Delphi | Leading Entertainment & Theme Park Operator – IBM Data Landscape Migration to Azure

Leading Entertainment & Theme Park Operator – IBM Data Landscape Migration to Azure

Entertainment & Hospitality

Middle East (UAE)

Azure Data Factory, Azure Data Lake, Databricks, Azure Synapse, Power BI

IBM Data Landscape Migration to Azure

The client aimed to migrate from its current 3-tier IBM BI analytics architecture to a modern, end-to-end BI analytics landscape using Microsoft Azure infrastructure. Source systems spanned Admissions, Ticketing, F&B, and Revenue functions across multiple parks.

Identifying the unique obstacles that hinder progress and uncovering the root causes behind complex business problems.

The existing IBM data landscape lacked automation in data flow, creating significant manual effort and operational inefficiency in data processing and reporting.

A complex data flow involving multiple file servers and staging tables made the architecture difficult to maintain, scale, and troubleshoot effectively.

Multiple staging tables and views were in use across the architecture, increasing data processing complexity and reducing overall system performance.

Netezza was approaching end-of-support, creating an urgent need to migrate historical data and modernise the data infrastructure before support cessation.

Integrating data from a large number of source systems — including CRM, ticketing, F&B, and revenue platforms — required a comprehensive and coordinated migration approach.

Developing strategic, innovative solutions designed to address each challenge with precision, ensuring measurable impact and long-term success.

Migrated historical data from Netezza to Azure Data Lake Storage, preserving all historical context and ensuring continuity of reporting across Admissions, Ticketing, F&B, and Revenue.

Used Azure Data Factory to integrate multiple source file systems and create ETL pipelines for ingesting data into Azure Data Lake Storage in a structured and automated manner.

Applied data transformations and preparations using Databricks, then loaded transformed data into Azure Synapse Analytics for enterprise-grade data warehousing.

Developed Power BI dashboards for data visualisation, providing leadership with self-service, interactive insights across Admissions, Ticketing, F&B, and Revenue functions.

Orchestrated end-to-end data flow using Azure Data Factory pipelines, with parameterisation jobs and automated scheduling across all transformation and reporting workloads.

Delivering tangible results that drive operational efficiency, data trust, and sustained business value for the organisation.

Azure-based cloud services enabled unlimited scalability, allowing the client to process and analyse large volumes of data from multiple parks quickly and efficiently.

Cloud-based services reduced hardware and software costs, as the client now pays only for resources consumed rather than maintaining on-premises infrastructure.

Migrating from IBM to Azure modernised data management and analytics capabilities, enabling the client to stay competitive in an increasingly data-driven business environment.

Improved productivity was achieved as the new tools offered more intuitive interfaces and better automation capabilities compared to the legacy IBM architecture.

Azure Data Factory, Azure Synapse, and Power BI integration enabled end-to-end data solutions covering ingestion, processing, and analysis across all park operations.

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