From Data Lakes to Data Warehouses: Data Architecture

In many organizations, data lives in many places. A data lake stores raw files, logs, and streaming data. A data warehouse brings together cleaned, structured data for reporting. A solid data architecture maps how data flows from source to insight, so teams can answer questions quickly and safely. This map also helps align vocabulary like customer, product, and order across teams.

The two storage styles have different design rules. A data lake often uses schema-on-read, meaning the data stays flexible until someone queries it. A data warehouse uses schema-on-write, with defined tables and constraints. This makes dashboards fast, but it requires upfront modeling and clear ownership.

Core layers to plan: Ingestion, Storage, Processing, Curation, and Access. Ingestion pulls data from apps, databases, and devices. Storage holds raw data in a lake and curated data in a warehouse. Processing cleans, transforms, and formats data. Curation adds metadata, quality checks, and data lineage. Access provides controlled views for analysts, dashboards, and apps.

Tech choices like ETL and ELT shape timing. ETL extracts, transforms, then loads. ELT loads raw data and transforms inside the target system. ELT fits modern cloud platforms where scale makes transforms cheap and fast. Choose based on data volume, latency needs, and governance requirements. A hybrid approach can work too.

Governance matters. Metadata, data catalogs, quality rules, and access controls help keep data trustworthy. Track lineage so analysts know where a value came from. Use clear naming conventions, standards, and documentation. Security and privacy rules should travel with data across lakes and warehouses.

A practical path: start with a small, business-critical dataset in a warehouse for trusted reports. Keep the rest in a lake, with automation to move and clean data on a schedule. Over time, map common subject areas into a warehouse or lakehouse model. This reduces risk and builds trust, while letting teams innovate.

Example: a retailer collects website events in a data lake. A nightly job cleans and aggregates the data into a star schema in the warehouse for sales dashboards and inventory planning. Analysts get fast answers, data quality improves, and new questions emerge. The result is a resilient, scalable data foundation that supports both reporting and analysis.

Key Takeaways

• Plan architecture with clear layers: ingestion, storage, processing, governance, and access.
• Use ETL or ELT based on needs; cloud platforms often favor ELT for scale.
• Start small with a warehouse for trusted reports, then expand to combine lake and warehouse capabilities.