Enterprise data warehousing:
architecture, types, best tools, and selection

Enterprise data warehousing: architecture, types, best tools, and selection

June 20, 2023

Enterprise data warehousing market overview

the projected enterprise data warehouse market size by 2030

Market Research Future

Scheme title: The most important data warehousing-related trends
Data source: yellowbrick.com — Key Trends in Hybrid, Multicloud and Distributed Cloud for 2021

What are the most important data warehousing-related trends for your company this year? Select all that apply.

Scheme title: Companies’ investments in analytics infrastructure
Data source: yellowbrick.com — Key Trends in Hybrid, Multicloud and Distributed Cloud for 2021

In the current climate of uncertainty, our investment in analytics infrastructure such as data platforms, data warehouses, etc. is:

6 components of an enterprise data warehouse

An enterprise data warehouse is more than a repository connected to your data sources (CRM, IoT devices, SaaS apps, etc.) on one end and to BI and analytics software on the other. In truth, an enterprise data warehousing solution is a comprehensive data processing and storage environment that consists of the following components:


Extract, transform, load (ETL) or extract, load, and transform (ELT) tools ingest information from the source systems and process it until it’s suitable for permanent storage. It’s one of the core elements for enterprise-grade analytics because companies have to rely on numerous data sources with different data types, models, and information generation speeds.

2 Staging area

A staging area is a temporary raw data repository between data sources and its permanent storage that hosts the data during the transformation stage. This element is typical for solutions built with the ETL approach but can be omitted if the transformations are performed in the data warehouse database.

3 Data warehouse database

Traditionally, an enterprise data warehouse database is a relational database where the integrated and subject-oriented business information is loaded into data models for analytical querying. This component also includes a metadata repository where an enterprise stores a map of its data for easy access and handling.

4 Data marts

Dimensional data marts are built to satisfy the analytics needs of specific user groups and departments, for example, sales and marketing, production, supply chain management, and finance. Data marts facilitate easier and quicker data access and analysis as they handle smaller, more applicable datasets. 

5 OLAP cubes

Deploying multidimensional OLAP cubes that store data in the pre-aggregated form helps overcome the limitation of relational databases and streamline data analysis. The data in OLAP cubes can be sliced and diced, drilled down, rolled up, and pivoted to handle various analytics requests of business users.

6 Data governance

The data governance component defines processes and policies for managing data quality and security, data modeling, metadata, data retention and backup, data usage, and user activity.

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Enterprise data warehouse architecture

Traditional enterprise data warehouse solutions are built according to the three-tier architecture, which includes:

  • Data warehouse server (bottom tier)
  • OLAP server (middle tier)
  • Data access layer (top tier)

However, the architectural approach should still be dictated by the company’s needs, so other design methods (e.g. a one-tier or a two-tier architecture) can prove more suitable in some cases.

Operational databasesSaaS applicationsFilesSocial mediaWebsiteData sourcesExtract, transform, and loadStaging areaData integrationExtract, transform, and loadMetadataEDW databaseData martsBottom tierOLAP cubeOLAP cubeMiddle tierReportsDashboardsData miningTop tier

Enterprise data warehousing functionality

An enterprise data warehouse is not a specific software type but an environment combining multiple technologies. Together, they enable the following functionality: 


    • Pre-built connectors to various cloud and on-premises data sources, including databases, business applications, flat files, feeds, web URLs, IoT devices, and ecommerce platforms
    • API libraries for custom connectors creation
    • Integration with business intelligence and analytics software, including big data analytics and ML tools
    • Integration with an operational data store and a data lake

    Enterprise data warehouse integrations

    To serve the needs of various users across the company, the enterprise data warehouse should integrate data from all sources defined by the established analytics objectives at the required granularity level. Among the most commonly integrated data sources are:

    CRM system
    External data sources
    CSV and flat files
    Project management software
    Corporate website and intranet
    Enterprise data warehouse
    Supply chain management software
    Ecommerce platform
    Accounting and finance software
    Marketing software
    ERP system

    Enterprise data warehouse types

    When setting up an enterprise data warehouse, businesses have to choose between a cloud, on-premises, or hybrid environment.








    An in-house or outsourced IT team on-premises deploys DWH on the local server

    A cloud data warehouse is hosted and managed in the cloud. All hardware-related costs, software setup, infrastructure audits, and maintenance are the provider’s responsibility (if a DWH is delivered as a managed server).

    A hybrid data warehouse is distributed across both cloud and on-premises environments

    Major pros




    Comprehensive control over the data warehouse hardware and software infrastructure  High availability and security Compliance with data regulations, which require keeping data onsite

    Quick deployment and fast and cost-effective storage and computational resources scaling up and out Minimized upfront costs due to a pay-as-you-go model High fault tolerance and disaster recovery due to the distributed nature of the cloud data warehouses

    Efficient operation in the cloud while meeting the strictest regulatory requirements and addressing data latency issues





    Heavy upfront investments for hardware acquisition, software licenses, IT resources, etc. Requires comprehensive capacity planning due to complicated scaling Requires an experienced IT team to keep the system running efficiently

    Failure to meet compliance requirements prohibiting cloud data storage   Lack of pricing transparency and complicated pricing structures (e.g., egress fees, extra pay for hot data storage, excess compute, geo-redundancy)

    High price due to purchasing hardware and software and paying for the cloud resources Requires solid expertise in development and maintenance

    Top tools for enterprise data warehouse solutions

    We recommend starting the data warehouse selection process by reviewing the solutions that are recognized leaders in the recent Forrester Wave and Gartner Magic Quadrant reports.

    • Direct querying of structured, semi-structured, and unstructured data from Amazon S3 for analysis without loading and transformation Seamless integration with the AWS analytics services and select AWS partners to ingest data from Salesforce, Google Analytics, Facebook Ads, Slack, Jira, Splunk, etc. Querying live data across Amazon Relational Database Service (RDS), Aurora PostgreSQL, RDS MySQL, and Aurora MySQL databases with the federated query capability Native support of semi-structured data  Native support of advanced analytics
    • Flexible separate payment for compute and storage resources with RA3-type nodes Dynamic concurrency scaling for extra compute power Continuous cluster health monitoring Manual and automated snapshots for disaster recovery Data access permissions applied to tables, multi-factor authentication, manually-enabled data encryption, dynamic data masking Compliance with HIPAA, ISO 27001, PCI DSS, SOC 1 Type II, and SOC 2 Type II
    Software category
    Cloud data warehousing
    On-demand pricing from $0.25/hour Amazon Redshift Serverless $0.36/RPU hour Managed storage pricing $0.024/GB/month Free trial

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    A guide to choosing an enterprise data warehouse platform

    As the field of data analytics matures, the diversity of data warehouse software technologies can become overwhelming. So when assessing enterprise data warehouse technologies, a company should consider multiple factors to select the right tech stack.

    Data volume

    To avoid overwhelming storage costs and enterprise data warehouse scaling inflexibility, consider your current data volumes as well as target data volume when choosing the platform. To make estimates, take into account annual information growth rates and your strategic data management and analytics objectives, like plans to support decision-making with new data sources, including big data.

    Enterprise data warehouse cost and timeline

    Enterprise data warehouse costs include various categories of implementation and maintenance costs, such as:

    Key factors

    Data volume, its type, complexity, and quality

    The number of data sources and their disparity

    The number of data flows and data modeling complexity

    Type of workload to support

    Data cleansing and transformation complexity

    Data security and compliance requirements

    Platform scalability, fault tolerance, and velocity

    Real-time reporting requirements

    Software license fees

    Solution management

    Enterprise data warehouse benefits

    Corporate information consolidation

    Integrating enterprise-wide information typically scattered across multiple systems, companies can perform analysis of cross-functional historical data and carry out business performance assessment, risk analytics, or strategic planning.

    Separation of operational and analytics workloads

    Adopting an enterprise data warehouse, companies eliminate running analytics queries against OLTP databases, which are extremely slow and can result in system failures, and improving analytics speed and accuracy. 

    Centralized data governance and management

    Setting up a unified approach to data governance and management alongside the enterprise data warehouse implementation, companies prevent data inconsistencies and redundancy, varying data quality, data access constraints, and compromised analytics results.

    Automated data management

    An enterprise data warehouse helps companies eliminate manual resource-consuming and error-prone data extraction, cleansing, and transformation, all  while streamlining data management workflows and cutting operational costs.

    Facilitated self-service BI

    An enterprise data warehouse allows users to set up self-guiding data management and analytics and free up data teams from routine analytics and reporting activities as well as help business users easily obtain the insights they need. 

    Advanced analytics facilitation

    An enterprise data warehouse serves as a solid and well-governed foundation for new analytics initiatives – big data analytics, predictive analytics, self-service BI, ML, and AI.

    Do not delay your EDW transformation

    Do not delay your EDW transformation

    As companies across industries try hard to embed data-driven insights into every business decision, customer interaction, and business process, data warehousing is gaining traction as a key enabler. There has never been a better time to get more value out of data, with the growing information volume, increasing computing powers, and more advanced and affordable technology. Since implementing an enterprise data warehousing solution requires solid expertise, consider bringing a trustworthy DWH consultant to your project. At Itransition, we offer a full range of DWH services to help companies design and introduce a scalable and future-proof data warehouse and use it to accelerate decision-making and gain a competitive advantage.

    Let us guide you through EDW adoption

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    Enterprise data warehousing FAQs

    Enterprise data warehouse vs data warehouse

    Enterprise data warehouse vs data mart

    Enterprise data warehouses vs operational data stores (ODS)

    Enterprise data warehouse vs data lake

    Enterprise data warehouse vs lakehouse

    What is a virtual data warehouse?