Data Lake is a centralized repository that stores raw structured and unstructured data at any scale.
Data lakes (S3, Azure Blob, GCS) store cheap, raw data — vs data warehouses (Snowflake, BigQuery) which store cleaned, queryable data. Modern "lakehouse" architectures (Databricks, Apache Iceberg, Delta Lake) bridge the two. By 2026, "lake-first" architectures dominate as compute decouples from storage and AI workloads need raw data.
Data lakes give organizations a single low-cost store for the messy, varied data that pre-defined warehouse schemas cannot easily accept. They are the staging ground for modern analytics and ML.
A retailer stores raw event logs, product images, transaction CSVs and customer service transcripts in cloud object storage — all in their native format — so analysts and ML teams can query whatever they need without a prior schema.
A data lake is not just "cheap storage." Without governance and cataloguing it becomes a "data swamp" — full of data, useless for analysis.
Pair a data lake with a metadata catalogue (Unity Catalog, AWS Glue) from day one; trying to add governance later is far harder than adding it up front.
Data Lake falls under the Data category.
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