Digital Lexicon Hub

A repository designed to store large volumes of raw or lightly processed structured, semi-structured and unstructured data.

A data lake lets an organization keep large volumes of raw data from many sources at low storage cost, so analytics and machine learning teams can use it later without designing every schema up front.

A retailer lands daily clickstream files, POS exports and supplier PDFs in Amazon S3, catalogs them with AWS Glue and lets analysts query the clickstream folder with Athena while data scientists train demand models on the same files.

Without cataloging, ownership and quality rules, lakes become hard to search and trust. Raw storage lacks built-in transactions and fine-grained governance, and querying files directly can be slower and less consistent than a warehouse.

Data lakes use object storage such as Amazon S3, Azure Data Lake Storage or Google Cloud Storage and apply schema-on-read: structure is applied when data is queried rather than when it is written. Data is often organized into raw, cleaned and curated zones in open formats such as Parquet, with a metadata catalog and separate compute engines like Spark or SQL query services.