internal teams working at capacity, they brought Pwrteams’ remote data and DevOps experts to handle the task.
The project started by providing two specialists in charge of implementing a scalable Azure Databricks data lake service to store and analyse vast data and developing a REST API interface for communication. The aim was to seamlessly integrate it with the extensive data sources across Chile, Brazil, South Africa, Australia, the US, and other worldwide locations.
“Our main task was to build and manage the data pipeline while addressing the features requested by the Client’s data modellers,” says the Data Engineer working for the client. “We also worked on a proof of concept to test and show other potential technologies that could make data analytics faster, more efficient and scalable. This included implementing a solution for regular batch updates and Terraform scripts to automate the setup and management of Databricks workflows.” The team has also improved the code, refactoring it according to the best software development practices to make updates and changes easier in the future.
After the project's first stage, four more people joined the team. Their job was to improve data processing improvements and migrate the data orchestration framework to Apache Airflow. “Some of the Extract, Transform and Load processes were originally orchestrated by Azure Data Factory, which caused roadblocks and hindered data processes,” says the Data Engineer. “Together with our Ukraine-based team, we worked on a new orchestrator to better manage the data flow in the customer’s pipeline.”
Our experts adapted existing pipelines to the new orchestration framework, completing the work within a shorter timeframe than estimated. The team also moved and united siloed information from multiple storage locations into the new data lake for efficient and secure analysis. “We implemented the Unity Catalog for Databricks to enhance the overall security and governance policies associated with data management,” adds our Data Engineer.
Removing manual infrastructure management and replacing it with container-based architecture to enable Infrastructure as Code (IaC) was another integral part of the project. The team achieved it by combining the powers of Docker to manage the deployment of containers, Kubernetes for container orchestration, and Terraform for the provisioning of resources.
Altogether, the specialists provided by Pwrteams delivered the following projects:
The cooperation surpassed expectations, providing secure, compliant access to essential data for international teams and improving overall efficiency ahead of schedule. Now, the client has a strong framework for managing data within the Databricks platform, giving them better control over data access, metadata, and overall data governance.
The data lake implementation went from the drawing board to production in under four months, driving outstanding operational and cost savings.
Moreover, introducing the Unity Catalog has improved control over access, metadata, and overall data governance, meeting the client's pressing need to better secure their critical data. Additionally, by leveraging Terraform, our experts have enhanced the efficiency of infrastructure management, allowing for greater scalability, repeatability, and consistency across the company’s Databricks environment.
“Client wanted to connect data from all its divisions for better, consistent insights that empower their workforce,” summarises our Data Engineer. “With our assistance, they built a data team that made this happen, creating a clear, scalable, real-time analytics solution. Now, it helps them handle mining challenges and confidently move toward their sustainability goals, with data supporting their decisions.”
Get a firsthand account from one of our expert data engineers who worked on this project. In an exclusive interview, they share:
Discover the creative problem-solving and innovation that make data engineering both an art and a science. Read the full interview here.
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