A large collection of business data that is utilized to help the organizations make decisions is known as Data Warehouse. The data warehouse concept came into being in the 1980s when building a data warehouse assisted a transition from just improving the operations to being a bit more supportive of the systems that are used to make decisions to acknowledge business intelligence.
An ample amount of data stored in the data warehouses appears from different places in the form of internal applications like sales, marketing, finance, external partner systems, and customer-facing App, etc.
What is Data Warehouse and what is used for:
A data warehouse on the technical aspect pulls data periodically from those Apps and systems, followed by which the data undergo formatting and the processes of import to confirm the data that is present in the warehouse. The processed data if further stored by the Data warehouse to be ready for decision-makers to connect.
How generally does the data pull occur or how the formatting of data takes place varies based on the specific organizations’ needs. This factor must be understood in the context of building the data warehouse.
What are some of the advantages of having a Data Warehouse?
The companies that utilize data warehouse to have their analytics and business intelligence assisted see a lot many advantages substantially in the form of:
- Brilliant Data:
The addition of Data sources to data warehouses facilitates the organizations to assure that they are gathering persistent and important data from the source. They don’t need to be curious about the data being accessible or inconsistent as it enters the system, ensuring higher data integrity and quality for making better decisions.
- Swift Decision making:
A warehouse stored with data is an inconsistent format that it can be analyzed. An analytical power along with a full dataset is given to make the decisions which are based on the hard facts. So, the ones who make decisions may not have to reply to incomplete or poor quality data or also hunches and they don’t even have to risk the delivery of slow and inaccurate results.
You can connect with EPCGroup for data warehouse consulting
What are some data warehouse examples:
One of the finest examples is an organization called Beach body that considers being the leading provider of nutrition, fitness and weight loss programs. By this, they inculcate the habit of setting the targets and personalizing contributions to customers, to get better health outcomes that would induce their company to enhance their business performance.
The company cleaned the architecture of analytics with the addition of Cloud data which is Hadoop based on AWS that is energized by the Talend Real-Time Big Data. With this new architecture came the allowance of the Beach body to reduce the acquisition of data by 5 times and at the same time, they would improve the database accuracy for the marketing campaigns.
The Do’s
You can run the logical queries with the help of Data warehouses, build models that are accurate and predictable and also analyze the impactful trends throughout the organization. What one needs to know is what can go into the designing of a data warehouse?
Whether you choose to apply a pre-built vendor solution or you want to start from the very beginning, then there is a requirement of some level of warehouse design that needs to be adopted for building a Data warehouse.
Here are the Necessary Dos of Building a Data Warehouse:
- The business requirements need to be well defined:
Before building a data warehouse, it is the designing of a data warehouse that is a wide business journey. The data warehouses should touch all the business areas, so every department needs to be competent in making the design.
The warehouse is as powerful as the data that it contains, thereby coordinating the exclusive department needs and the goals with the project in a whole, that turns out to be the critical factor to your success.
Therefore, if the sales data of a company cannot be aligned with the marketing data, the overall inquiry results will have some critical components missing in it.
Gaining knowledge of the value of the leads would be entirely dependent on the marketing data. Every department needs to understand the data warehouse’s purpose, with regards to how it would be advantageous for them and the results they can expect from the warehousing solution. Until and unless such steps are not taken, hardly any outcome can be witnessed.
- The physical environments need to be set up:
There are three primary physical environments of the Data Warehouses which include Testing, Development and Production, thereby mimicking the best practices of the software development standard, where these environments exist on the separate physical servers.
You might also be interested in reading the difference between data lake and data warehouse
Understanding Data Modelling in Data Warehouse concept.
- Data modelling introduction:
The process of visualizing data distribution in the warehouse is known as Data Modeling. We all have to think of it as a blueprint to assess and understand the crucial factors. It’s all before you start building and are curious to see what goes where and not. Such elements are crucial and must be known with keen interest. This is the same case for Data Modeling.
Moreover, You can visualize the relationships between the data with the help of Data Modeling and it turns out to be useful for the setup of standardized naming conventions, relationship creations between the data and the sets, and the establishment of the compliance and security processes that coordinate with the overarching goals of Information Technology.
You can also try data modeling through Power BI which connects multiple data sources using relationships.
A system that stores the data to run analytics and inquiries is known as a Data Warehouse, whereas a Data Mart is the area in the vicinity of the data warehouse that is used for the storage of data for a specific business function. Know this key factor in the multiple contexts of building a Data Warehouse for better evaluation.
- The extract, transfer and load solution must be chosen:
ETL or Extract, Transfer, the load is the process of pulling the data out of the current tech stack or the storage solutions that already exist, and it can be put in your warehouse. Always pay careful attention to the Usage of the ETL solution. Since, this ETL lays full responsibility for the bulk of the in-between work, that develops a poor ETL process and can break the entire warehouse. Give it a keen focus to avoid any unforeseen situation.
- Online analytical processing cube or the OLAP:
The OLAP cubes need to be addressed for the design of the whole database from the very beginning or if you need to maintain your OLAP cube, then it most likely requires specialized personnel. So, if you plan to use a vendor warehouse solution, then you won’t be needing an OLAP.
- The front end creation:
The backend processes have been created and there needs to be a front end visualization as well to make the users get an immediate understanding and to put the application of the results of the data inquiries well in order.
The Don’ts
- Heading towards the Cloud:
Some prefer to shift the Data Warehouse to the Cloud to get the data volume and the data computational issues solved effectively. The moving of a larger data amount to the Cloud would not be easy, as the data schema and the data along with facilitating the ETL functions on the Cloud need to be removed. For the optimization processes of a data warehouse, there is a need for architectural modernization, and reforming the database schema.
And, also the rebuilding of the data pipelines does matter the most. Likewise, if these factors are to be taken into consideration, you are most likely to experience a cumbersome process.
- Dependent on Data Warehouse Appliance:
Currently, many companies have started to head back to the building of data warehouse appliances as the solution to the modeling problems of Data Warehousing. A set of hardware and software solutions that are used by the enterprises to give room to huge amounts of data is known as the Data Warehouse Appliance.
Despite an increase in the data warehouse appliances, there will be an increase in the performance of the inquiry speed against the million records. It can only be achieved through external hardware and memory configurations.
- Eyeing towards the Big Data Solution:
There are large data solutions, like Hadoop for the optimization of the data warehouse that has not delivered the analytics and performance for a BI system because these systems are not built to act as the building of the data warehouse.
However, Architects and scientists carrying out those feats have not been too much success due to the various reasons. The notable ones are as follows:
- The Structures of the data and the schemas of the database are not user friendly.
- There is a compromise of Data Security.
- There is toughness on the integration of the outside data.
In the end, It is well understood that while building a data warehouse, numerous factors are there that should be focused on. Keeping the pros and cons in mind means saving time, energy, and resources.
