Understanding Data Warehouse Architecture

In today’s competitive era, data is a catalyst fueling businesses to grow faster. As data volumes increase, fetching insights from this data comes with its challenges. Sure, you can use lakes and marts to dump any data, but ultimately, deriving business insights requires structured data with a faster querying experience. This raises the need for modern data warehouses. Unlike traditional methods, modern data warehouses are optimized for speed, scalability, and real-time analytics. Modern data problems require modern solutions and, hence modern data warehouses. 

Here in this blog, we explore data warehouse architecture, its components, and the different types of data warehouses. By the end of this blog, you’ll understand how modern data warehouses solve data challenges.

What is a Data Warehouse?

Data warehouses are central repositories of structured data. They store integrated data from various sources and are designed for fast query and analysis to support business intelligence, analytics, and decision-making processes.

Data warehouses have the following characteristics:

• Subject-Oriented: Data warehouses are organized around key subjects, such as sales, customers, or products, enabling focused analysis and decision-making.
• Integrated: Data warehouses consolidate data from multiple sources into a unified format. Naming conventions, attribute measures, and encoding structure are made consistent so that everybody speaks the same language. This ensures data consistency and accuracy across the organization.
• Time-Variant: Data warehouses store historical data. Every primary key in the data warehouse has an element of time in it. This helps BI Teams by allowing them to analyze and compare trends over different periods.
• Non-Volatile: Once data is stored in the warehouse, it remains unchanged. Only data loading and data access operations can be performed in warehouses. This ensures data stability and reliability for consistent reporting and analysis.

Modern software solutions focus on user experience and real-time actionable insights. This is why data warehouses are essential for organizations to streamline operations and gain competitive advantages.

What is a Data Warehouse Architecture?

Data Warehouse Architecture defines how data is collected, stored, processed, and accessed within a data warehouse system. It is a blueprint for the entire data lifecycle in warehouses. In the upcoming sections, we discuss the different layers and components of a primary data warehouse architecture.

Layers in a Data Warehouse Architecture

The matured data warehouse architecture mostly consists of the following four layers.

Data Source Layer

This is the layer where data is encountered. Data from the source layer is enriched and sent to further layers for processing. It may consist of data of any structure and format.  

Staging Layer

The staging layer can also be understood as the landing layer. This storage layer enriches raw data fetched from the Data Source Layer. 

Warehouse Layer

This layer consists of a central repository where data in structure format is stored for further analysis and query operations

Consumption Layer

This is the layer from which end users consume data stored in the warehouse layer. This layer consists of BI and reporting tools accessing the warehouse.

Components of a Data Warehouse Architecture

Data Warehouse architecture may consist of many components according to use case and tools. Below we discuss some of the common data warehouse components.

• Data Sources: Data sources are the input to the entire data warehouse platform. Various structured and unstructured data sources feed data into the warehouse.
• Data Integration: Data integration consists of ETL tools(Extract, Transform, Load) to process and enrich data collected from various sources.
• Staging Area: The staging area consists of intermediary lake tables where transformed and cleaned data from the data integration layer is stored.
• Data Storage: Data storage consists of a data warehouse, where data is structured and stored in table format for further analysis.
• Metadata Management: Data about data is called Metadata. Storing metadata helps in data management and usage.
• Data Marts: Data Marts are subsets of the data warehouse. These are mostly organized by specific business areas or departments.
• Governance and Security: The governance and security component employs different tools and policies to ensure data accuracy, consistency, and security to protect data from unauthorized use.
• Query Tools: Query tools are used to analyze data present in the data warehouse system to make strategic decisions. Query tools could comprise reporting, data mining, and OLAP tools.

Types of Data Warehouse Architecture

There are three types of data warehouse architecture.

Single Tier Architecture

A single-tier architecture is built for simple use cases. This architecture combines data processing and storage into a single layer, usually for smaller-scale systems. It is used where organizations have limitations on data storage resources. Data warehouses in this architecture are implemented as multi-dimensional views of operational data.

Two Tier Architecture

The two-tier architecture separates data storage and processing. Although it is called two-tier architecture to depict the separation of storage and processing layers, It consists of four layers i.e. Source layer, the Staging layer, the Data Warehouse layer, and the analysis layer. All these layers have already been discussed in the above section.

Three Tier Architecture

The Three-tier architecture consists of a data source layer, a reconciled layer, and a data warehouse layer. The reconciled layer creates a standard reference model for the whole organization and separates data extraction and transformation from data warehouse load.

This architecture is useful when organizations have huge volumes and a large variety of data. However, it is complex and may cost more than others due to the requirement of an extra storage layer. This may also lead to increased processing time, Making it difficult for real-time analysis.

Traditional Data Warehouses vs Modern Data Warehouse

Modern data warehouses use cloud-based technologies compared to traditional ones that rely on on-premise systems. Organizations may decide which way to go based on their business requirements. The following table shows the difference between them and which is best for your use case.

Aspects	Modern	Traditional
Storage Location	Cloud	On-Premise
Use Case	Processing and Analysing large volumes of data	Facilitate small decision-making processes
Architecture	Compatible with most	ETL, Star schema
Cost	Lower	Higher
Data Source	Any data source	Transactional database
Scope	Fetching valuable insights from data	Business Intelligence (BI)
Maintenance	Lower	Higher

Migrate Data to the Warehouse with Hevo

Hevo is a no-code data integration platform. with Hevo, you can seamlessly integrate data from various sources, such as databases, cloud services, and applications, into your data warehouse in real time. The platform automates data extraction, transformation, and loading (ETL), ensuring that your data is cleansed, structured, and ready for analysis.

Hevo supports popular data warehouses like Amazon Redshift, Snowflake, Google BigQuery, and more, enabling businesses to streamline their data workflows without manual intervention. Its user-friendly interface, pre-built connectors, and robust security features make data migration and integration easy and efficient.

Conclusion

Data warehouses play an important role in facilitating value generation from data for strategic decision-making. They provide a structured and efficient way to store, manage, and analyze vast amounts of data. With data warehouses businesses can collect vast amounts of data, providing a single source of truth for insights and reporting. Hevo with its pre-built connectors and support to various data warehouses can simplify the process of migrating data into popular data warehouses. With Hevo businesses can focus on making strategic decisions by fetching insights from data without having to think of managing and setting up infra or writing code.

Frequently Asked Questions

1. What is a data warehouse?

A data warehouse is a centralized repository that stores and organizes data from multiple sources. It is used for efficient querying, reporting, and analysis.

2. Why is data warehousing important?

Data warehousing is important because it allows organizations to have a consistent single source of truth for decision-making, analytics, and reporting.

3. What are the key components of a data warehouse architecture?

Key components of data warehouse architecture include data sources, data integration (ETL processes), staging area, data storage, metadata management, data marts, and governance and security layers.

4. How does a modern data warehouse differ from a traditional one?

Modern data warehouses leverage cloud technology, and offer scalability, real-time processing, and advanced analytics, whereas traditional data warehouses are often on-premise, limited by hardware, and slower in processing.

5. Which type of data warehouse architecture should I choose?

The type of data warehouse to choose depends on the use case. Single-tier architecture can be used for simplicity, two-tier for moderate scalability, and three-tier for maximum scalability, flexibility, and performance.