Consolidating data using datamarts
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ETL extracts data from several sources, transforms the data to meet business needs using certain business rules, and finally loads (writes) data into a target system.
When starting with a Data Warehouse, you’ll typically use ETL to get data directly from source systems to the Data Warehouse, and then from the Data Warehouse to Data Marts as needed.
Inmon advocates for the creation of a Data Warehouse as the physical representation of a corporate data model from which Data Marts can be created for specific business units as needed.
Each approach has its merits, and a number of factors influence whether you should start with Data Marts vs.
So far, most cloud use cases focus on application servers.
Normalization works by reorganizing data so that it contains no redundant data and separating related data into tables with joins between tables that specify relationships.Data Warehouses/Marts often use a denormalized data structure, wherein the administrators take steps to improve query performance by adding back redundant data to normalized data to decrease analytic query running times.An important concept is extract, transform, and load (ETL).Its innovative approach to supporting both semi-structured and structured data in a single system makes it ideal for combining data into one location.
With Snowflake’s zero-management solution, you can focus on using data to drive insights instead of the overhead of maintaining a legacy data warehouse.
For a small to medium-sized marketing business, it makes sense to start with a Data Mart.