Data Lake Best Practices
Before diving into some modern data lake best practices, a definition is in order. As recently as five years ago, most people had trouble agreeing on a common description for data lake. Even though data lakes have become productized, data lakes are really a data architecture structure. At its most basic, data lake architecture is constructed to store high volumes of ingested data for analysis later. In the past, data lakes were considered distinct from data marts and data warehouses. In a modern cloud data platform, such distinctions are no longer necessary.
Data Lake Best Practices and the Snowflake Data Cloud
Today it is no longer necessary to think about data in terms of existing separate systems, such as legacy data warehouses, data lakes, and data marts. Snowflake has changed the data engineering landscape by eliminating the need to develop, deploy, and maintain these distinct data systems. For the first time, there is one enterprise cloud data platform, making it far easier to manage structured and semi-structured data, such as tables and JSON, in a holistic manner.
Common data repositories need to move data through four logical data zones and many organizations have had challenges in figuring out how to achieve this movement with minimal friction while still prepping data for consumption. Snowflake's platform has an extensible data architecture that allows for the seamless movement of data (from raw to modeled to consumption) inside one data cloud ecosystem. Data can be generated via Kafka or a similar messaging pipeline and persisted into a cloud bucket. From the cloud bucket, Apache Spark or a similar transformation engine converts the data into an optimized columnar format, such as Parquet, and persists the data into the conformed data zone. Now, businesses no longer have to choose between a data lake and a data warehouse.
To learn more, download the eBook "Data Management and the Data Lake: Advantages of a Single Platform Approach."