In a data warehousing environment, data is typically loaded
into the data warehouse from various source systems through a process known as
ETL (Extract, Transform, Load). The "Load" phase of ETL involves
loading the transformed and processed data into the data warehouse for storage
and analysis. There are different types of loads used in this process, each
serving a specific purpose.
Here are the main types of loads in data warehousing:
Full Load:
In a full
load, all the data from the source system is extracted and loaded into the data
warehouse.
This type
of load is typically used for the initial population of the data warehouse or
when performing periodic full refreshes of data.
Full
loads can be time-consuming and resource-intensive, especially for large
datasets, but they ensure that the data in the warehouse is complete and
up-to-date.
Incremental Load:
In an
incremental load, only the new or changed data since the last load is extracted
and loaded into the data warehouse.
Incremental
loads are used to keep the data warehouse up-to-date with changes in the source
systems while minimizing the amount of data processed and loaded.
This type
of load is often more efficient than full loads, especially for large datasets
where only a small portion of the data changes frequently.
Delta Load:
A delta
load is a variation of an incremental load where only the changed data, or
"delta," is extracted and loaded into the data warehouse.
Instead
of extracting all the new or changed data since the last load, a delta load
only extracts the specific records that have been added, updated, or deleted.
Delta
loads are useful for optimizing the ETL process and reducing the processing
time and resources required for incremental updates.
Initial Load:
An
initial load, also known as a baseline load or seed load, is performed during
the initial setup of the data warehouse.
It
involves loading the entire dataset from the source systems into the data
warehouse for the first time.
Initial
loads are typically followed by incremental or delta loads to keep the data
warehouse synchronized with ongoing changes in the source systems.
Historical Load:
A
historical load involves loading historical or archival data into the data
warehouse.
This type
of load is used to populate the data warehouse with historical data that may
not have been captured in real-time or may have been stored in separate
systems.
Historical
loads are often performed as part of the initial setup of the data warehouse or
when integrating data from legacy systems or historical records.
Real Time Load:
In a
real-time load, data is loaded into the data warehouse immediately or shortly
after it becomes available in the source systems.
This type
of load is used for streaming or near-real-time data integration, where timely
analysis of fresh data is critical.
Real-time
loads require robust data integration and processing capabilities to handle
high volumes of data with low latency.
Comments
Post a Comment