Data Lake Zones

Shannon Lowder walks us through a multi-zone approach to storing data in a data lake:

Our first zone is the raw zone.  This zone will serve as the landing point for source files.  Like the extract (or stage) schema in our data warehouse, we want these files to match the source system as close as possible.In the data lake, we actually go one step beyond saying we want the schema of our raw files to match the source system, we also want these files to be immutable.

Immutable means once they are written to the raw folder we shouldn’t be able to modify or delete them.  That way, we can always reconstruct different states from these files without having to retrieve them from the source system.

Worth reading the whole thing.

Related Posts

On Whether Relational Data Belongs In A Data Lake

Melissa Coates debates whether relational data really belongs in a data lake: For certain types of data, writing it to the data lake really is frequently the best choice. This is often true for low latency IoT data, semi-structured data like logs, and varying structures such as social media data. However, the handling of structured […]

Read More

The Value Of Power BI Dataflows

Matt Allington gets to the core benefits of Power BI Dataflows: Dataflows are: An online service provided by Microsoft as part of Power BI (software as a service, or SaaS). In effect dataflows are an online data collection and storage tool. Collection:  It uses Power Query to connect to the data at the source and transform that data as […]

Read More

Categories

October 2017
MTWTFSS
« Sep Nov »
 1
2345678
9101112131415
16171819202122
23242526272829
3031