Financial data architecture dictates how financial information is captured, structured, and aggregated for analysis. Modern architecture transitions corporations from legacy, siloed databases to unified data repositories.
Data Warehouses vs. Data Lakes vs. Lakehouses
- Data Warehouses: Centralized repositories storing highly structured, relational data optimized for fast SQL querying. They rely heavily on structured schemas (Schema-on-Write) and support core corporate accounting and financial reporting.
- Data Lakes: Repositories designed to store vast amounts of raw, unstructured, semi-structured, and structured data (Schema-on-Read). They ingest operational logs, clickstreams, and external market feeds without prior transformation.
- Data Lakehouses: A hybrid architecture combining the file storage flexibility and low cost of data lakes with the ACID transactions, data governance, and schema enforcement of data warehouses.
Enterprise Data Pipeline Architecture
[Raw Sources] ──> [Ingestion (ELT/ETL)] ──> [Data Lakehouse] ──> [BI / Analytics Engines]
Relational vs. Non-Relational Financial Schemas
- Relational Databases (SQL): Utilize star and snowflake schemas. Transactions are recorded across normalized tables linked by foreign keys to guarantee transactional integrity (ACID compliance).
- Non-Relational Databases (NoSQL): Store high-frequency trading data, unstructured loan documents, or complex portfolio hierarchies using document (JSON), key-value, or graph databases.