Scalable Casino Warehouse Cutting ETL Costs by 90%

About Client

The client is an award-winning online gaming group based in Malta, operating multiple brands within a regulated casino enterprise environment. The organization manages high transaction volumes across gaming platforms, player engagement systems, and compliance workflows, making data reliability critical to daily operations.

Background

A fundamental part of the client’s data-first approach was ensuring data acted as a single source of truth for the entire casino enterprise. This ensured transparency, readability, higher reliability, and more valuable insights across business, risk, and marketing teams.

However, building a centralized casino warehouse introduced challenges across architecture design, data governance, and master data management. While some issues were technical, many stemmed from business-level decisions that limited scalability and slowed insight generation.

Challenge

The client’s existing data architecture posed serious performance and cost risks:

  • The platform was not scalable enough to support the growing needs of a multi-brand casino enterprise
  • Data was managed inconsistently, making processing slow and operationally expensive
  • High overall costs were incurred to maintain a complex and fragmented system

These limitations prevented the organization from supporting modern analytics and emerging confluent gambling use cases, such as cross-brand player analytics and consolidated reporting.

The Objective

Build a scalable Enterprise Data Warehouse that would:

  • Speed up processing time and ensure no query failures
  • Implement a standard data framework across the casino enterprise
  • Consolidate multi-brand data into a centralized casino warehouse for easier extraction and analysis
  • Support future growth and advanced confluent gambling use cases without re-architecting the platform

Our Solution

  • Meetings were held with all the stakeholders from the client’s side to decide on a single definition of KPIs and variables to be used across the organization. The process resolves the data consistency issues.
  • A logical data model was designed along with data governance policies, which included- same KPI definitions across the organization, improving user access control, and data security measures.
  • The EDW model was built in Amazon Redshift using the data from their earlier data lake (Hive with storage as S3) as the source.
  • AWS Glue was used for ETL jobs and Apache Airflow for orchestrating the Glue Jobs. Redshift Spectrum was used to read data from S3, such that it bypasses Hive which was partly responsible for slowing down the queries.
  • Python Shell Glue Jobs was deemed to be the optimal solution to call Redshift stored procedures; to both load data from source to the warehouse as well as to process data in the warehouse. This reduced query run times and operational cost significantly compared to the conventional Spark jobs.

Outcome

  • A Scalable Enterprise Data Warehouse was built.
  • A standard data framework was built to consolidate multi-brand data for enterprise wide data consistency with high processing speed.

a. Any future brand acquisitions will need minimal integration effort.

The total annual costs incurred for ETL was reduced by

90.44%
Owing to the usage of Redshift Spectrum and Python Shell Glue jobs

The Enterprise Data Warehouse was built in a record

4 months
For the client who needed it urgently.

The complete historical data from the client’s largest table with 1.3Tb compressed size, was loaded into the EDW within

48 hours

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