Is a data lake a replacement for a data warehouse?

Not usually. A lake is cheap, flexible storage for all data, and a warehouse is optimized for fast, governed reporting. Lakehouse formats blur the line, but most organizations still use both for different jobs.

Building a Data Lake Module 1: What a Data Lake Is and How It Is Organized

Introduction

A data lake is a central store that holds large amounts of data in its original form, cheaply, until someone needs it. Unlike a database, it does not force a structure up front. This first module of the seven-module Building a Data Lake course explains what a lake is, how it differs from a warehouse, and the layered design that keeps one organized.

Prerequisites

Basic SQL and cloud storage concepts

Step-by-Step Guide

Step 1: Know the three kinds of data store

A data warehouse stores cleaned, structured data in tables designed for fast reporting. A data lake stores any data (files, logs, images, JSON) cheaply on object storage and applies structure when the data is read. A lakehouse adds table features, such as transactions and schema enforcement, on top of lake storage so one copy of data serves both reporting and data science.

Warehouse   structured tables, schema first, fast SQL, higher cost per TB
Lake        any file format, schema when reading, cheap object storage
Lakehouse   lake storage + table formats (Iceberg, Delta, Hudi) = SQL tables on files

Step 2: Decide when you need a lake

A lake helps when you have many data sources, large or varied data (logs, events, documents), data scientists who need raw data, or a need to keep history cheaply. It is a poor fit for a small company with a handful of clean tables: a managed database or warehouse is simpler. Many teams use a lake for storage and a warehouse for the polished reporting layer.

Step 3: Organize data in layers

The most common design uses zones, often called the medallion architecture. Raw (bronze) data is stored exactly as it arrived and never changed, so you can always reprocess it. Clean (silver) data is validated, deduplicated, and converted to an efficient format. Curated (gold) data is shaped for specific uses such as dashboards and models.

Sources --> RAW (bronze)  --> CLEAN (silver)  --> CURATED (gold) --> BI, ML, apps
            as received       validated, typed,    business-ready
            never edited      deduplicated         aggregates

Step 4: See the building blocks of a lake on AWS

Amazon S3 provides the storage. AWS Glue provides ingestion and transformation jobs and the Data Catalog, which records which tables exist and where. Amazon Athena queries data in place with SQL. AWS Lake Formation and IAM control access. Other clouds offer equivalents: Azure Data Lake Storage with Synapse, and Google Cloud Storage with BigQuery.

Storage        Amazon S3
Catalog        AWS Glue Data Catalog
Processing     AWS Glue (Spark), Amazon EMR, Athena
Query          Amazon Athena, Amazon Redshift Spectrum
Security       IAM, AWS Lake Formation, AWS KMS
Streaming in   Amazon Kinesis Data Firehose, Amazon MSK

Step 5: Avoid the data swamp

A lake without ownership, naming rules, a catalog, and quality checks turns into a data swamp: nobody knows what files mean or whether to trust them. Prevent it from day one with consistent folder names, a catalog entry for every dataset, an owner for each source, documented schemas, and automated quality checks. The remaining modules build these habits in.

FAQs

Is a data lake a replacement for a data warehouse?: Not usually. A lake is cheap, flexible storage for all data, and a warehouse is optimized for fast, governed reporting. Lakehouse formats blur the line, but most organizations still use both for different jobs.

Summary

A data lake stores raw data cheaply and applies structure later. Organize it in raw, clean, and curated layers, use a catalog and ownership rules to avoid a data swamp, and consider a lakehouse table format for reliable SQL on files.