The new data platform implemented an ELT stack, focusing on cost-effective raw data ingestion and in-warehouse transformations. Key components included Debezium for real-time data replication, Airflow for orchestrating data ingestion, and Kafka for streaming data pipelines. Data storage was optimized using S3 and Parquet format, while dbt and Great Expectations ensured data transformation and quality. The platform adopted a medallion architecture to organize data for optimal consumption, with AWS Glue and Trino facilitating efficient data discovery and querying. This new architecture significantly reduced infrastructure costs from approximately $2,200 to $460 monthly by maximizing existing infrastructure and moving away from expensive managed services.
Key takeaways:
- Building a scalable and cost-effective data platform requires strategic use of existing infrastructure and transitioning away from expensive managed services.
- Implementing an ELT stack with a focus on raw data ingestion and in-warehouse transformations can optimize data processing and storage costs.
- Utilizing a medallion architecture (Bronze, Silver, Gold) helps organize data for optimal consumption and improves query performance.
- Integrating tools like Trino, Metabase, and AWS Glue enhances data querying, visualization, and discovery, empowering data-driven decision-making.