What is a Data Lake?

A data lake is a repository of data, typically stored in file format with variable organization or hierarchy. Built on object storage, data lakes allow for the flexibility to store data of all types, from a wide variety of sources. 

Data lakes typically contain a massive amount of data stored in its raw, native format. This data is made available on-demand, as needed; when a data lake is queried, a subset of data is selected based on the query’s criteria and presented for analysis. 

What is the Purpose?

A data lake is a comprehensive way for users to explore, refine, and analyze petabytes of information constantly arriving from multiple data sources. One petabyte of data is equivalent to 1 million gigabytes: about 500 billion pages of standard, printed text or 58,333 high-definition, two-hour movies. Data lakes are for users to explore and analyze data of high volume, variety, and velocity.

Data Lake Features

The characteristics of data lakes that distinguishes them from other types of big data storage are:

• Open to all data, regardless of type or source

• Data is stored in its original raw, untransformed state

• Data is transformed only when provided for analysis based on matching query criteria

Benefits of Data Lakes

The source- and format-agnostic nature of data stored in a data lake offers several benefits for businesses, including:

• Flexibility, as data scientists can utilize data in its rawest form for feature engineering and machine learning

• Accessibility, as all data is centrally stored

• Affordability, as data lake object storage is typically cost-effective

• Compatibility with most open source data analytics technologies

• Comprehensive, combining data from all of an enterprise’s data sources including IoT

Data Lake vs Data Warehouse

Both data lakes and data warehouses are big data repositories. The primary difference between a data lake and a data warehouse is in compute and storage. A data warehouse typically stores data in a predetermined organization with a schema. A data lake does not always have a predetermined schema. Also, whereas a data warehouse usually stores structured data as tables, a data lake stores structured, semi-structured, and unstructured data as files.

Comparison Chart: Data Lake and Data Warehouse

Data Lake in the Cloud

The sheer volume of big data—particularly the unfiltered data of a data lake—make on-premises data storage difficult to scale. Amazon S3, Snowflake, and Microsoft Azure Data Lake are a few cloud-based data storage service providers that enable data storage of varying sizes and speeds for processing and analysis. 

Snowflake as Data Lake

Snowflake introduced significant enhancements, further blending the benefits of data lakes with the efficiency of data warehousing and the scalability of cloud storage. 

Snowflake now supports Apache Iceberg tables, enhancing its ability to manage data lakehouse workloads. This integration enables users to treat Iceberg tables as standard Snowflake tables, thereby simplifying the management of diverse data formats and enhancing query performance.

Key to Snowflake's data lake strategy is its commitment to security, scalability, and cloud independence. The platform's architecture allows for independent scaling of storage and computing, ensuring optimal performance and cost efficiency. Snowflake's data lake also offers advanced security features like auditing, granular access control, and encryption, crucial for modern data management and compliance.

Explore the Snowflake Data Cloud's enhanced data lake capabilities with a free trial, and discover its full potential for unified data management and advanced analytics.