In recent years the logical data warehouse concept has gained a lot of attention, but the understanding of what it really involves is fairly limited.
What is a Logical Data Warehouse?
You may or may not know the architecture of a typical data warehouse includes many layers. Some of those layers might be rather complex ones, such as data storage which require a specific architecture of its own. The logical data warehouse sits at the very top of the other elements and integrates the layers underneath it.
Considered a semantic layer, the logical data warehouse communicates between systems not usually able to talk to each other. The logical data warehouse works alongside the storage layer holding the data and offers data management control.
Overall the logical data warehouse is a power layer of the data warehouse structure enabling data to be seen, assessed, and analyzed which significantly reduces the needs of data processing.
A Wealth of Interfaces
The logical data warehouse should be supporting several interface types as it will permit the accessibility of a large number of users who can then access the data. By supporting many interfaces, it also enables the use of a variety of tools making it much more than just a view layer.
The logical data warehouse operates in real time making it beneficial for time-sensitive data helping to increase productivity within the workforce.
The Components of the Logical Data Warehouse
The repository management element of the logical data warehouse provides filtered data easily accessible for many users within the company. Data virtualization enables a single view of data originating from multiple sources regardless of whether or not it’s structured. This can include relational databases, cloud services, file servers, data lakes, social networks, and the like. Having distributed processing means analytics and data querying is handled by the area in which the data resides.
In some cases, this could mean data searching through several different sources and with distributed processing, each section processes its own data where the results are then laid out in one place. Metadata management maintains the collection of metadata from all data services which is used to support data governance and data quality.
The metadata management aspect works alongside the taxonomy and ontology resolution element to pull data from multiple sources and combine it. That metadata is then used to locate data assets and support service level agreement services and auditing. The auditing and performance services of the logical data warehouse collects performance statistics of the warehouse's elements and continues to maintain the desired preferences of connected applications and users.
The Benefits of a Logical Data Warehouse
There are many benefits of a logical data warehouse and the fact it's called ‘logical’ simply emphasizes its purpose. Managing vast amounts data, the smart way. Not only can you gain access to all the enterprise data enabling business reporting in real time, but the detailed analytics scope of the logical data warehouse provides insight as to how to increase upselling and cross-selling.
The speed of operations and customer service are also greatly enhanced which, along with the additional upselling opportunities, is a recipe for business growth and recognition. In opting for the logical data warehouse, the data is integrated much quicker than the traditional extract, transform, and load (ETL) solution.
Huge corporations have included the logical data warehouse into their systems and have been able to perform live migrations to the cloud while having a minimal impact on the business operations. The delivery of data is rapid, reporting time is massively decreased, and the data received is formatted in an easily digestible way for all users.
What a Logical Data Warehouse Offers that is Different than a Classic Data Warehouse
One of the defining factors of a logical data warehouse is it is up to 90% faster to implement than a classic data warehouse. Saving much needed time businesses can use on more pressing matters. Another beneficial element is ETL/programming is not required for a logical data warehouse and as a result a lot of time and effort is saved.
As stated previously no data is stored in the logical data warehouse and remains at its source which means there are no delays of data delivery due to the execution of actions.
In a traditional data warehouse data latency is often an issue, but the logical model has data sets which are accessed by several services including REST, Odata, SOAP, Sharepoint, and so on. Even though a significant framework or infrastructure is usually in place, a logical data warehouse doesn’t actually require one.
The traditional data warehouse has many flaws and begins to crack under the huge volume of data is collected every day by businesses. Instead of improving efficiency in the workplace it has started to become a hindrance, causing unnecessary delays and in some cases requiring almost constant maintenance.
A logical data warehouse can process the data live because only a small amount of hardware is needed due to the data not being moved to a store. When implemented, the data is retrieved from the sources live with the specific business rules applied. Analytics tools are used for business identify a logical data warehouse as a database which means they see no need for intervention or alterations.
The traditional enterprise data warehouse is struggling to cope with the increasing demand for businesses. Big data arrives left, right, and center from sources such as the cloud, mobile devices, social media, and many more.
With that in mind, it's no surprise the logical data warehouse was a welcomed addition to the data tech world and businesses are incorporating it into their systems.
