What's the difference between a data lake and data warehouse?

Lakes: raw data, schema-on-read, diverse formats, data science focus. Warehouses: processed data, schema-on-write, structured, BI focus. Lakes are cheaper storage; warehouses are faster queries.

How do I prevent my data lake from becoming a data swamp?

Implement: data cataloging, metadata management, access controls, data quality checks, lifecycle policies (archive/delete old data), and clear ownership. Governance is critical.

Can I query a data lake with SQL?

Yes - tools like Athena, Presto, Spark SQL, and Databricks enable SQL queries on lake data. Performance depends on data format and organisation. Parquet/Delta formats work best.

What is a data lakehouse?

Combines lake flexibility with warehouse performance. Technologies like Delta Lake, Iceberg, and Hudi add ACID transactions, schema enforcement, and time travel to lake storage. Best of both worlds.

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Data Lake

A storage repository holding vast amounts of raw data in native format until needed. Unlike warehouses, lakes store unstructured and semi-structured data without predefined schemas.

In-Depth Explanation

A data lake stores data in its raw, native format - structured, semi-structured, and unstructured. Data is loaded as-is and transformed only when needed (schema-on-read).

Data lake characteristics:

Raw storage: Data kept in original format
Schema-on-read: Structure applied at query time
Diverse data types: Structured, semi-structured, unstructured
Massive scale: Petabytes of data cost-effectively
Flexible: Support varied analytics use cases

Data lake use cases:

Machine learning training data
Log and event analysis
IoT sensor data
Document and media storage
Data science exploration

Data lake platforms:

AWS S3 + Athena/Glue
Azure Data Lake Storage
Google Cloud Storage
Databricks Delta Lake
Apache Hadoop/Spark

Business Context

Data lakes enable AI and advanced analytics by storing diverse data types cost-effectively. They complement warehouses for different use cases.

How Clever Ops Uses This

We design data lake architectures for Australian businesses, particularly for AI/ML workloads requiring diverse training data.

Example Use Case

"Storing raw customer interaction logs, images, documents, and IoT sensor data for future ML model training and exploratory analysis."

Frequently Asked Questions

Related Terms

Data Warehouse Data Lakehouse

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