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Implementing Containerised Big Data Infrastructure for Advanced Analytics

To support real-time insights, scalable data processing, and machine learning development, we designed a modern big data platform built on containerised infrastructure. This involved deploying Docker and Kubernetes for orchestration, developing automated data pipelines, and optimising database operations. The result is a flexible analytics environment that accelerates data workflows, empowers engineering teams, and enables more informed, data-driven decision-making.

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Client Overview

The client operates in the energy and refinery sector, managing large volumes of operational data across complex industrial systems. As part of their strategic shift toward data-driven optimisation, the organisation aimed to enhance its analytics capabilities and build a scalable foundation for future machine learning initiatives.

Big Data Infrastructure

Challenge

The client faced mounting data challenges that limited their ability to scale analytics and support AI-driven decision-making. Key obstacles included:

Unscalable Data Processing Workflows

The legacy infrastructure lacked the ability to efficiently handle large-scale, real-time data ingestion and transformation.

Barriers to ML Development

The absence of an optimised processing layer made it difficult for teams to train, test, and deploy machine learning models effectively.

Siloed Systems and Manual Operations

Lack of automation and centralisation across systems increased operational friction and limited visibility into data health.

Low Reusability and Knowledge Gaps

Development workflows were fragmented, and the engineering team lacked the tooling and processes needed to collaborate and iterate efficiently.

Solution

Bion Consulting implemented a containerised, scalable data infrastructure built to support advanced analytics and machine learning workloads.

Containerised Infrastructure Deployment
  • Docker and Kubernetes: Deployed containerised environments managed by Kubernetes to deliver a flexible, scalable architecture for data workloads.
  • Python-Based Applications: Integrated custom Python applications into the container ecosystem to support distributed data processing and analytics tasks.
Automated Data Pipelines
  • Real-Time Data Ingestion: Built streaming pipelines using Apache Kafka for high-throughput ingestion from multiple sources.
  • Transformation and Storage: Used Apache Spark and PostgreSQL for transforming, structuring, and storing large datasets in formats optimised for analytics and ML.
Team Enablement and Tooling
  • Training Workshops: Delivered hands-on training to the internal data team to ensure effective use and maintenance of the new platform.
  • Private Package Registry: Deployed a secure Python package registry to promote code reusability, versioning, and collaborative development.
Monitoring and Optimisation
  • Centralised Monitoring: Integrated Prometheus and Grafana dashboards for visibility into infrastructure, data pipelines, and application performance.
  • Database Optimisation: Applied best practices for PostgreSQL tuning and maintenance to improve query speed and ensure data integrity.

Results

The new platform transformed the client’s data operations and laid the foundation for ongoing machine learning innovation.
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Faster Data Processing

The optimised infrastructure reduced processing times by 50%, accelerating time-to-insight across analytics workflows.

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Scalable, Flexible Architecture

Containerisation enabled dynamic resource allocation, allowing the platform to scale in line with growing data volumes and new initiatives.

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Stronger Data Capabilities

Training and tooling empowered the internal data team to build, maintain, and iterate on machine learning solutions independently.

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Better Decision-Making

Improved access to timely, accurate data enabled more informed, operational decisions, delivering efficiency gains across departments.

Technology Stack


To implement a scalable and ML-ready analytics platform, the following technologies were used:

  • Containerisation & Orchestration: Docker, Kubernetes
  • Programming & Data Processing: Python
  • Data Pipelines & Storage: Apache Kafka, Apache Spark, PostgreSQL
  • Monitoring & Optimisation: Prometheus, Grafana
By adopting this containerised big data architecture, the client now operates with a high-performance, flexible foundation for advanced analytics and machine learning at scale.


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