Source URL: https://jchandra.com/posts/data-infra/
Source: Hacker News
Title: We built a Modern Data Stack from scratch and reduced our bill by 70%
Feedly Summary: Comments
AI Summary and Description: Yes
Summary: The text offers valuable insights into building a scalable and cost-effective data platform within a fintech startup. It describes the challenges faced in data management and the strategies adopted to optimize costs and enhance data processing capabilities, making it particularly relevant for professionals in infrastructure and data management.
Detailed Description:
The article provides a comprehensive overview of the transition from an initial data platform to a more scalable and cost-efficient architecture within a fintech startup. This transformation was crucial as the organization sought to manage an increasing volume and variety of financial data from multiple sources.
Key Points:
– **Initial Challenges**:
– The startup faced a variety of data sources, including structured and semi-structured data, which complicated data management.
– The previous platform, primarily built with Hevo, could not adequately handle scaling, leading to increased costs due to inefficient queries and database load.
– **New Data Platform Development**:
– The introduction of an ELT (Extract, Load, Transform) stack that focused on raw data ingestion first and then conducting transformations within the warehouse.
– Use of cost-effective storage solutions, with raw data stored in S3 in Parquet format to optimize query performance.
– **Technological Components**:
– **Data Ingestion Layer**: Implemented tools like Debezium for real-time data replication, Airflow for orchestration, and Kafka for setting up a streaming data pipeline.
– **Storage and Compute Layer**: Data was stored efficiently, with mechanisms in place for quality checks through integrations like Great Expectations.
– **Medallion Architecture**:
– This structure organizes data into Bronze (raw data), Silver (cleaned/processed data), and Gold (business-ready datasets), enhancing data accessibility and query performance.
– **Cost Reduction Strategy**:
– Significant cost savings were achieved by optimizing existing infrastructure and moving away from expensive managed services. Monthly expenses dropped drastically from around $2,200 to approximately $460.
– **Data Discovery and Visualization**:
– The integration of AWS Glue for metadata management and Trino for federated queries allowed for efficient data discovery across the data lake. Metabase enhanced analytics through user-friendly visualization tools.
Overall, this detailed analysis illustrates the importance of an adaptable and strategic approach to data management, particularly for organizations looking to enhance their operational efficiency while minimizing costs. The insights provided can benefit security, compliance, and infrastructure professionals by showcasing best practices that can be implemented in diverse organizational contexts.