What is an enterprise data warehouse?
An enterprise data warehouse refers to a centralized data repository that stores all of an organization’s data from multiple sources across the entire enterprise. That is what makes it different from a smaller data warehouse, which is designed to support a particular department or a specific business area. In that respect, the significance of an EDW for an enterprise can’t be overstated: with all data in one place, organizations stand a better chance of enabling enterprise-wide visibility and advanced analytics.
It also makes sense to differentiate an EDW from a traditional database and a data lake.
In contrast to an enterprise data warehouse that stores both current and historical data for analytical processing and reporting, a traditional database is designed to support real-time or near-real-time transactional processing with highly structured data.
While an EDW holds only structured, cleansed, and transformed data optimized for business intelligence, a data lake, in turn, houses all types of data in any format—raw, unstructured, semi-structured, and structured—and supports both real-time and exploratory analysis.
Key components of enterprise data warehouse architecture
A typical EDW has the following components:
-
Data sources
DWHs consolidate data from multiple sources, including Excel spreadsheets, customer relationship management (CRM) applications, enterprise resource planning (ERP) software, transactional systems, internet of things (IoT) devices, social media platforms, and many other applications.
-
ETL Process
ETL stands for “extract, transform, load.” ETL tools are designed to move data from its sources into an enterprise data warehouse while converting it into a consistent format for efficient analysis. First, data is extracted from its source applications. Then, the extracted data is cleaned and transformed into a structured, usable format that is ready for processing. And finally, the transformed data is loaded into the EDW.
-
Data storage
As the ETL process is over, the processed data resides in the data storage layer in structured formats optimized for queries. For example, relational databases such as Oracle, SQL Server, or PostgreSQL have robust indexing and querying capabilities and store structured data in tables with predefined schemas. Modern data warehouses often utilize columnar storage, where data is stored by columns rather than rows, which is particularly efficient for analytical queries that aggregate large volumes of data but only access a few columns.
-
Metadata repository
Metadata is a detailed description of all data stored in a system. It contains information such as data definitions, data types, source systems, transformation rules, and table relationships and helps make data easily searchable and usable. The metadata repository supports data lineage tracking, thereby enhancing data governance.
-
Data access tools
Access tools are a critical component of an enterprise data warehouse as they enable business users and data analysts to seamlessly interact with data in an intuitive and user-friendly manner. Business intelligence (BI) tools such as Tableau, Power BI, Looker, and Qlik offer easy-to-use visual interfaces, drag-and-drop features, filters, and built-in analytics that allow non-technical users to create interactive dashboards, visualizations, and reports.
Types of enterprise data warehouse architectures
-
Traditional EDW architecture
The traditional data warehouse architecture relies on on-premises infrastructure and relational database management systems like Oracle, IBM DB2, Teradata, or SQL Server. This model offers strong data governance, standardized data models, and reliable, structured reporting through centralized control and batch processing. Yet, traditional EDWs struggle with scalability and adaptability—characteristics that are mission critical for modern, fast-paced data environments.
While it’s a more expensive option than a cloud data warehouse solution, it may be well suited as part of a hybrid strategy alongside cloud platforms or data lakes for government agencies, financial institutions, and other companies that need tighter data control or must comply with strict security or privacy regulations.
-
Cloud-based data warehousing
A cloud-based data warehouse is designed to run in the cloud and be managed by a cloud service provider. It offers a fully managed environment so that organizations don’t have to invest large sums of money in setting up and maintaining the hardware and software. Unlike the traditional on-premises data warehouse architecture, a cloud-based EDW represents a more modern, scalable, and flexible solution that supports multiple parallel processing, meaning multiple nodes process different queries at the same time.
On-demand scalability is one of the major benefits of a cloud-based data warehouse. Without hardware investment, companies can increase storage and computational resources as needed to manage data or query load spikes. Such adaptability makes it a perfect choice for enterprises with variable workloads or quick expansion.
Furthermore, these architectures offer integration with a wide range of cloud-native tools. These include data ingestion tools, ETL/ELT pipelines, business intelligence platforms, machine learning frameworks, and more. Additionally, they work with semi-structured data, which facilitates working with a variety of data formats without having to do a lot of complicated changes.Another key benefit is that cloud-based solutions expedite the deployment process, allowing organizations to set up and begin querying data in a matter of hours rather than weeks or months. Maintenance tasks, such as patching, performance tuning, and backups, are also automated.
Among the leading cloud-based platforms are Amazon Redshift, Google BigQuery, and Snowflake, all offering powerful, scalable solutions for data warehousing—but each with its own architecture, pricing model, performance characteristics, and integration strengths. Below is a comparison table of the most popular cloud-based EDWs.
| Feature | Amazon Redshift | Google BigQuery | Snowflake |
|---|---|---|---|
|
Architecture |
Cluster-based with decoupled storage and compute |
Serverless, fully managed with separated storage and compute |
Multi-cluster shared data architecture with separated storage and compute |
|
Performance |
High performance with proper tuning; requires manual optimization |
High performance; automatically allocates resources based on query complexity |
High performance with automatic optimization and scaling |
|
Scalability |
Manual scaling; resizing clusters requires downtime |
Automatic scaling; handles massive datasets without manual intervention |
Automatic scaling; supports multi-cluster warehouses for concurrent workloads |
|
Pricing model |
Pay for provisioned resources, regardless of usage |
Pay-as-you-go based on query and storage usage |
Pay for compute separately from storage; offers on-demand and pre-purchased options |
|
Data types supported |
Structured data; limited support for semi-structured data |
Structured and semi-structured data (e.g., JSON) |
Structured and semi-structured data (e.g., JSON, Avro, Parquet) |
|
Maintenance |
Requires manual maintenance and tuning; automated backups |
Fully managed with minimal maintenance; automated backups |
Fully managed with minimal maintenance; automated backups |
|
Security |
VPC isolation, encryption at rest and in transit, integration with AWS IAM |
Encryption by default, integration with Google Cloud IAM |
Encryption at rest and in transit, role-based access control, integration with cloud provider IAM |
|
Integration ecosystem |
Deep integration with AWS services |
Seamless integration with Google Cloud services |
Multi-cloud support; integrates with various cloud services and tools |
-
Data lakehouse
The data lakehouse represents a hybrid architecture that combines the scalability and flexibility of a data lake with the structure of a data warehouse, allowing users to leverage the best of both worlds. It is built on top of cloud object storage (like Amazon S3, Azure Data Lake Storage, or Google Cloud Storage) and uses open table formats like Delta Lake, Apache Iceberg, or Apache Hudi. This approach enables companies to store both raw and analytics-ready data in a single platform, which reduces data movement, eliminates data silos, and simplifies architecture. Furthermore, because this architecture approach is capable of supporting both batch and streaming data, it allows businesses to ingest real-time feeds (such as logs or data from the internet of things) and do analytics without having to duplicate data across several systems.
One of the most popular lakehouse architectures is the Databricks Lakehouse Platform, which integrates Apache Spark with Delta Lake. Designed with collaboration in mind, the platform provides a collaborative workspace for data scientists and engineers to work together using interactive notebooks. The platform is available on AWS, Azure, and Google Cloud and integrates with a wide range of data sources, tools, and BI platforms.
Best practices for designing an enterprise data warehouse from ITRex
Building an enterprise data warehouse is a complex process that requires a clear strategic approach. Following these industry best practices will help you make sure your EDW initiative will pay off.
-
Start with a clear business case and requirements.
When designing an enterprise data warehouse, the first question that arises is “why?” First, it’s important to explain what problem your EDW project might solve. Specifying exactly what your EDW will look like is also what you need to begin with to get the results you are aiming at. To achieve this, you need to gather requirements from business stakeholders, identify all data sources, and determine key metrics to track.
-
Choose the right architecture and technology stack.
At this stage, decisions are taken regarding the transferring and storing of data, namely whether it will be done on-premises or in the cloud. Whether you adopt a traditional, cloud-based, or hybrid architectural approach depends on your real-time needs, regulatory requirements, data strategy, workload types, and organizational structure. When choosing the right platform (Redshift, BigQuery, Snowflake), it makes sense to consider such factors as your existing ecosystem, data volume and complexity, budget, and technological resources you have.
-
Focus on data quality and governance.
It’s mission-critical that you determine the quality of your data before building data pipelines to ensure it’s accurate and consistent. To this end, implement rigorous processes for data validation, cleansing, and transformation. A clear set of data governance policies should also be defined. These policies should outline data ownership, data access rules, user authentication mechanisms, documentation standards, and auditability provisions.
-
Plan for scalability and performance optimization.
As the volume of data grows, your enterprise data warehouse must also scale to accommodate increasing workloads. It’s imperative that you plan for scalability from the get-go—both in terms of storage and compute—to ensure your system remains effective over time. Consider using data partitioning, indexing, and compression, which can improve the query processing performance and scalability of systems.
-
Ensure robust security and compliance measures.
Enterprise data warehouses frequently store critical information about customers and businesses, which makes security a non-negotiable requirement. Protection against unwanted access and data breaches can be achieved by the implementation of role-based access control (RBAC), data encryption (both while it is in transit and while it is at rest), and activity monitoring. Make sure the warehouse complies with industry-specific compliance standards like GDPR, HIPAA, or SOC 2.
With extensive experience in modern data architecture consulting, ITRex can help your organization craft a bespoke enterprise data warehousing strategy using industry best practices for building a future-ready EDW solution. Below you will find some of the case studies from our portfolio.
Enterprise data warehouse implementation: examples from the ITRex portfolio
Project 1: Data warehouse modernization for a telecom giant
A major EU-based telecom operator, serving over 15 million subscribers and processing billions of call detail records (CDRs), faced challenges with their aging, on-premises data infrastructure. Their large Oracle database supported thousands of outdated reporting flows, leading to long processing times, frequent script errors, and operational inefficiencies—all while working under strict budget constraints.
Looking to modernize without major spending, they partnered with ITRex, initially requesting five data engineers to support their in-house team. The collaboration quickly evolved as ITRex demonstrated deep understanding of telecom data, stringent compliance with security protocols, and seamless integration with the client’s internal culture.
The modernization effort led to significant, measurable improvements. Operational costs were reduced without increasing the budget, allowing the client to scale capabilities efficiently. Furthermore, we helped drastically improve data processing performance —tasks that once took hours are now completed in minutes or seconds, thanks to optimized scripts and streamlined reporting flows. Analysts gained direct access to raw data, improving report accuracy and reducing reliance on slow, outdated workflows.
With enhanced data warehouse layers and the addition of scalable data marts, the client achieved better analytics precision and faster decision-making. We established a secure, near-live testing environment, ensuring sensitive subscriber and CDR data remained protected during development and QA cycles. A portion of the database was successfully migrated to a Big Data platform on Azure, setting the foundation for cloud scalability and future innovation.
Project 2: Data warehouse transformation for a global creative marketplace
A top global platform for stock photos, videos, music, and other creative content faced mounting data management challenges as it scaled. As the content library grew beyond 60 petabytes, the company faced increasing data chaos—making the transition to Snowflake and the push for a single, reliable source of truth even more challenging.
To modernize and streamline their data operations, the company partnered with ITRex.
Our team worked closely with the client to rebuild their data warehouse on Snowflake. We started by auditing data sources to identify the most reliable inputs. ITRex then set up ETL processes using dbt and integrated real-time data from sources like Google Analytics and the client’s website.
After cleansing and structuring the data into layered formats, we developed dedicated data marts for business units including sales, HR, and finance. Custom reports and advanced visualizations were created using SQL, Looker, Tableau, and Python within Snowflake.
As a result, the client gained faster, more accurate insights, reduced reporting complexity, and improved agility in responding to market changes.
Project 3: Scalable data warehouse for a behavioral therapy EdTech startup
An EdTech startup focusing on behavior management and special education needed better visibility into their operations. With data scattered across ERP, LMS, and CRM systems, the company lacked a unified view of business performance and sought to use AI for automated analytics and reporting tailored to students’ unique needs.
ITRex built a scalable, cloud-based data warehouse on Microsoft Azure to centralize and streamline their data ecosystem. Using Azure Synapse Analytics and Azure Data Factory, the team handled ETL processes and implemented strong data governance across departments. Azure Machine Learning and AI Services were used to analyze behavior patterns and enhance educational outcomes for students with developmental disabilities.
The Azure-based data warehouse reduced manual data consolidation, improved data quality, and sped up access to insights. Teams became more agile and data-driven, while advanced analytics helped enhance educational services and student outcomes.
