Data Warehouse Architecture

A data warehouse architecture defines the overall architecture of data communication. From processing the data to presenting it for end-clients computing, each data warehouse is characterized by some vital components within the enterprise. Data warehouse applications are designed to support online analytical processing (OLAP) which includes applications such as profiling, summary reporting, forecasting, and analyzing the trends. Unlike production databases, data warehouses are updated periodically from the operational systems. 

How do Data Warehouses Work? 

A data warehouse is subject-oriented as it offers information regarding the subject instead of an organization’s ongoing operations. The OLTP (Online Transactional Processing) data, that executes transaction-focused tasks, is accumulated in the production database on a regular basis which is then extracted, filtered, and then loaded inside an exclusive warehouse server from which users can access the information they require. An advantage of a data warehouse is that the previous data is not erased when new data is entered into it. A data warehouse is time-variant as the data stored in it has a high shelf life. 

Data Warehouse Architectures are complex since they contain historical and cumulative data from single or multiple sources. These sources can be Traditional Data Warehouse, Cloud Data Warehouse, or Virtual Data Warehouse.

Data Warehouse Layers

One-Tier Data Warehouse Architecture:

The objective behind this type of architecture is to minimize the amount of data stored as there is a single layer. This is done to avoid data redundancy. This type of architecture is not very commonly used.</span

Two-Tier Data Warehouse Architecture:

Two-layer architecture has two separate layers for the physically available sources and the data warehouse. Due to network limitations, this type of architecture has connectivity issues. This architecture isn’t expandable and hence does not support a lot of end-users. 

Three-Tier Data Warehouse Architecture:

Consisting of a top, middle, and bottom tier, this kind of architecture is very popular and is most widely used. 

1. Bottom Tier: This usually comprises the relational database system. In this layer, data is sorted, cleaned, transformed, and loaded using the back-end tools. 
2. Middle Tier: This is the OLAP server. It is implemented using either ROLAP or MOLAP models. This layer acts as a mediator between the database and the end-user as it presents an abstract view of the database. 
3. Top-Tier: This is also called the front-end client layer. Here, you can find all the tools and APIs that you need to get the data out from the data warehouse. Tools such as Query and reporting tools, Application Development tools, Data mining tools, and OLAP tools can be found here.             

Data Warehouse Components

1. Separation: Analytical and transactional processing should be kept separate from each other. 
2. Scalability: Use simple Hardware and software architectures that can manage and process large data volumes to meet the user’s growing requirements. 
3. Extensibility: The architecture should be able to perform new operations and technologies without much hassle and effectively.  
4. Security: Monitoring accesses are critical since strategic data is stored in the data warehouse. 
5. Administrability: Data Warehouse management should be easy and uncomplicated.

Data Warehouse Best Practices

1. Create a data model: Identify your organization’s business logic and understand what type of data is vital to the organization before charting a strategy on whether this data will flow through the data warehouse in a dimensional, denormalized, or hybrid mode.
2. Opt for a reputed data warehouse architecture standard: Make sure that your data model has a framework and a set of best practices to follow. Popular architecture standards include 3NF, Data Vault modeling, and star schema.
3. Create a data flow diagram: It’s time to document how you want the data to flow through the data warehouse to meet your business requirements. 
4. Create a single repository: When dealing with such large amounts of data, it becomes important to have a single source of truth where all your data is consolidated.
5. Utilize automation: Automation tools help in sorting vast amounts of data efficiently. This will ensure that your data is processed quickly and accurately.
6. Permit metadata sharing: Choose the appropriate design approach as a top-down and bottom-up approach in Data Warehouse which can facilitate metadata sharing between data warehouse components easily.
7. Enforce coding standards: Enforcing coding standards ensures the system’s efficiency. Carefully design the data acquisition and cleansing process for the Data warehouse.

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