Data Lake Analytics

The common definition for a data lake is a storage repository for large volumes of raw, native-format data.

Data lake analytics is the process of gleaning actionable insights out of raw data lake data by the deployment of specialized tools or via data extraction to analytics platforms such data warehouses. The growing volume and importance of data means modern businesses are continually tasked with how to best store, manage, transform, and analyze diverse data sets from a wide range of sources.

Most organizations use or are planning a cloud-based data lake to ingest and store data from a range of sources. However, in order to run dashboards, reports, uncover trends and glean insights, you need to take this raw data and render it usable for business analysis, often across multiple departments and stakeholder that require concurrent access.

Traditionally, the data lake was the place where structured, semi-structured, and unstructured data was stored and extracted for analysis as needed. However, getting the data from the data lake to the end analyst for data lake analytics (often via a data warehouse) usually involved a range of time-consuming data prepping and data transformation processes, such as extract, transform, load (ETL).

Data Lake Analytics and the Snowflake Cloud Data Platform

With a cloud data platform, it is no longer required to consider data lakes, data warehouses, data exchanges, and other use-delimited data platforms as separate, siloed systems. Snowflake has changed the data management and analytics environment by eliminating the need to develop, deploy, and manage separate data systems. For the first time, there is one enterprise cloud data platform, making it possible to store, manage, disseminate, and analyze structured and semi-structured data, such as tables and JSON, without the need to move it between platforms. Snowflake’s extensible data architecture, combined with robust data pipeline and transformation toolset compatibility, allows for the easy movement of data (from raw to modeled to consumption) inside one data cloud platform. With Snowflake, modern businesses no longer have to ponder the data warehouse vs. data lake question.