A modern data warehouse enables you to integrate data at scale. You can then get insights for business intelligence and perform advanced analytics.
Therefore, data warehousing is a critical part of the data analytics process.
In this blog post, we look at what a data warehouse is, its architecture, as well as its components.
What Is a Modern Data Warehouse?
A data warehouse is a central data management system that enables you to get business intelligence by storing and integrating data from multiple sources.
It helps you pull, process, and organize data for efficient analysis, as well as make it available to anyone within your organization.
Did you know that while data warehouses have been around since the 1980s, they have undergone extensive evolution with the coming of big data?
Data warehouses now give you capabilities for advanced analytics, as well as data visualization.
What Does the Architecture of a Data Warehouse Look Like?
There are three ways in which you can build a data warehouse. These methods vary by the number of tiers in the architecture of the warehouse:
Single-Tier Data Warehouse
The objective behind having a single-tier data warehouse is to eliminate redundancy by reducing the amount of data stored.
However, there’s a limitation. A single-tier data warehouse doesn’t have a component that separates analytical and transactional processing.
Two-Tier Data Warehouse
The architecture of a two-tier data warehouse has a staging area for the data sources, before the data warehouse layer.
This helps you make sure that clean data is loaded into the warehouse, as well as in the correct format.
There are, however, network limitations to this approach. You also cannot expand it for an increased number of users.
Three-Tier Data Warehouse
The most commonly-used architecture for data warehouses, the three-tier approach, as the name suggests, has three tiers:
Bottom Tier: This is the database of the warehouse. Cleansed and transformed data is loaded here.
Middle Tier: Giving an abstract view of the database, this is the application layer. Data is arranged for analysis here.
Top Tier: This is where you access and interact with data. Representing the front-end client layer, here you can use reporting tools, querying, analysis or data mining.
What Are the Components of a Modern Data Warehouse?
As you can see from the architecture of the three types of data warehouses, a few components overlap.
On the other hand, some components are unique to the number of tiers.
Let’s look at the critical components of a data warehouse and the role they play in data analytics.
ETL stands for Extract, Transform & Load.
The staging layer uses ETL tools to extract data from different formats and loads it into the data warehouse after verifying quality.
The system cleans and organizes information, following which data collected from a number of sources is merged into one database.
What’s the difference between ETL and ELT? Find out more in this blog post.
This is the most important component of the data warehouse. It is where you store and access data.
Before you create a data warehouse, you’ll need to decide what kind of database you’ll be using. You can select from:
- Relational databases
- Analytics databases
- Data warehouse applications
- Cloud-based databases
The data warehouse stores the following types of data:
Meta-Data: This is information that defines data. It enables you to organize, find, and address queries to required data.
Summary Data: The warehouse manager generates summary data. It gets updated as new data gets loaded into the warehouse. Summary data also helps you accelerate query performance.
Raw Data: This is the unprocessed data loading into the repository. Once you have raw data, you can process and analyze it further.
These tools enable you to analyze data, gather insights, and generate reports. Here are a few tools that you’ll use:
Reporting Tools: These tools enable you to understand business performance and decide on your next course of action. For example, data visualization using graphs and charts.
OLAP Tools: OLAP stands for Online Analytical Processing. With these tools, you can analyze data by extracting it from multiple relational datasets and reorganizing it into a multidimensional format.
Data Mining Tools: These tools help you spot patterns within your warehouse by enabling you to analyze datasets and find correlations.
A data mart enables you to have a number of groups within the system by segmenting warehouse data into categories.
It partitions data for specific user groups, such as departments within an organization. For example, sales, finance, or marketing.
In this blog post, we looked at what a data warehouse is, its architecture, as well as its components.
Building a data warehouse will depend on the business logic of your individual use-case.
Build Your Very Own Modern Data Warehouse.
At Grazitti, the data analytics team knows a thing or two about building modern data warehouses.
Should you want to know more, please write to us at [email protected] and we’ll take it from there.
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