Digital transformation using an Enterprise Data Warehouse (EDW) will enable your company to increase efficiency, adapt to market changes, and remain competitive in the long run.
According to Mordor Intelligence, the global Data Warehouse as a Service (DWaaS) market was valued at $4,97 billion in 2024 and is projected to reach $13.77 billion by 2029, with a compound annual growth rate (CAGR) of 22.60%.
What do these numbers tell us? At the very least, that the digital technology market is growing rapidly:
- Companies worldwide are increasing their investments in EDW and cloud technologies. If your competitors leverage EDW-based analytics, they can make strategic decisions faster, leaving you behind.
- The growth of the DWaaS (Data Warehouse as a Service) market indicates that many companies prefer cloud solutions to reduce infrastructure costs. This is especially important for businesses operating in highly competitive industries such as retail, finance, and manufacturing.
There are also other, less obvious signs that indicate the need for digital transformation and an EDW Warehouse:
- Lack of automation hinders your ability to respond quickly to business changes.
- Forecasting sales, expenses, or customer demand is difficult due to a lack of high-quality analytical data.
- Existing IT systems struggle to handle growing data volumes or store data across multiple platforms.
- Employees complain about long wait times for reports or frequent data errors.
- Business performance reports are inconsistent or incomplete. For example, marketing sees one set of numbers, while finance sees another.
And even if you haven't noticed these warning signs yet, that doesn't mean your company doesn't need EDW cloud integration.
We invite you to take a closer look at this topic and address the pressing questions. Let’s get our ducks in a row!
Understanding Enterprise Data Warehousing: Key Definitions, Challenges, and Real-World Examples
The concept of the Enterprise Data Warehouse (EDW) was born from the desire to organize data in the same way that libraries organize books - each volume has its place and is easily accessible.
Bill Inmon, a renowned American data management expert, is considered the founder of the EDW warehouse concept. As early as the 1970s, he introduced the fundamental principles of corporate data warehousing.
Fun fact: When EDW first emerged, data volumes were measured in megabytes or gigabytes. Today, EDWs handle petabytes (millions of gigabytes) of data. Fortunately, we now have top EDW tools for cloud infrastructure to manage this scale.
Bill Inmon coined the term "Data Warehouse" and defined it as:
"a subject-oriented, integrated, non-volatile data repository structured to support decision-making."
A key focus was placed on "subject orientation" (data is grouped by business topics such as sales, customers, and finance) and integration (harmonizing data from different systems into a unified format).
What is an EDW and its Benefits?
Simply put, an EDW (Enterprise Data Warehouse) is a centralized repository that consolidates data from various systems and provides access for analysis and reporting.
Think of it as Google Search, but for your company. You enter a query: “How has profit changed over the last three months?”
The EDW warehouse instantly retrieves data from all systems, processes it, and presents a clear answer. Of course, for this to work, the system must first gather, clean, and organize information from different departments - accounting, marketing, warehouse, and more.
Implementing EDW in cloud comes with numerous advantages. First and foremost, it serves as a Single Source of Truth, eliminating the "data battle" between departments. It prevents duplication, numerical errors, and conflicting metrics that influence business decisions.
No more situations where the warehouse reports 5 units sold, accounting records show only 2, and marketing insists they signed 10 preliminary contracts this month.
However, EDW design goes far beyond just data storage. It’s not just a knowledge library - it’s a powerful tool that can bring significant benefits to your company.
Accelerating business analytics and reporting. Processes that once took weeks can now be completed in minutes with Enterprise Data Warehouse. EDW provides centralized access to data for all stakeholders.
Improving decision-making quality through complete and up-to-date information, the ability to conduct deeper analysis, and identify hidden patterns. The EDW warehouse is optimized for handling complex analytical queries. You will be able to significantly reduce the time and effort needed to prepare reports, run "what if" scenarios, and generate forecasts. In the end, you get a higher quality result.
Increasing operational efficiency. For example: automatic sales report generation, optimization of marketing campaigns, improved inventory management. You will be able to quickly identify inefficiencies (excessive costs or performance issues) and take steps to address them.
Improving customer service. The EDW warehouse allows you to create a unified customer profile that combines data on their purchases, preferences, and interactions with the company. Based on this, you can create personalized product recommendations, proactively solve issues, and increase customer loyalty.
A comprehensive view of key business aspects. Enterprise Data Warehouse integrates data from multiple sources, such as transactional systems, external APIs, and even unstructured data (e.g., from social media). Without developing a Data Warehouse, you wouldn’t be able to obtain this kind of information by other means.
Supporting predictive analytics. EDW serves as the foundation for applying machine learning and artificial intelligence technologies. Thanks to unified data and historical trends, algorithms can more accurately predict customer behavior, inventory needs, risks, or other metrics critical to the business.
Compliance with regulatory requirements and data security. Enterprise Data Warehouse helps comply with international standards and regulations, such as GDPR, HIPAA, and ISO. A unified approach to data management ensures reliable storage, protection, and the ability to quickly provide data for audits.
Key Challenges in EDW Implementation
When seeking cloud Data Warehouse services, you likely don’t want to worry about the challenges faced by the team. For example, data coming from different sources (CRM, accounting, marketing) often has varying formats and structures. Let the EDW solutions providers handle that headache for you.
However, there are several other aspects that will be highly beneficial for you to understand if you plan to buy a cloud EDW solution:
- Not all employees are ready to switch to new tools. It’s like trying to replace an old, familiar instrument. Someone will definitely ask, "Why, it worked fine before?"
- EDW cloud migration services take time, just like the actual implementation of the new system. This doesn’t mean downtime, but it can lead to process slowdowns, which may seem critical in some cases.
- EDW setup pricing is a separate, often painful issue. Implementing a new system requires investments in licenses, hardware, and expertise.
- If the data in the existing systems contains errors, the EDW will inherit those problems. "Garbage in, garbage out," as analysts say.
You can certainly hire EDW consultants at any time, but keep in mind that this is a dynamic project. The system needs ongoing maintenance: updates, error corrections, and adaptation to new requirements. Therefore, it’s better to start thinking about finding a good team that can offer a comprehensive solution and provide post-implementation support.
Real-World EDW Use Cases and Examples
Walmart: Gigantic Data Analytics in Retail
Walmart uses Enterprise Data Warehouse to analyze millions of transactions daily. They track which products sell better in different regions, allowing them to instantly adjust their product assortment. For example, if they notice a sudden spike in umbrella sales in Nevada due to a rain forecast, EDW helps redirect stock from neighboring warehouses.
Bank of America: Flawless Financial Analytics
A great Enterprise Data Warehouse example! In the banking sector, this system is used to prevent fraud. For instance, Bank of America analyzes customer transactions in real-time to identify suspicious activities. Additionally, Enterprise Data Warehouse provides the bank with valuable information to make strategic decisions: developing new products and services, optimizing business processes, and managing assets.
Mayo Clinic: Applying EDW in Healthcare
Mayo Clinic uses a Data Warehouse to develop personalized treatment plans based on genetic data and patient medical histories. It also plays a crucial role in conducting medical research and developing new treatment methods.
Step-by-Step Guide to Building a Data Warehouse from Scratch
Building a Data Warehouse from scratch is a complex but structured process. It requires a clear approach that can be broken down into sequential stages.
We'll also explain the data models used at each stage. Understanding this is crucial for designing the warehouse correctly. With our structured approach, your Data Warehouse will become the foundation for analytics and strategic management of your company.
We’re confident that with our Step-by-Step Guide, you'll easily understand how to make a Data Warehouse.
Planning and Requirements for Building an EDW
Stage 1: Defining Business Requirements
When creating a Data Warehouse, how to? comes second. The first question is: Why?
Before diving into technical aspects, it's crucial to understand why your company needs a Data Warehouse and what problems it should solve. To achieve this:
- gather requirements from key stakeholders: executives, analysts, marketers, and finance teams.
- identify key metrics to track (e.g., revenue, customer lifetime value, inventory levels).
- determine data sources (CRM, ERP, IoT, social media, etc.).
By defining clear objectives, you set the foundation for an effective Data Warehouse that meets your business needs.
Stage 2: Analysis of Existing Data
The goal of this stage is to examine what data you already have and determine its quality.
How is it conducted?
analyze current sources: tables, databases, files.
check data quality: identify duplicates, errors, gaps.
map data flows: track where the information comes from, who uses it, and in what format it is stored.
Stage 3: Data Model Design
Designing begins with defining the logic of how data will be structured. Data Warehouse design best practices provide us with three main data models:
Conceptual Data Model - a high-level representation of data intended to understand business processes. It answers the question: "What data do we need?"
Logical Data Model - a more detailed representation where each entity is broken down into attributes, and relationships between them are established. It describes the structure of data without being tied to a specific platform.
Example:
The "Customers" entity may contain attributes such as Customer ID, Name, Email.
The "Sales" entity is linked to "Customers" through the Customer ID.
Physical Data Model - a concrete representation of data, including tables, indexes, keys, and other database elements. It is used to implement Enterprise Data Warehousing at the database level.
Stage 4: Selection and EDW Architecture Design
At this stage, decisions are made about how data will be moved and stored (on-premise server or cloud).
Enterprise Data Warehousing architecture can be:
Traditional - Data is first loaded into the EDW, cleaned, and then transferred to data marts.
Data Lake + EDW - A Data Lake is used for storing raw information, while the Enterprise Data Warehouse handles cleaned and aggregated data.
Cloud-based EDW setup - Examples include Snowflake, Google BigQuery, AWS Redshift, etc., which offer flexibility and scalability.
Data Integration, ETL, and Storage Solutions
Stage 5: Development of ETL/ELT Process
The fifth of the Data Warehouse steps is the stage of Extracting, Transforming, and Loading (ETL/ELT) data into the warehouse:
- Extract - Data is retrieved from various sources (e.g., databases, APIs, CSV files).
- Transform - Data is cleaned, normalized, and converted into the required format. For example, merging customer data from CRM and ERP into a single record.
- Load - Data is loaded into the warehouse (depending on the Enterprise Data Warehousing architecture, either into a raw zone or directly into a cleaned one).
Stage 6: Development of Data Marts
Data marts are subsets of data from EDW, tailored for specific users or departments. They are essential for ensuring quick access to analytics.
Testing, Deployment, and Optimization
Stage 7: Setting up Data Warehousing with Analytics and Visualization Tools
- Configuring BI tools: Tableau, Power BI, Qlik.
- Creating dashboards for key metrics.
- Ensuring all users have access to data in a convenient format.
Stage 8: Testing and Launch
Before implementation, it's necessary to ensure that the system works correctly and that data from all sources is loaded and displayed properly.
- Compare reports from the Enterprise Data Warehouse with data from the source systems.
- Conduct load testing to make sure the system can handle large volumes.
Stage 9: Support and Development
After the launch, the warehouse requires continuous monitoring of ETL processes and improvements. Update them according to changes in the business or data sources. Implement new technologies to enhance performance.
Designing Enterprise Data Warehouse Architecture and Schemas
Data warehouse schemas are ways of structuring and organizing data. They describe how data is arranged, how tables are linked, and how information flows are organized. Schemas serve as the foundation for efficient storage, processing, and analysis. They help developers and analysts quickly locate and understand the necessary information.
Key Components and Types of EDW Architecture
Key components can be interpreted in different ways. In our work, when developing the Enterprise Data Warehouse architecture, we highlight six:
- Sources - systems from which information is extracted: CRM, ERP, databases, IoT, files, APIs.
- ETL/ELT - the mechanism for extraction, transformation, and loading. These processes clean, normalize, and prepare data for storage in the warehouse.
- Warehouse - the central place for storing and managing data. It can be local or implemented EDW in the cloud.
- Data Marts - subsets of data from the warehouse optimized for specific departments (e.g., finance, marketing).
- Analytics Tools - BI platforms that use information from the warehouse to create visualizations and reports.
- Users - management, analysts, marketers, and other staff who use data to make decisions.
These key components form the foundation for EDW architecture examples, which we will review below (as schematic diagrams).
Data Modeling: Star Schema vs. Snowflake Schema
We almost always talk about two schemas: "Star" and "Snowflake." However, there is actually a third one - "Galaxy" or "Constellation." It is suitable for a complex organizational structure where data from different departments intersect. It allows working with multiple processes within a single data warehouse.
In fact, this schema is essentially the combination of several "Star" schemas into one. Therefore, we will not go into too much detail on this schema as an independent one.
There is also the "Data Vault" schema, which is a very complex Enterprise Data Warehousing architecture used for flexible management of changes in data. It consists of three components: hubs (key business objects), satellites (attributes of the objects), and links (relationships between objects).
Star Schema
It includes one central fact table that contains numerical data (e.g., sales amount, order quantity) and several dimension tables that describe the context of this data (e.g., customers, products, regions).
The dimension tables are denormalized, meaning that data in them is often duplicated to simplify queries.
Advantages:
- The tables are logically organized, making query creation easier.
- Facts are directly linked to dimensions, which reduces the number of joins.
- Convenient for BI tools (popular analytics platforms integrate easily with this schema).
This schema is suitable for business users who need simple and fast queries.
Disadvantages:
- Dimension tables contain repeated information, which increases storage volume.
- If the structure of the dimension table changes, it can affect a large amount of data.
Not suitable if the dimensions have many nested levels.
Snowflake Schema
In fact, this is an extended version of the "Star" schema, where the dimension tables are normalized. This means that the data is split into sub-tables to eliminate duplication.
Advantages:
- Data is not duplicated due to normalization, which reduces storage volume.
- It's easier to make changes to dimensions as they are split into sub-tables.
- Data is represented in a more structured and logical form.
Disadvantages:
- To retrieve data, more joins are required, which complicates queries.
- The increase in joins slows down analytical operations.
- Users must have a good understanding of the data structure to work with it effectively.
Data Quality, Consistency, and Schema Selection
When designing and choosing a schema for a data warehouse (EDW), the quality and consistency of data play a key role as they directly affect the accuracy of analytics, query performance, and the long-term sustainability of the system. The choice between the "Star" and "Snowflake" schema depends on the state of the data and business requirements.
If the data quality is low and contains many errors:
Opt for the "Snowflake" schema, as it allows for easier error correction in one table rather than in multiple places. Focus on data cleaning and normalization during the ETL process.
If the data is of good quality but the volume is small:
The "Star" schema would be more suitable, as it is faster and simpler to implement. Ensure data version control mechanisms are in place to avoid duplicates and contradictions.
If it is important to maintain data consistency across the entire company:
Choose the "Snowflake" schema due to its strict normalization, which eliminates the risk of inconsistencies.
If the analytics needs to be fast and easy to understand:
Choose the "Star" schema, as it requires fewer joins between tables, speeding up queries. For complex systems, implement regular data quality control processes to prevent the spread of errors.
If the company plans to grow data volume or frequently update data:
The "Snowflake" schema is better suited for scaling and reducing data redundancy.
Utilizing Cloud Services for Setting Up Your EDW Infrastructure
Modern cloud services enable companies to efficiently build and manage Enterprise Data Warehousing without the need to create complex local infrastructure. This is a powerful tool for storage and analysis. Key factors for successful implementation include:
- Choosing the right platform and good EDW cloud hosting.
- Ensuring security and compliance with regulations.
- Monitoring costs and optimizing resources.
Benefits and Considerations for Cloud-Based EDWs
- Flexible scaling of resources on demand
- No capital expenditures on infrastructure
- Built-in security and monitoring tools
- Automatic backup and recovery
- Access to modern technologies without additional investments
However, it's important to note that EDW resources for cloud architecture might not be suitable if the company has high security requirements, very large data volumes, or high workloads.
Cloud Setup, Security, and Scalability
For data security in a cloud-based EDW setup, the following is necessary:
- Data encryption: both at rest and during transmission.
- Access management: defining clear rules and user roles.
- Security monitoring: for threats and attacks.
- Compliance with regulatory requirements: data protection laws and regulations.
To ensure the scalability of the cloud-based EDW, the following is necessary:
- Automatic scaling: setting up a Data Warehouse and particularly computational resources based on load.
- Elastic infrastructure: using cloud technologies that allow quick addition or removal of resources.
- Optimization of SQL queries and use of indexes: to improve performance.
Cost Management and Performance Optimization in the Cloud
One of the most significant decisions is choosing the payment model. Based on our experience, here's the advice: for irregular queries, opt for the "on-demand" model. For predictable workloads, consider reserved instances, which offer discounts.
5 tips to optimize performance and reduce costs:
- Make sure to deactivate resources that are not being used (e.g., old tables or processes). This may not be relevant during the building a Data Warehouse from scratch, but it will be crucial in the future.
- Store long-term data in more cost-effective storage solutions (e.g., Google Cloud Storage or AWS S3).
- Rewrite slow queries: Minimize the number of joins and avoid resource-intensive operations in slow queries.
- Setting up a Data Warehouse should include query caching for data that is accessed often.
- Use partitioned and clustered tables to speed up query performance.
FAQ
Which storage method to choose?
On-premise Enterprise Data Warehouse: Suitable for companies with high security requirements.
Cloud Storage: Provides flexibility, scalability, and reduces hardware costs.
Hybrid Approach: A combination of on-premise and cloud storage for critical data.
What are the most popular services? EDW cloud technology overview
- Snowflake: A cloud data platform built specifically for working with Enterprise Data Warehouse architecture.
- BigQuery: A cloud analytics platform from Google, focused on working with big data.
- Amazon Redshift: A cloud data warehouse service from AWS.
- Azure Synapse: A cloud platform from Microsoft that combines data warehousing and analytics.
What are ETL and ELT, and how to choose between them?
ETL (Extract, Transform, Load): A process of extracting data from sources, transforming it into the required format, and then loading it into the warehouse.
ELT (Extract, Load, Transform): The information is loaded into the Enterprise Data Warehouse in its raw form, and transformations are done within the warehouse itself. This approach is commonly used in cloud environments (such as Snowflake, Google BigQuery), where resources are easily scalable, and for large volumes of data that require flexibility in processing.
How to choose a team for building a Data Warehouse from scratch?
You can read all the best EDW design guides, but the most important factor is relevant experience. Choose a team that can demonstrate its expertise and show in-depth knowledge in the areas of design, development, and implementation of data warehouses. Look for evidence of past successful projects, a solid track record, and the ability to solve complex challenges specific to your business needs.