Tipico Facilitates Faster Data Access with a Modern Data Strategy on AWS

Founded in 2004, Tipico is an international provider of digital mobile and in-person gaming and is one of the leading sports betting websites in Germany. “We wanted to become a data-driven organization and use data to power our products,” says Jimmy Borg, head of data at Tipico. “To satisfy our ambitions, we needed to make it simpler for departments to make decisions with data, and to accomplish this, we needed scalability, more data touchpoints, and comprehensive data warehousing.”

This new, diverse data would provide valuable insights, but Tipico needed a modern data strategy and architecture to efficiently process and analyze it. To accomplish this, the company migrated from an on-premises MySQL data warehouse to a structured data warehouse on Amazon Redshift, which provides optimal price performance for cloud data warehousing. To meet its growing data demands, Tipico then began to build a data lake on Amazon Simple Storage Service (Amazon S3), an object storage service built to retrieve any amount of data from anywhere. With this data strategy, Tipico can support near-real-time and semistructured data sources, thereby increasing the number of touchpoints and enriching its analytics capabilities.

“AWS excels in the data space,” says Jake Dalli, data infrastructure and operations team lead at Tipico. “AWS gives you the tools to build the systems, and you are free to choose and tailor the solution to exactly what you need. Throughout this journey, we continued to grow on AWS.”

The General Data Protection Regulation (GDPR) legislation that went into effect throughout Europe in 2018 changed the legal landscape for how entities manage and use their data, especially with respect to protecting the rights of data subjects.

Before the introduction of GDPR, Tipico’s data lake primarily served as a cost-effective and flexible storage solution. However, with the advent of GDPR, Tipico needed swift access to customer data and had to evolve the governance of the data lake to align with new regulations. To meet these standards, the company had to ensure that it could modify, delete, or update customer data within the required regulatory timeframes.

Tipico turned to the AWS team for guidance on the project. At the team’s suggestion, Tipico restructured its data lake using Amazon EMR Serverless, which makes it simple to run big data applications without managing clusters and servers. The company also adopted Apache Hudi, a data-management framework that facilitates record-level data management on Amazon S3.

This combination simplifies change data capture and streaming data ingestion and helps Tipico handle data protection use cases that require prompt updates and deletions. Previously, Tipico had to create custom data management and ingestion solutions to track individual changes and rewrite large datasets for just a few changes. Using Apache Hudi on Amazon EMR Serverless, Tipico can adjust the data, track transactions, and make granular changes on Amazon S3.

Tipico also implemented changes to different layers of its data stack to manage continued compliance. To meet regulatory requirements, Tipico introduced new functionalities. For example, it now uses Amazon Redshift for enterprise reporting, and adopted Amazon Redshift Spectrum—which helps businesses query data directly from files stored on Amazon S3—to seamlessly couple the data warehouse and the data lake. The data lake stack acts as an ingestion layer to its Amazon Redshift cluster and performs deduplication, which makes sure that the data is reliable and consistent. This helps Tipico perform accurate analytical queries using Amazon Redshift.

To optimize for cost and performance, Tipico upgraded its Amazon Redshift cluster to Amazon Redshift RA3 instances with managed storage, which lets users choose the number of nodes based on performance requirements and pay only for the managed storage that they use. By making this change, Tipico gained the ability to separate storage and compute, improving the scalability of its data environment. Tipico can scale both components independently of one another to meet its business needs, resulting in better price performance.

“Our upgrade to Amazon Redshift RA3 instances has opened doors in terms of data sharing and implementing a data mesh architecture,” says Dalli. “We can democratize processes throughout our business units.”

On AWS, Tipico has improved the performance of its data lake, achieving faster speeds and near-real-time updates while reducing storage costs by 75 percent. This project did more than modernize Tipico’s infrastructure—it reshaped the organization’s entire data perspective.

“On AWS, we built a more accurate data lake that we are truly confident about putting in the hands of
analysts and business users,” says Dalli. “We don’t have to worry that they will report data issues, and
we have increased confidence in our ability to meet service-level agreements.”

By harnessing the power of AWS, teams can craft unique, engaging experiences that resonate with customers on a personal level. To optimize further, the company is preparing a proof of concept to assess Amazon Redshift Serverless, which makes it simple to run analytics workloads of any size without having to manage data warehouse infrastructure.

Tipico also plans to incorporate artificial intelligence into its stack to deliver personalized customer experiences. As one of the first private beta customers for Amazon Bedrock, a fully managed service that makes foundation models from Amazon and leading AI startups available through an API, Tipico is also exploring innovative generative artificial intelligence use cases.

“Before, we couldn’t use our data to impact the customer journey. With our transformation on AWS, we can do just that,” says Borg. “I believe that we’re going to drive the future of personalization and data science forward.”