What is an Enterprise Data Warehouse?
Whenever you have got to make a decision, you make it depending upon the experiences you have had and what suits you best, you go for it. Similarly, for an enterprise, these experiences are data that is stored in a warehouse to make decisions.
What is an Enterprise Data Warehouse?
This is a form of the corporate repository that stores and manages all the data of an enterprise. Customer Relationship Management, Enterprise Resource Planning, physical recordings, and other flat files are the sources from where data gets collected i.e. from different systems. To make sure that the analysis happens, data is stored in one place.
So, with the data warehouse, an enterprise manages huge, gigantic data sets for Business Intelligence. Now, with all this raw data collected, intelligent insights are brought into the realm through various methods/ technologies.
How is it different from a usual data warehouse?
Enterprise Data Warehouses not only contain data integration tools on one end, along with analytical interfaces that help in transforming the raw data, they also contain much wider architectural diversity and functionality. Also, EDWs are often decomposed into smaller databases for the comfort of the users as the system is complex.
Benefits of an EDW
Improves in providing insights and gives you a competitive edge over other businesses. An EDW’s greatest benefit comes from the way the data is centrally located so that business units across the organizations can easily replicate data from various resources for analysis
Let's understand basic concepts while understanding this enterprise.
**The Mine of All the Data **It stands by its name- warehouse. It is a place where all types of corporate data fit in and is available in the organization.
Makes it viable for the end-users
The data stored in an EDW is always standardized and structured, the reason being it makes it possible for the end-user to query it via BI interfaces. This makes it different from data lakes where it stores unstructured data and is frequented by data scientists who work on it for analytical purposes.
Data models
Business data relates to different domains, and is structured around one subject, and is called a data model. Also, metadata provides you with an explanation of where the piece of information is picked from.
Chronologically Segregated
It is important to understand the period in which an event occurred, to explain or understand the certainty of its occurrence and make further decisions based on it. Hence, the stored data is divided into periods.
Deletion is not the option
The data never gets to be deleted, it can be modified, updated, or manipulated according to the need. However, it is not erased with the plausibility that they could be used again for analytical purposes.
Types of EDW
Depending upon the kind of business and the type of storage you would need and the type of analytical complexity to the security issues you would be dealing with, there are different types of warehouse types.
Classic Data Warehouse- Having a unified storage system that has both software and hardware is considered a classic variant for an EDW. Now, you don’t need to have data integration tools between multiple databases, your problem can be sought by getting connected through APIs that can easily source and transform information. So, the work is done in either of the cases- when the information is getting downloaded or when it is present in the warehouse itself.
Why is it considered better than a virtual data warehouse?
There is no additional layer of abstraction because as discussed earlier everything happens
In the preprocessing stage or in the warehouse. It simplifies the work for data engineers. However, the cons would be the cost incurred due to the expensive infrastructure setup required. Along with this, the maintenance cost would require the hiring of a team of professions to keep the platform running.
** Virtual Data Warehouse-** Alternative to a classic warehouse, multiple databases are connected virtually and queried in a single system. It is best when you do not want to engage with the underlying infrastructure or when you think you have manageable data. Hence, the data can be used with the help of analytical tools. However, you need to realize that there are certain drawbacks to the same-
Because there will not be one unified storage space but multiple, the maintenance cost would vary and would need a constant update on it.
You would be needing transformation software to make it insightful for the end-users.
It would be convoluted to get complex data queries done as the data would be lying in two separate databases.
Cloud Data Warehouse
Naming a few, Amazon Redshift, Snowflake, Microsoft SQL Data Warehouse, offer holistic, fully managed warehouse and provide EDW as a service on its own. It provides you with the same benefits as a cloud service, where infrastructure is maintained for you. The investment would depend upon the memory and the number of computing capabilities that are required for querying.
You need to be cautious about data security as business data cannot be leaked due to confidentiality and privacy reasons. So, you must have a reliable vendor who can trust.
Conclusion-
It is important that you understand the type of warehouse that you need to figure out that fits your data platforms. It takes a lot of energy to set up one warehouse and it is important to make the right decision because a lot is at stake.
It is important to understand that there are experts at the end who can help you figure it out despite the plethora of options that exist and end up confusing you. Try engaging with the people who are using the different data platforms to make sure you end up with the right choice.
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