If you’re building a modern analytics stack, you’ll quickly run into two big terms: data lake and data warehouse. Both store data for analysis, but they were built for different purposes, and selecting the right one (or combining them) determines how quickly you can turn raw data into informed decisions.

What is a data lake? A data lake is a low-cost repository that stores all kinds of data, including structured tables, semi-structured logs (JSON), and unstructured files (images, PDFs), usually in cloud object storage. It uses schema-on-read, meaning you define the structure only when you analyze the data. That makes lakes ideal for large-scale ingestion, data science, and AI/ML exploration. To understand how this aligns with a broader analytics program, explore NuSummit’s Data & Analytics capabilities.

What is a data warehouse? A data warehouse stores curated, structured data modeled for analytics and BI dashboards. It relies on schema-on-write: data is cleaned and modeled before loading (ETL/ELT), which yields consistent performance for reporting, finance, and compliance. If you’re new to analytics fundamentals, this primer is a good starting point:

What is Data Analytics? Learn more.