What is a Data Lake? - Explanation & Meaning

Definition

A data lake is a centralized storage system that holds large amounts of raw data in its original format, whether structured, semi-structured, or unstructured. Unlike a data warehouse, data in a data lake is only structured when read (schema-on-read).

Technical explanation

Data lakes store data in object storage systems like Amazon S3, Azure Data Lake Storage, or Google Cloud Storage. The schema-on-read principle means data is stored without a predefined schema and only interpreted when read, providing maximum flexibility. Data formats like Parquet, ORC, and Avro offer efficient columnar storage and compression. Delta Lake, Apache Iceberg, and Apache Hudi add ACID transactions, schema evolution, and time-travel functionality to data lakes, creating the "data lakehouse" concept. Data catalogs like Apache Atlas or AWS Glue Data Catalog provide metadata management and data discovery. Data governance in a data lake includes access control, data quality checks, and lineage tracking. Partitioning and bucketing optimize query performance by logically organizing data. The risk of a "data swamp" arises when data is stored without governance, documentation, or quality controls.

How MG Software applies this

MG Software helps organizations set up data lake architectures when they want to centralize large amounts of diverse data for analysis, machine learning, or reporting. We design data lake solutions with clear governance, data quality controls, and efficient partitioning strategies. We also advise clients on choosing between a data lake, data warehouse, or data lakehouse based on their specific needs.

Practical examples

• A media company storing unstructured data like videos, images, and article text in a data lake on S3, after which machine learning models automatically tag and classify the content.
• An insurance company implementing a data lakehouse with Delta Lake, combining raw claims data, policy data, and external datasets for fraud detection and actuarial analyses.
• An IoT platform storing millions of sensor readings per day in a data lake with Parquet format and date-based partitioning, enabling analysts to efficiently analyze historical patterns.

Related terms