The digital universe is proliferating at an astronomical rate, with 2.5 quintillion bytes of data generated daily. That said, it wouldn’t be wrong to position data as the new oil. Even though businesses are drowning in information, they are often thirsty for actionable insights.
Using data analytics can help you acquire more customers. But for that, you will have to refine this raw data into a tactical asset. The answer to how lies in a meticulously crafted data warehouse.
The goal is to rise above just storing numbers and instead to build a digital brain for your organization. The need of the hour is a robust system that transforms chaotic data streams into decisive intelligence.
But what constitutes the data warehouse architecture that makes it a robust system?
Let’s demystify its foundational layers, imperative components, and architectural types with which you can consolidate, use, and analyze your data for strategic foresight and unprecedented success.
| TL;DR: Data warehouse architecture is the essential blueprint for transforming raw data into valuable insights. The blog breaks down DWH architecture into four layers (source, staging, modeling, presentation) and discusses its core components, including the database, data integration, and metadata. It then details five types of architecture—single-tier, two-tier, three-tier, data mart bus, and cloud-based, outlining their benefits and drawbacks. |
What is Data Warehouse Architecture?
Data warehouse architecture is the blueprint or the structure that outlines data storage, management, access, and organization in a data warehouse (DWH) system.
The process of converting raw data into useful insights is long and full of different components. The architecture combines these components with the processes and interactions.
The active data warehousing (ADW) market size is expected to grow at a CAGR of 13.7% and reach $19.43 billion in 2029.
DWH architecture creates a single source of truth for your entire organization, making it easier for transformation and analysis. These are designed to be stakeholder-oriented. Thus, even though different stakeholders are using common data, they have different data analysis and modeling needs.
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Understanding the architecture begins by learning about its four crucial layers.
Data Warehouse Layers
In data warehousing, the architecture follows a layered approach to make the data more usable for analytical purposes.
Let’s understand the four DWH layers.
1. Source Layer
The source layer is the foundational layer that stores data in multiple databases and operational systems. You cannot usually access your data directly from here since it is a storage unit for further processing. Some common sources are NoSQL DBs, APIs, relational DBs, etc.
2. Staging Layer
The staging layer temporarily holds your collected data to clean, validate, and transform it. Different ETL tools come together to carry out data preparation tasks.
3. Modeling Layer
Structured data is stored here in an organized manner for analytical querying and reporting. Star schemas and Snowflake schemas are common data models for this layer.
4. Presentation Layer
Also known as the consumption layer, it offers a user-friendly interface for easy data access and analysis. You can use visualization tools, such as reports and dashboards, to interact with this information.
Components of Data Warehouse Architecture
A robust data warehouse is made of an intricate interplay of distinct components. Each component is important for transforming your accumulated raw data into refined intelligence.
They are interconnected aspects of a powerful analytical engine and must be understood to appreciate how a DWH seamlessly delivers accurate and actionable insights on time.
Here are the core components of data warehouse architecture:
1. Data Warehouse Database
The database (DB) is at the center of the warehouse and stores the data to make it reporting-ready. There are four main types of databases, including typical relational DBs, cloud-based DBs, analytics DBs, and data warehouse applications. Your organizational requirements will determine which database is right for you.
2. Data Integration
Different integration approaches transform the information for swift analytical consumption. The two common approaches are ETL (extract, transform, load) and ELT (extract, load, transform).
However, other approaches include data transformation and bulk-load processing. Choosing the right tool is important to determine extraction approaches, fill incorrect data, time consumption, etc.
3. Metadata
Metadata is data about your data, which is also stored in the repository. It includes all the information about the structure, use, source, content, and other DWH features. Business metadata has information about data related to storage point, structure, and accessibility.
4. Data Warehouse Access Tools
As DWHs are made of databases, they require access tools to tap into the stored data. While not all business units need these tools, they are still fundamental to the architecture. Some key access tools are data mining, query and reporting, OLAP, and app development.
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Types of Data Warehouse Architecture
More than 97% of business leaders highlight their intent towards using data for better decision-making processes and operations optimization.
Many businesses wish to adopt the right techniques and tools for using data correctly. Choosing the right data warehousing architecture can change your organization’s scalability, integration, and performance.
1. Single-Tier Architecture
The single-tier architecture is the simplest on the list and stores very little data. It is built atop a unified, centralized DB that accumulates data from various sources.
It reduces redundancies, which is also why a business with multiple data streams does not opt for it. All its components are present on a single system.
Single-Tier Components
- Source Layer: Raw data originates here from ERP systems, external data feeds, transaction processing systems, etc.
- DWH Layer: It is a specialized middleware that provides an integrated view of the data collected from the source layer.
- Analysis Area: You interact with your data here for analysis, reporting, and querying. It encompasses query interfaces, reporting software, and BI tools.
Single-Tier Benefits
- Easy to understand and simple
- Lower software and hardware costs
- Reduced redundancy
Single-Tier Drawbacks
- Limited performance and scalability
- Not very flexible
- Not apt for complex organizational needs
- Limited historical data
2. Two-Tier Architecture
Two-tier architecture is a step ahead of the simple, single-tier architecture. It has a data staging area where data cleansing happens to get it in the appropriate format.
The staging area separates the data sources and the warehouse, which was unavailable in the former type. It uses a database server and system for faster access to information for analysis.
It is difficult to manage gigantic data amounts with this design and is thus mostly preferred by small and medium-sized businesses (SMBs). It works well when the data volume is moderate, and the analytics and reporting needs are simple. Also, it enables direct integration between BI and DWH tools.
Two-Tier Components
- Source Systems: Works like a single-tier architecture.
- Staging Area: New intermediary layer where all the extracted data lands first. The cleansing, transformation, and integration happen here to make information load-worthy for the next stage.
- Data Warehouse: This is the core and stores data after its integration, transformation, and cleansing. Usually, a carefully designed Relational Database Management System (RDBMS) is used for analytical queries. The data here is often structured through the Snowflake or the Star schema.
- Analysis Layer: The analysis layer encompasses applications and tools that enable interaction between you and your data. You can explore the data to generate insights through different BI platforms, OLAP tools, query interfaces, data mining software, etc.
Two-Tier Benefits
- Better data quality due to the staging area
- Separates analytical workload and operational systems for better performance
- Effective resource allocation
- More scalable
Two-Tier Drawbacks
- Higher hardware and software licensing costs. Additional costs for skills ETL/ELT personnel
- More complex
- Possibility of delays between data generation and availability for analysis
3. Three-Tier Architecture of Data Warehouse
Organizations with large, complex datasets and diverse user needs usually go for a three-tier architecture of data warehouses. It is high-level architecture addresses the connectivity issues often seen in other architectures.
It follows a clearly defined and systematic data flow to transform the gathered information from raw to insightful.
It takes the principle of the two-tiered design a notch higher by introducing an additional layer of specialization by implementing data marts. Different business units have different requirements and analytical needs. For the data to be useful, personalized subsets are essential.
Let’s understand its three tiers.
• Bottom Tier
The foundational level that works as the data repository. Raw data comes in here from various internal and external source systems. It is then cleaned, integrated, transformed, and stored in the staging area for further use. ETL/ELT processes are the core activities in this part.
The metadata repository is an important component here, containing all the information about the data. The tier primarily prepares data correctly and makes it analysis-ready.
• Middle Tier
The analytical processing engine of the architecture, having one (or more) OLAP (Online Analytical Processing) servers. It binds together the bottom tier’s data storage and the top tier’s front-end tools. The integrated data is processed into a format you can optimize for complicated analytical queries.
You can also analyze data from various perspectives (or dimensions) because OLAP servers often sort data into multidimensional structures (such as cubes).
You have the liberty to carry out operations like drilling down, rolling up, slicing, and dicing. The layer makes optimizing query performance easier for analytical workloads.
• Top Tier
The client-facing layer includes multiple front-end applications and tools. You can interact with these tools and apps to analyze the DWH data. It could also be looked at as the UI and reporting suite.
Comprises various reporting and query tools, it enables you to formulate ad-hoc queries against the DWH and produce reports of different formats.
It has business intelligence platforms for different analytical capabilities like OLAP analysis, data mining, and dashboards. Google Sheets and Microsoft Excel are spreadsheet software that form connections with data marts or warehouses for analysis or visualization creation.
The tier drives data-oriented decision making by giving you access to insights from DWH.
Three Tier Benefits
- Different tiers focus on varying functionalities
- Highly scalable
- Security at all tiers
- Tool picking flexibility
Three Tier Drawbacks
- High complexity level due to separate implementation and maintenance needs for each tier
- Potential bottlenecks
- High development and maintenance costs
- Challenges in integrating all tiers
4. Data Mart Bus Architecture
The data mart bus architect is an approach to building a warehouse in a bottom-up manner instead of creating a huge, centralized warehouse.
A button-up approach is where many smaller, more focused warehouses called data marts are built. These tend to specific business process needs, like finance, sales, etc.
Bus matrix is an important planning tool for identifying the connection between marts and conformed dimensions.
Every single data mart needs its individual ETL/ELT process. Finally, you can access these marts through different business intelligence and reporting tools.
Data Mart Bus Benefits
- Faster access to analytical capabilities
- Easy to maintain
- Different data marts for different business needs
- Low initial investment
- High flexibility and scalability
Data Mart Bus Drawbacks
- Potential data silos due to improper management
- Distinct ETL/ELT processes may lead to more development and maintenance efforts
- Consistency challenges
5. Cloud Data Warehouse Architecture
Around 149 zettabytes of data were created, captured, and consumed in 2024 on a global level. The number will drastically multiply by 2028 and is forecast to reach over 394 zettabytes.
The increasing numbers have called for a change and have made cloud-based data warehouse architectures a huge hit in growth-oriented organizations. It is also a more agile, scalable, and cost-efficient alternative.
You no longer have to rely on on-premises software and hardware. Instead, everything has switched to the cloud, scalable infrastructure, and pay-as-you-go pricing plans.
Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform are the top three names in the industry.
Cloud-Based Components
- Cloud Storage: It is a scalable storage space for all sorts of data (semi-structured, unstructured, or structured).
- Computer Resources: These are scalable and provisionable engines for processing and analyzing stored data. These include serverless compute (Azure Functions, AWS Lambda, etc.), virtual machines (Azure VMs, AWS EC2, etc.), and managed DWH services (Google BigQuery, Amazon Redshift, etc.)
- Data Integration & Orchestration Services: Data from different sources is moved, transformed, and loaded to the cloud DWH. It includes messaging and queueing services (Azure Service Bus, AWS SQS/SNS, etc.) and ETL/ELT services (Google Cloud Dataflow, AWS Glue, etc.)
- Managed Data Warehouse Services: These cloud services are built for specific purposes, which they carry out with features like built-in analytics functions and columnar storage for performance optimization and scaling.
- BI & Analytics Tools: These frontend tools query, understand, and visualize data in the cloud DWH. The cloud providers either provide their own BI tools or integrate flawlessly with well-known third-party tools.
Cloud-Based Benefits
- Cost-effective due to pay-as-you-go models
- Less time to deployment
- Highly scalable and elastic
- Swift integration with cloud services like AI, ML, data lakes, etc.
- Good security level
- Global infrastructure and global availability
Cloud-Based Drawbacks
- Possibility of vendor lock-in and migration restrictions
- Require proper cost management knowledge
- Lack of direct control
Stepping into the Future – Real-Time Data Warehouse Architecture
Modern data warehouse architecture is the strategic foundation upon which all modern analytics and BI are built. To design a robust, scalable, and efficient system, understanding its core components is a must.
A well-architected data warehouse transforms your data into a single source of truth, enabling confident, data-driven decisions that propel your business forward.
At Aegis Softtech, we offer unparalleled expertise. Our team specializes in designing, implementing, and optimizing next-generation data warehouse solutions. Each of these solutions is tailored to your unique business needs for a high-performance foundation.
We are here to help you build a data warehouse that scales with your ambitions.
Don’t let architectural complexities hold you back. Connect with our experts to transform your data into your most powerful asset.
FAQs
Q1. What is ETL in a data warehouse?
ETL here stands for the process of extract, transform, and load. It includes extracting data from various systems, transforming it into a uniform format, and loading it into DWH for analysis.
Q2. What are the 4 features of a data warehouse?
A DWH is non-volatile, integrated, subject-oriented, and time-variant.
Q3. Is Snowflake an ETL tool?
Snowflake is not an ETL tool but a cloud-based warehousing platform supporting ETL and ELT processes.
Q4. What is the Snowflake data warehouse architecture?
The Snowflake data warehouse architecture is a cloud-native design. It separates storage and compute for independent scaling and enhanced flexibility.
Q5. What is the Enterprise Data Warehouse architecture?
An Enterprise Data Warehouse (EDW) architecture is a framework that structures how your organization collects, stores, processes, and makes data available for decision-making and BI.
