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Building a data warehouse:
a step-by-step guide
March 11, 2025
The process of building a data warehouse encompasses a set of steps aimed at engineering a DWH system, which consolidates and stores corporate data from various sources for it to be used in business intelligence and data analytics initiatives.
Providing expert data warehousing services for over 15 years, Itransition helps companies build scalable DWH solutions to securely store their data and use it in a way that ensures informed decision-making, enhanced operational efficiency, and data democratization.
Core components to build a data warehouse
When building a data warehousing solution, businesses consider multiple factors, such as their analytics objectives, existing technology environment, the number of data sources to be connected to the data warehouse, the amount and type of information that resides in the source systems, and many more. Despite these variations, most DWH solutions share the following three components in common:
Source systems
- Enterprise applications like CRM, ERP, eсommerce systems, and so on
- IoT devices streaming sensor data
- Operational SQL/NoSQL databases hosting transaction data
- External data sources
Data integration & quality management layer
- A zone where the extract, transform, load (ELT/ETL) process is carried out to prepare data for storage in the DWH
Data storage layer
- A data warehouse database for keeping company-wide information
- Data marts (DWH subsets) created for specific departments or lines of business
Traditionally, an enterprise data warehousing solution serves as an indispensable element of a bigger ecosystem, a BI solution, becoming a useful source of structured historical data that can be transformed into value-driven insights. Besides a DWH system, a BI architecture also includes a data governance and metadata management system, OLAP cubes, and a data access layer.
Build a high-performing data warehouse with Itransition
Six steps for building a data warehouse
A data warehouse project roadmap differs from one company to another, as it heavily relies on business needs. Here, we present a typical framework that can be adjusted to a particular organization and its existing IT ecosystem, vision, and goals.
Business requirements definition
Start the data warehouse project by interviewing business users and in-house IT specialists (database administrators, operational source system experts, etc.) to identify:
- General company goals and those specific to particular business units
- Different sources that should be connected to the DWH system
- Established data management and data governance practices
- Data security requirements
- Key data management issues, such as inconsistent, low-quality, and missing data
- Data analytics and reporting requirements
- Data access and analytics tools
- Methods and metrics to measure the project’s success, such as the return on investment, improved productivity, and time to generate reports
Data warehouse conceptualization
Choose a suitable architectural approach to building a data warehousing solution between:
- Inmon’s (top-down) approach: Firstly, a centralized repository for enterprise information is designed, and after that, data marts are architectured. This approach is more time- and resource-consuming but boasts of strong data consistency, accuracy, and reliability.
- Kimball’s (bottom-up) approach: Dimensional data marts for specific analytics and reporting needs are developed first, followed by the incremental creation of a logical enterprise data warehouse. Kimball’s approach promises quicker deployment but can lead to data inconsistency, requiring IT specialists to repeat the ETL process.
- Hybrid approach: Combines the principles of both design methods, when an EDW and data marts are built simultaneously, to facilitate enterprise-wide data quality with the quicker delivery of business insights.
Scheme title: Inmon’s approach to data warehouse design
Data source: computerweekly.com — Inmon or Kimball: Which approach is suitable for your data warehouse?
Scheme title: Kimball’s approach to data warehouse design
Data source: computerweekly.com — Inmon or Kimball: Which approach is suitable for your data warehouse?
Deployment option & technology selection
Decide on the optimal deployment option – on-premises, cloud-based, or hybrid - which will directly impact your DWH system performance, security, cost, and scalability.
On-premise | Cloud | Hybrid | |
|---|---|---|---|
Infrastructure | Physical hardware, virtualized software, and networking equipment | Storage and compute resources provided by the cloud vendor | On-premises and on-cloud infrastructure |
Scalability |
| Instant both for downscaling and upscaling | Easier than for on-premises software |
Performance | Excellent query performance with minimal latency for local users | Fast, as resources are distributed across multiple servers, working in parallel | Efficient operation in the cloud and low latency of the on-premises infrastructure |
Cost | The need to acquire and maintain hardware, a data center (if there is any), purchase and renew software licenses, find IT resources, etc. |
| Expensive due to the need to buy software and hardware and pay for cloud resources |
Availability & disaster recovery | Requires costly and complex configurations for high availability | Built-in geo-redundancy and disaster recovery features, as well as maximum uptime | Eliminated data latency issues |
Security | High level of security, compliance with strict data regulations | Shared responsibility between a cloud provider and a company, compliance certifications are provided by cloud services | Stringent regulatory requirements compliance |
Then, select the optimal technology for each of the architectural components. While drawing up the tech stack, consider such factors as your current technological environment, planned strategic technological directions, the technical competencies of the in-house IT team members, and specific data security requirements.
Data warehouse environment design
-
To design a DWH solution that effectively ingests, processes, and stores corporate data, you have to
define data sources and analyze information stored there, including:
- Available data types and structures
- The volumes of data generated daily, monthly, etc.
- Its quality, sensitivity, and refresh frequency
- Perform logical data modeling, which entails arranging the company’s data into a series of logical relationships called entities (real-world objects) and attributes (characteristics that define these objects).
The key data models - a normalized schema, a star schema, a snowflake schema, and a data vault schema - differ in how fact tables and dimensional tables are structured to support analytics needs.
- Convert these logical data models into database structures, transforming entities into tables, attributes into columns, relationships into foreign key constraints, and so on.
- If required, design a staging area, which is a zone for temporarily hosting copied data and a set of processes to help you clean and transform it according to the business-defined rules. This area can be omitted if all data transformations occur in the data warehouse database itself rather than using the ETL process.
- Create data access and usage policies and establish a metadata catalog and business glossaries.
Data warehouse development & launch
- Customize and configure the selected technologies, including a DWH platform, data transformation technologies, and data security software.
- Develop ETL pipelines and introduce data security measures.
- Integrate the DWH components with each other as well as with the existing data infrastructure - data sources, BI and analytics software, dashboards, visualization solutions, and a data lake.
- Perform data warehouse performance and ETL testing, data quality verification (data legibility, completeness, security, etc.), and ensure users have access to the system.
- Ensure that end users can operate the new technology environment by conducting customized training for both standard and power users and preparing support documentation.
After-launch support, optimization & maintenance
- Ensure ongoing user support and education so that business users can understand how to use the DWH system and adapt to the changes it brings. Measure data warehouse performance and user satisfaction.
- Schedule regular performance and security audits to eliminate operational bottlenecks and ensure compliance with both internal and external data standards, such as GDPR or ISO.
- Analyze who accesses the data, when, and what actions they perform, the most common data utilization patterns and analytics queries as well as define obsolete data for archiving. Update the DWH solution constantly to get rid of vulnerabilities and bugs.
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Data warehouse benefits
An effective data warehouse has diverse use cases, from enabling centralized data management to separating analytics workflows from the operational ones and facilitating optimized business workflows. It stores data in large volumes and makes sure its access and usage are strictly governed. The successful implementation of such a repository promises multiple benefits.
Data consolidation
The DWH platform acts as a unified repository, creating a single source of truth where consistent, high-quality, and cleansed corporate data is kept.
Accelerated data-driven decision-making
As data management activities become automated and end-users get easy access to clean and reliable data, they can make fact-based decisions at the speed of business.
Improved data security & safety
Due to the unified approach to data governance, which defines policies for data access and usage and robust security mechanisms, the risk of data loss, breaches, and leaks is minimized.
Advanced analytics enablement
A DWH solution facilitates comprehensive data cleansing and transformation, which serves as a solid foundation for advanced analytics capabilities, such as data mining, machine learning, artificial intelligence, big data analysis, forecasting, and what-if scenario modeling.
DWH implementation challenges & solutions
The process of building a data warehouse is complex in its nature, which means the project implies various inherent risks. Yet, with a well-defined strategy and proper tools, the company can overcome these challenges and create a solution widely adopted across the organization, facilitating the implementation of self-service business intelligence and data democratization.
1 Ensuring data quality | Data combined from disparate sources can come in different formats and structures, leading to
incompleteness, duplication, discrepancies, and, consequently, erroneous analysis and incorrect business
decisions.
| Introduce rigid source-to-target data transformation rules, implement data cleansing and validation methods, and assemble a data governance committee responsible for overseeing the proper implementation of data governance practices within the company. |
|---|---|---|
2 Support for the growing volumes of data | The data warehouse design should allow for future business expansion and increasing data integration,
transformation, and storage needs without hampering system performance.
| Before committing to a particular DWH platform, create a strategy that factors in your current data management needs and your strategic goals, such as increasing data volumes and the number of users to access the DWH, as well as the aggregation of new data types, such as sensor information. In case your data management objectives are hard to predict, consider opting for a cloud or hybrid DWH solution. With the cloud-specific auto-scaling capabilities, elasticity, and pay-as-you-go models, you can minimize the risk of resource overprovisioning at the beginning, while still meeting your unpredictable scalability requirements. |
3 Data security & regulatory compliance | A data warehouse stores sensitive information that can be used for various malevolent purposes, be it
stealing the data for financial fraud or holding it for ransom, leading to data breaches, reputation
losses, and expensive lawsuits. Plus, strict laws governing data processing, storage, and retention apply
to businesses in various domains, from finance to healthcare.
| Guarantee robust data warehouse security and regulatory compliance with such data security mechanisms as role-based access controls, data encryption, masking, and anonymization, among others. When opting for a cloud solution or a DWH implementation partner, choose trustworthy companies that follow industry regulations and security standards when delivering their services. |
Leading data warehouse technologies to consider
Choosing the optimal software from numerous options available on the market determines the success of the overall project. To start the evaluation process, consider the top-performing companies marked as leaders in The Forrester Wave and Gartner Magic Quadrant reports. These data warehouse services and platforms are equipped with robust data integration features, vast data storage capabilities, and built-in connectivity with analytics and BI tools, such as Power BI or Tableau.
Features
- Has 90+ native connectors for on-premises and cloud data sources via Azure Data Factory
- Offers support for native HTAP via Azure Synapse Link
- Supports big data and streaming data ingestion and processing with the built-in Apache Spark and Azure Stream Analytics event-processing engine
- Native integrations with the Microsoft ecosystem, including Power BI, Azure Machine Learning, Azure Cognitive Services, and Azure Data Lake Storage
- Allows scaling storage and computation separately
- Offers built-in fault tolerance and disaster recovery capabilities such as automatic snapshots and geo-backup
- Default data security features including granular permissions on schemas, tables, views, individual columns, and procedures, multi-factor user authentication, and data encryption
- Meets compliance requirements for HIPAA, ISO 27001, PCI DSS, SOC1, SOC2, etc.
- Performance issues due to capacity, memory, and concurrency limits
- Steep learning curve
- Complicated workarounds and problem diagnosis
Pricing
- Separate pricing for serverless and dedicated resources and capabilities like data storage and snapshots, geo-redundant disaster recovery, and threat detection
- 2-month free trial
Features
- Offers the federated query capability and built-in cloud data integration with Amazon S3 to query and analyze data of any type, format, and size across operational databases and a data lake
- Allows ingesting and transforming data in streams and batches, within and outside the AWS services with AWS Data Pipeline, AWS Data Migration Services, AWS Glue, and AWS Kinesis Firehose
- Offers native integration with AWS analytics services, including AWS Lake Formation, Amazon EMR, Amazon QuickSight, and Amazon SageMaker
- Provides built-in fault tolerance and disaster recovery capabilities, such as automated cluster snapshots, snapshot replications, and continuous cluster monitoring and replacement
- Safeguards data with granular permissions on tables, multi-factor user authentication, and data encryption
- Meets compliance requirements for SOC1, SOC2, SOC3, PCI DSS Level 1, HIPAA, ISO 27001, etc.
- Allows for the decoupling of storage and compute resources
- Limited scalability and flexibility
Pricing
- Pay-as-you-go and on-demand pricing, as well as separate pricing for Serverless (charging only for the consumed compute capacity), managed storage, Spectrum (charged for the number of bytes), Concurrency Scaling, Reserved Instance, Zero-ETL integration, backup storage, and data transfer
- 2-month free trial
Features
- Offers native data integration with 150+ data sources via Cloud Data Fusion
- Comes with multi-cloud analytics support provided by Google BigQuery (Omni) to query data across AWS and Azure without copying data
- Native integration with Looker and the whole Google Cloud Analytics ecosystem
- Charges for cold and hot data
- Provides replicated storage in multiple locations charge-free by default
- Offers granular permissions on datasets, tables, views, multi-factor user authentication, and data encryption (by default)
- Meets compliance requirements for HIPAA, ISO 27001, PCI DSS, SOC1, SOC2, etc.
- Data processing performance roadblocks and delays in accessing or retrieving data
- Steep learning curve
- Complex pricing model
Pricing
- Separate compute and storage pricing and charges for using BigQuery Omni, BigQuery ML, BI Engine, and streaming reads and writes
- Free operations (load, copy, and export data, delete operations, and metadata operations)
- Free-usage tier for 90 days
Features
- Provides more than 145 different data connectors for on-premises and cloud data sources
- Integrates with Microsoft 365, Power BI, Azure Synapse Analytics, and Azure Data Factory
- Comes with embedded generative AI capabilities and a Microsoft Copilot chatbot for improved productivity
- Comprehensive intelligent data transformation capabilities to join, aggregate, and cleanse data, apply custom transformations, and more
- Offers out-of-the-box rich data orchestration capabilities to create adaptable ETL and data factory workflows
- Unifies the OneLake and lakehouse architecture, simplifying data access, management, analysis, and collaboration on this data
- Supports the deployment of AI models with Azure AI Foundry
- Ensures security with row-level access, automatic application of permissions, inherited data sensitivity labels, and Purview-powered governance
- Multiple geographies are not currently supported for data warehousing
- The need for manual deletion of certain files due to garbage collection restrictions
Pricing
- Separate pricing for the Microsoft Fabric Capacity plan and OneLake Storage
- Free Mirroring storage
- A 60-day free trial
Features
- 11 native integrations
- Is built on an open lakehouse architecture and is part of an integrated platform, the Data Intelligence Platform
- Provides Unity catalog’s built-in search functionality to find relevant tables, queries, and dashboards
- Offers an in-platform SQL editor that allows you to author, run, and schedule queries
- Incorporates AI that automatically suggests descriptions and tags for tables and columns and configures and tunes the platform for better performance with minimal human involvement
- Works on a vectorized query engine Photon that shows superior, world-record 100TB TPC-DS benchmark for performance
- Enables conversational interfaces in natural language
- The ability to store raw data like logs, texts, audio, video, and images
- Seamlessly integrates with modern tools, like dbt, Tableau, Power BI, and Fivetran
- Allows users to set up granular access to information and protect data with encryption, network controls, data governance, and auditing features
- Limited flexibility due to restrictions for specific features, functionalities, and APIs
- The inability to use languages other than Python and SQL
Pricing
- Premium and Enterprise pricing plans on the AWS cloud and a Premium plan on Azure and Google Cloud, each divided into three tiers that provide differing performance and set of capabilities
- 14-day free trial
Features
- 100+ supported connection types with the ability to create custom connectors
- Open SQL processing engine, Apache Spark, and Python connectivity for big data processing
- Fully automated data warehousing and transaction processing workloads
- The ability to build a DWH in Oracle’s public cloud environment as serverless or dedicated compute, storage, network, and database service, as well as run the DWH in an on-premises data center that connects to Oracle Cloud
- Auto-scaling, auto-tuning, auto-indexing, hybrid columnar compression, columnar processing, smart scan, and automatic optimizer statistics gathering for consistently high performance
- Built-in Oracle Real Application Clusters, parallel infrastructure, automated disaster recovery, and backups for maximum availability
- Data encryption, masking, and redaction, privileged user and multi-factor access control, and database activity monitoring and blocking
- Scalable and optimized in-database ML algorithms
- Disaster recovery is available only with an Enterprise license
- Scalability constraints
Pricing
- Costs depend on the deployment model (Serverless or Dedicated Exadata infrastructure) and infrastructure (Exadata Storage for ECPU or Backup Storage)
- 30-day free trial
- Oracle Cloud Free Tier
Get the right tech stack for your data warehouse with Itransition
Data warehousing services we offer
We offer a wide range of data warehousing services and various cooperation models, whether you need a cross-functional team of experts who can handle the end-to-end DWH implementation process or external specialists to strengthen your in-house team and assist with specific parts of the project.
DWH design
We build a data warehouse architecture based on the company’s requirements, existing systems, and data integration needs. After analyzing data flows, source systems, and established data management practices, we define DWH requirements, propose a suitable DWH tech stack, perform data modeling, and design ETL/ELT pipelines.
DWH implementation
For the DWH solution to support critical business processes and integrate faultlessly with the existing IT environment, we meticulously study the company’s needs, conceptualize a DWH solution, and design, build, and deploy a centralized repository, connecting it with the current corporate systems and tools, migrating data along the way.
DWH optimization & support
We ensure flawless DWH operation by optimizing its functionality and reconfiguring the elements that cause performance glitches and delays. We can also monitor how the DWH system functions and provide ongoing support in case some technical or user adoption issues arise.
Cloud DWH migration
We can migrate an on-premise data warehouse system to the cloud for greater flexibility, availability, security, and scalability. We develop an all-encompassing migration strategy that covers everything from the existing DWH setup audit and new cloud DWH solution conceptualization to risk mitigation and post-migration validation.
Build a high-performing, future-proof DWH solution
A modern, skillfully built data warehouse can help accomplish many of your current data management and analytics
objectives, including broken-down data silos, real-time analytics, interactive reporting, and safeguarded
corporate data. Yet, to make your data warehouse a long-term success, considerable investments and technical
expertise are vital.
By cooperating with a trustworthy DWH vendor with solid domain expertise, tangible data warehouse benefits will not
take long to appear.
FAQs
Building a data warehouse and integrating it into the existing infrastructure involves a substantial investment. The price depends on several factors, including overall project complexity, tech stack, data security requirements, the size of a data engineering team, their hourly rates, and others. Contact our experts to get ballpark cost estimates.
The core team is composed of a project manager, a business analyst, a data modeler, a data warehouse database administrator (DBA), an ETL developer, and QA specialists. Besides these key roles, other professionals can participate in the project as well, such as a data steward, a DWH trainer, a solution architect, and a DevOps engineer. It is worth noting that sometimes individual staff members can perform several roles.
The first and foremost reason for implementing a DWH platform is to create a unified repository for cleansed data consolidated from across marketing, sales, HR, financial, and other departments. This, in turn, enables centralized data management, business analytics, and strategic and tactical decision-making.
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