AWS Database Migration Service (DMS) is a fully managed service that helps migrate databases quickly and securely to AWS. It supports homogeneous (same database engine) and heterogeneous (different database engines) migrations with minimal downtime.
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project2026-03-03
The article details Yggdrasil Gaming's migration from Google BigQuery to an AWS-based lakehouse architecture, highlighting the challenges faced due to multi-cloud operational complexity and the need for a scalable analytics foundation. It outlines the phased approach taken to establish a new architecture using AWS services, including Amazon S3, Apache Iceberg, and Amazon Athena, which enabled real-time data ingestion and advanced analytics capabilities. The migration resulted in significant cost savings, improved data freshness, and enhanced governance for analytics workloads. The article serves as a case study for organizations looking to modernize their data architecture.
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tech12025-12-30
The article discusses the author's approach to structuring data pipelines by integrating the medallion architecture, Kimball dimensional modeling, and semantic layers. It emphasizes the importance of defining clear roles and outputs for each layer—Bronze, Silver, and Gold—to cater to different user needs. The author argues for making the semantic layer a first-class priority in data architecture, highlighting its role in providing governed metrics for self-service analytics. The article concludes with a concrete example of how marketing attribution data flows through this architecture.
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project2026-01-30
The article outlines Halodoc's comprehensive approach to data validation within a Lakehouse architecture, emphasizing the importance of data accuracy and reliability. It describes a multi-layered validation strategy that employs AI to enhance data quality checks at various stages of the data pipeline. The validation layers include checks for data consistency, structural correctness, business correctness, and reconciliation, ensuring that data remains trustworthy throughout its journey. The implementation of this strategy has led to reduced data incidents and increased trust among analytics and product teams.
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vision2025-11-17
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project2025-11-13
The article discusses Yelp's transformation of its data infrastructure through the adoption of a streaming lakehouse architecture on AWS. This modernization aimed to address challenges related to data processing latency, operational complexity, and compliance with regulations like GDPR. By migrating from self-managed Apache Kafka to Amazon MSK and implementing Apache Paimon for storage, Yelp achieved significant improvements, reducing analytics data latencies from 18 hours to minutes and cutting storage costs by over 80%. The article outlines the architectural shifts and technologies involved in this transformation.
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product2024-12-10
The validation and remediation are interesting.
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