This can be a visitor publish by Edijs Drezovs, CEO and Founding father of GOStack, Viesturs Kols, Knowledge Architect at GOStack, and Krisjanis Beitans, Senior Knowledge Engineer at GOStack, in partnership with AWS.
Yggdrasil Gaming develops and publishes on line casino video games globally, processing huge quantities of real-time gaming knowledge for sport efficiency analytics, participant conduct insights, and business intelligence. As Yggdrasil’s system grew, managing dual-cloud environments created operational overhead and restricted their skill to implement superior analytics initiatives. This problem grew to become crucial forward of the launch of the Sport in a Field answer on AWS Market, which generates will increase in knowledge quantity and complexity.
Yggdrasil Gaming diminished multi-cloud complexity and constructed a scalable analytics basis by migrating from Google BigQuery to AWS analytics providers. On this publish, you’ll uncover how Yggdrasil Gaming remodeled their knowledge structure to satisfy rising enterprise calls for. You’ll be taught sensible methods for migrating from proprietary techniques to open desk codecs reminiscent of Apache Iceberg whereas sustaining enterprise continuity.
Yggdrasil labored with GOStack, an AWS Accomplice, emigrate to an Apache Iceberg-based lakehouse structure. The migration helped cut back operational complexity and enabled real-time gaming analytics and machine studying.
Challenges
Yggdrasil confronted a number of crucial challenges that prompted their migration to AWS:
- Multi-cloud operational complexity: Managing infrastructure throughout AWS and Google Cloud created important operational overhead, lowering agility and rising upkeep prices. The info crew needed to preserve experience in each environments and coordinate knowledge motion between clouds.
- Structure limitations: The prevailing setup couldn’t successfully help superior analytics and AI initiatives. Extra critically, the launch of Yggdrasil’s Sport in a Field answer required a modernized, scalable knowledge setting able to dealing with elevated knowledge volumes and enabling superior analytics.
- Scalability constraints: The structure lacked the unified knowledge basis with open requirements and automation required to scale effectively. As knowledge volumes grew, prices elevated proportionally, and the crew wanted an setting designed for contemporary analytics at scale.
Resolution overview
Yggdrasil labored with GOStack, an AWS APN companion, to design their new lakehouse structure. The next diagram reveals the excessive stage overview of this structure.
Yggdrasil efficiently migrated from Google BigQuery to a knowledge lakehouse structure utilizing Amazon Athena, Amazon EMR, Amazon Easy Storage Service (Amazon S3), AWS Glue Knowledge Catalog, AWS Lake Formation, Amazon Elastic Kubernetes Service (Amazon EKS) and AWS Lambda. Their strategic method goals to scale back multi-cloud complexity whereas constructing a scalable basis for his or her Sport in a Field answer and particular AI/ML initiatives like personalised sport suggestions and fraud detection.
The mix of Amazon S3, Apache Iceberg, and Amazon Athena allowed Yggdrasil to maneuver away from provisioned, always-on compute fashions. The Amazon Athena pay-per-query pricing expenses just for knowledge scanned, eradicating idle compute prices throughout off-peak intervals. Inside price modeling carried out throughout the analysis part indicated that this structure may cut back analytics system prices by 30–50% in comparison with compute-based warehouse pricing fashions of different options, notably for bursty workloads pushed by sport launches, tournaments, and seasonal site visitors. By adopting AWS-native analytics providers, Yggdrasil diminished operational complexity via native integration with AWS Identification and Entry Administration (AWS IAM), Amazon EKS, and AWS Lambda, serving to simplify safety, governance, and automation throughout the analytics system.
The answer facilities on a contemporary lakehouse structure constructed on Amazon S3, which gives sturdy and cost-efficient storage for Iceberg tables in Apache Parquet format. Apache Iceberg desk format gives ACID transactions, schema evolution, and time journey capabilities whereas sustaining an open normal. AWS Glue Knowledge Catalog serves because the central technical metadata repository, whereas Amazon Athena acts because the serverless question engine utilized by dbt-athena and for ad-hoc knowledge exploration. Amazon EMR runs Yggdrasil’s legacy Apache Spark software in a totally managed setting, and AWS Lake Formation gives centralized safety and governance for knowledge lakes, permitting fine-grained entry management at database, desk, column, and row ranges.
The migration adopted a phased method:
- Set up lakehouse basis – Arrange Apache Iceberg-based structure with Amazon S3 with AWS Glue Knowledge Catalog
- Implement real-time knowledge ingestion – Deploy Debezium connectors for real-time change knowledge seize from EKS and Google Kubernetes Engine (GKE) clusters
- Migrate processing pipelines – Re-system ETL pipelines utilizing AWS Lambda, and legacy knowledge functions re-systemed on Amazon EMR
- Modernizing the transformation layer – Implement dbt with Amazon Athena for modular, reusable fashions
- Allow governance – Configure AWS Lake Formation for complete knowledge governance
Set up lakehouse basis
The primary part of the migration centered on constructing a strong basis for the brand new knowledge lakehouse structure on AWS. The purpose was to create a scalable, safe, and cost-efficient setting that might help analytical workloads with open knowledge codecs and serverless question capabilities.
GOStack provisioned an Amazon S3-based knowledge lake because the central storage layer, offering nearly limitless scalability and fine-grained price management. This storage-compute separation allows groups to decouple ingestion, transformation, and analytics processes, with every element scaling independently utilizing essentially the most applicable compute engine.
To ascertain dataset interoperability and discoverability, the crew adopted AWS Glue Knowledge Catalog because the unified metadata repository. The catalog shops Iceberg desk definitions and makes schemas accessible throughout providers reminiscent of Amazon Athena and Apache Spark workloads on Amazon EMR. Most datasets, each batch and streaming, are registered right here, enabling constant metadata visibility throughout the lakehouse.
The info is saved in Apache Iceberg tables on Amazon S3, chosen for its open desk format, ACID transaction help, and highly effective schema evolution options. Yggdrasil required ACID transactions for constant monetary reporting and fraud detection, schema evolution to accommodate quickly altering gaming knowledge fashions, and time journey queries to align with regulatory audit necessities.
GOStack constructed a customized schema conversion and desk registration service. This inner instrument converts source-system Avro schemas into Iceberg desk definitions and manages the creation and evolution of raw-layer tables. By controlling schema translation and desk registration straight, the crew makes positive that metadata stays per the supply techniques and gives predictable, versioned schema evolution aligned with ingestion wants.
The preliminary setup made the next parts:
- Amazon S3 bucket construction design: Applied a multi-layer structure (uncooked, curated, and analytics zones) aligned with knowledge lifecycle greatest practices.
- AWS Glue Knowledge Catalog integration: Outlined database and desk schemas with partitioning methods optimized for Athena efficiency.
- Iceberg configuration: Enabled versioning and metadata retention insurance policies to steadiness storage effectivity and question flexibility.
- Safety and compliance: Configured encryption at relaxation utilizing AWS Key Administration Service (AWS KMS), helped implement entry controls through AWS IAM and Lake Formation, and carried out Amazon S3 bucket insurance policies following the precept of least privilege.
The redesign of the earlier GCP setup helped ship price-performance enhancements. Yggdrasil diminished ingestion and processing prices by roughly 60% whereas additionally reducing operational overhead via a extra direct, event-driven pipeline.
Implement real-time knowledge ingestion
After establishing the lakehouse structure, the following step centered on enabling real-time knowledge ingestion from Yggdrasil’s operational databases into the uncooked knowledge layer of the lakehouse. The target was to seize and ship transactional adjustments as they happen, ensuring that downstream analytics and reporting replicate essentially the most up-to-date data.
To realize this, GOStack deployed Debezium Server Iceberg, an open-source challenge that integrates change knowledge seize (CDC) straight with Apache Iceberg tables. It was deployed as Argo CD functions on Amazon EKS and used Argo’s GitOps-based mannequin for reproducibility, scalability, and seamless rollouts.
This structure gives an environment friendly ingestion pathway – streaming knowledge adjustments straight from the supply system’s outbox tables into the Apache Iceberg tables registered within the AWS Glue Knowledge Catalog and bodily saved on Amazon S3, bypassing the necessity for intermediate brokers or staging providers. By writing knowledge within the Iceberg desk format, the ingestion layer maintained transactional ensures and rapid question availability via Amazon Athena.
As a result of Yggdrasil’s supply techniques emitted outbox occasions containing Avro information, the crew carried out a customized outbox-to-Avro transformation inside Debezium. The outbox desk saved two key parts:
- The Avro schema definition
- The JSON-encoded payload of every document
The customized transformation module mixed these components into legitimate Avro information earlier than persisting them into the goal Iceberg tables. This method preserved schema constancy and verified compatibility with downstream processing instruments.
To dynamically route incoming change occasions, the crew leveraged Debezium’s occasion router configuration. Every document was routed to the suitable Apache Iceberg desk (backed by Amazon S3) primarily based on subject and metadata guidelines, whereas desk schemas and partitioning had been ruled on the AWS Glue facet to keep up stability and alignment with the lakehouse’s knowledge group requirements.
This setup helped ship low-latency ingestion with end-to-end streaming from database outbox to S3-based Iceberg tables in close to actual time. The crew managed operations finish to finish on Amazon EKS utilizing Helm charts deployed through Argo CD in a GitOps mannequin for totally declarative, version-controlled operations. ACID-compliant Iceberg writes verified that partially written knowledge couldn’t corrupt downstream analytics. The modular transformation logic allowed future growth to new supply techniques or occasion codecs with out rearchitecting the ingestion pipeline.
This Debezium Server answer gives quick, real-time knowledge ingestion. GOStack considers it an interim structure. In the long run, the ingestion pipeline will evolve to make use of Amazon Managed Streaming for Apache Kafka (Amazon MSK) because the central occasion spine. Debezium connectors will act as producers, publishing change occasions to Apache Kafka matters, whereas Apache Flink functions will eat, course of, and write knowledge into Iceberg tables.
This deliberate evolution towards a Kafka-based streaming structure verifies Yggdrasil’s lakehouse stays not solely scalable and cost-efficient right now, but in addition future-ready – able to supporting richer streaming analytics and broader knowledge integration eventualities because the group grows.
Migrate processing pipelines
As soon as real-time knowledge ingestion was established, GOStack turned its focus to modernizing the info transformation layer. The purpose was to simplify the transformation logic, cut back operational overhead, and unify the orchestration of analytical workloads throughout the new AWS-based lakehouse.
GOStack adopted a lift-and-shift method for a few of Yggdrasil’s knowledge pipelines to help a quick and low-risk transition away from GCP. The light-weight Cloud Run features that beforehand dealt with extraction duties – pulling knowledge from file shares, SharePoint, Google Sheets, and numerous third-party APIs – had been re-implemented utilizing AWS Lambda. These Lambda features now combine with the identical exterior techniques and write knowledge straight into Iceberg tables.
For extra complicated processing, earlier Apache Spark functions working on Dataproc had been migrated to Amazon EMR with minimal code adjustments. This allowed it to protect the prevailing transformation logic whereas benefiting from the managed scaling capabilities of EMR and improved price management on AWS.
Over time, these processes might be steadily refactored and consolidated into containerized workflows on the EKS cluster, totally orchestrated by Argo Workflows. This phased migration permits Yggdrasil to maneuver workloads to AWS shortly and decommission GCP sources sooner, whereas nonetheless leaving room for steady enchancment and modernization of the info system over time.
Lastly, a variety of analytical transformations that beforehand lived as BigQuery saved procedures and scheduled queries, that had been now rebuilt as modular dbt fashions executed with dbt-athena. This shift made transformation logic extra clear, maintainable, and version-controlled, bettering each developer expertise and long-term governance.
Modernizing the transformation layer
With the ingestion pipelines migrated to AWS, GOStack turned its focus to simplifying and modernizing Yggdrasil’s analytical transformations. Quite than replicating the earlier stored-procedure–pushed method, the crew rebuilt the transformation layer utilizing dbt to assist enhance maintainability, lineage visibility, orchestration, and long-term governance.As a part of this redesign, a number of knowledge fashions had been reshaped to suit the brand new lakehouse structure. Probably the most important effort concerned rewriting a crucial Spark-based monetary transformation right into a set of SQL-driven dbt fashions. This shift not solely aligned the logic with the lakehouse design but in addition eliminated the necessity for long-running Spark clusters, serving to generate operational and value financial savings.For the curated knowledge layers, changing the legacy warehouse, GOStack consolidated quite a few scheduled queries and saved procedures into structured dbt fashions. This gives standardized, version-controlled transformations and clear lineage throughout the analytical stack.
Orchestration was simplified as effectively. Beforehand, coordination was cut up between Apache Airflow for Spark workloads and scheduled queries analytical transformations, creating operational friction and dependency dangers. Within the new structure, Argo Workflows on Amazon EKS orchestrates dbt fashions centrally, consolidating the transformation logic inside a single workflow engine. Whereas most transformations nonetheless run on time-based schedules right now, the system now helps event-driven execution via Argo Occasions, giving the chance to progressively undertake trigger-based workflows because the transformation layer evolves.
This unified orchestration framework can carry a number of advantages:
- Consistency: One orchestration layer for knowledge workflows throughout ingestion and transformation.
- Automation: Occasion-driven dbt runs assist take away handbook scheduling and cut back operational overhead.
- Scalability: Argo Workflows scales with the EKS cluster, dealing with concurrent dbt jobs seamlessly.
- Observability: Centralized logging and workflow visualization assist enhance visibility into job dependencies and knowledge freshness.
Via this transformation, Yggdrasil efficiently unified its knowledge lakes and warehouses into a contemporary lakehouse structure, powered by open knowledge codecs, serverless question engines, and modular transformation logic. The transfer to dbt and Athena not solely simplified operations but in addition helped pave the best way for quicker iteration, less complicated governance, and better developer productiveness throughout the info setting.
Lakehouse efficiency optimizations
Whereas efficiency tuning is an ongoing journey, as a part of the transformation redesign, GOStack made few performance-oriented tweaks to verify Athena queries might be quick and cost-efficient. The Apache Iceberg tables had been saved in Parquet with ZSTD compression, offering sturdy learn efficiency and lowering the quantity of knowledge scanned by Athena.
Partitioning methods had been additionally aligned to precise entry patterns utilizing Iceberg’s native partitioning. Uncooked knowledge zones had been partitioned by ingestion timestamp, enabling environment friendly incremental processing. Curated knowledge used business-driven partition keys, reminiscent of participant or sport identifiers and date dimensions, to assist optimize analytical queries. These designs made positive Athena may prune unneeded knowledge and constantly scan solely the related partitions.
Iceberg’s native partitioning options, together with transforms reminiscent of bucketing and time slicing, change conventional Hive partitioning patterns. As a result of Iceberg manages partitions internally in its metadata layer, not all Glue or Athena partition constructs apply. Counting on Iceberg’s native partitioning helps present predictable pruning and constant efficiency throughout the lakehouse with out introducing legacy Hive behaviors.
To deal with the excessive quantity of small information produced by real-time ingestion, GOStack enabled AWS Glue Iceberg compaction. This routinely merges small Parquet information into bigger segments, serving to enhance question efficiency and cut back metadata overhead with out handbook intervention.
Allow governance
The crew adopted AWS Lake Formation as the first governance layer for the curated zone of the lakehouse, leveraging Lake Formation hybrid entry mode to handle fine-grained permissions alongside current IAM-based entry patterns. This hybrid mode gives an incremental and versatile pathway to undertake Lake Formation with out forcing a full migration of legacy permissions or inner pipeline roles, making it an excellent match for Yggdrasil’s phased modernization technique.
Lake Formation presents centralized authorization, supporting database, desk, column, and, critically for Yggdrasil, row-level permissions. These capabilities are important due to the corporate’s multi-tenant working mannequin:
- Sport improvement companions require entry to knowledge and studies pertaining solely to their very own video games, facilitating each safety and compliance alignment with companion agreements.
- iGaming operators integrating with Yggdrasil’s system should obtain operational and monetary insights solely for their very own knowledge, enforced routinely via reporting instruments backed by curated Iceberg tables.
With Lake Formation hybrid entry mode, tenant-specific row-level entry insurance policies are constantly enforced throughout Amazon Athena, AWS Glue, and Amazon EMR, with out introducing breaking adjustments to current IAM-based workloads. This allowed Yggdrasil to implement sturdy governance for exterior customers whereas retaining inner operations steady and predictable.
Internally, Lake Formation can be used to grant the Analytics crew and BI instruments focused entry to curated datasets, easy however centrally managed to keep up consistency and cut back administrative overhead.
For ingestion and transformation workloads, the crew continues to depend on IAM roles and insurance policies. Providers reminiscent of Debezium, dbt, and Argo Workflows require broad however managed entry to uncooked and intermediate storage layers, and IAM gives a simple, least-privilege mechanism for granting these permissions with out involving Lake Formation within the inner pipeline path.
By adopting Lake Formation in hybrid entry mode and mixing it with IAM for inner providers, Yggdrasil established a governance mannequin that may steadiness sturdy safety with operational flexibility – enabling the lakehouse to scale securely because the enterprise grows.
Outcomes and enterprise affect
The brand new lakehouse, constructed on Amazon Athena, Amazon S3, and AWS Glue Knowledge Catalog, now underpins superior analytics and AI/ML use circumstances reminiscent of participant conduct modeling, predictive sport suggestions, and fraud detection.
The optimized lakehouse design permits Yggdrasil to quickly onboard new analytics workloads and enterprise use circumstances, serving to ship measurable outcomes:
- Decreased operational complexity via consolidation on AWS analytics providers
- Value optimization with a 60% discount in knowledge processing prices
- Improved knowledge freshness with 75% decrease latency for analytics outcomes (from 2 hours to half-hour)
- Enhanced governance utilizing the AWS Lake Formation fine-grained controls
- Future-ready structure leveraging open codecs and serverless analytics
Conclusion
Yggdrasil Gaming’s migration journey illustrates how organizations can efficiently transition from proprietary analytics techniques to an open, versatile lakehouse structure. By following a phased method guided by AWS Effectively-Architected Framework ideas, Yggdrasil maintained enterprise continuity whereas establishing a contemporary basis for his or her knowledge wants.
Primarily based on this expertise, a number of classes emerged to assist information your individual transfer to an AWS-based lakehouse:
- Assess your present state: Establish ache factors in your current knowledge structure and set up clear aims for modernization.
- Begin small: Start with a pilot challenge utilizing AWS analytics providers to validate the lakehouse method in your particular use circumstances.
- Design for openness: Leverage open desk codecs like Apache Iceberg to keep up flexibility and keep away from vendor lock-in.
- Implement steadily: Observe a phased migration technique much like Yggdrasil’s, prioritizing high-value workloads.
- Optimize constantly: Use efficiency tuning methods for Amazon Athena to assist maximize effectivity and decrease prices.
To be taught extra about constructing fashionable lakehouse architectures, confer with “The lakehouse structure of Amazon SageMaker”.
In regards to the authors
