From Enterprise to Cloud: Exploring the Different Types of Data Warehouses

Learn the key differences between an enterprise data warehouse, cloud data warehouse, and a data mart.

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Data warehouses form the backbone of informed business decision-making. As a central hub for storing and analyzing large volumes of historical data, these systems empower organizations to gain valuable insights into trends, patterns, and key performance metrics from various sources.

As the data warehouse landscape has expanded, different types of data warehouses have emerged, each offering unique capabilities and benefits. Here, we will walk you through those different types of data warehouses so you can understand their differences and make the best choice for your organization and use cases.

Different types of data warehouses

When choosing a data warehouse type, consider your organization's data volume, analytical complexity, scalability needs, budget, and overall business objectives. The correct data warehouse will provide the foundation for effective data management, analysis, and decision-making.

Enterprise data warehouse (EDW)

An enterprise data warehouse (EDW) is a centralized repository that stores and manages an organization's data from various sources. It is designed to support complex queries, data analysis, and reporting across the entire enterprise. EDWs are ideal for large organizations with diverse data sources and a need for comprehensive, integrated data analysis.

Pros:

  • Provides a single, unified view of an organization's data

  • Enables complex, enterprise-wide data analysis and reporting

  • Supports data governance and data quality initiatives

Cons:

  • Can be expensive to implement and maintain

  • Requires significant upfront planning and design

  • May have longer implementation timelines

Ideal choice: Large enterprises requiring a comprehensive view of their data for strategic analysis and long-range planning.

Data Mart

A data mart is a subset of a data warehouse that focuses on a specific business function, department, or subject area. It is designed to meet the specific analytical needs of a particular user group or business unit. Data marts are often faster and more agile than EDWs, as they contain a smaller, more focused dataset.

Pros:

  • Faster implementation and time-to-value compared to EDWs

  • Lower cost and complexity than EDWs

  • Tailored to the specific needs of a department or business function

Cons:

  • Limited in scope and may not provide a comprehensive view of the organization's data

  • Can lead to data silos if not correctly integrated with other data marts or the EDW

  • May require additional effort to maintain data consistency and integrity across multiple data marts

Ideal choice: Businesses seeking targeted analysis for specific departments or teams. It is also well-suited for organizations starting their data warehousing journey.

Operational data store (ODS)

An operational data store (ODS) is a data warehouse focused on current operational data. It is designed to support real-time decision-making and operational reporting. ODSs typically contain a limited history of data and are optimized for fast querying and updating.

Pros:

  • Provides real-time access to operational data

  • Supports operational reporting and decision-making

  • Can serve as a source for other data warehouses or data marts

Cons:

  • Limited historical data storage

  • May not be suitable for complex, long-term data analysis

  • Requires careful design to ensure data consistency and avoid performance issues

Ideal choice: Businesses needing up-to-the-minute insights into operations, such as customer service, supply chain management, or transaction processing.

Cloud data warehouse

A cloud data warehouse is a data warehousing solution that leverages cloud computing infrastructure and services. Cloud data warehouses offer scalability, flexibility, and cost-efficiency compared to traditional on-premises solutions. They are ideal for organizations looking to minimize upfront infrastructure investments and scale their data warehousing capabilities as needed.

Pros:

  • Scalable and flexible to accommodate growing data volumes and changing requirements

  • Pay-as-you-go pricing model can be more cost-effective than on-premises solutions

  • Reduced need for in-house infrastructure management and maintenance

Cons:

  • Requires reliable internet connectivity to access the data warehouse

  • May have higher long-term costs for organizations with stable, predictable data volumes

  • Potential security and compliance concerns when storing sensitive data in the cloud

Ideal choice: Businesses of all sizes that want a scalable solution, fast setup, and flexibility to handle dynamic workloads. This is particularly advantageous for organizations with limited in-house IT resources.

How to choose the right data warehouse

Selecting the right data warehouse is crucial for the success of your data management and analytics initiatives. Consider the following factors when evaluating different data warehouse options.

Define your requirements

Start by clearly defining your data warehousing requirements. Consider the following aspects:

  • Data volume: Estimate the current and future volume of data you need to store and manage. This will help you determine your data warehouse's storage capacity and scalability requirements.

  • Data types: Identify the types of data you will be working with, such as structured data (e.g., transaction records), semi-structured data (e.g., JSON, XML), or unstructured data (e.g., text, images). Different data warehouses may have varying capabilities for handling different data types.

  • Query complexity: Assess the complexity of the queries and analytics you plan to perform. Consider factors such as the number of concurrent users, query response times, and the need for advanced analytics functions (e.g., machine learning, data mining).

  • Data latency: Determine your requirements for data freshness and latency. Some use cases may require near real-time data, while others can tolerate longer refresh intervals.

  • Business requirements: Align your data warehouse selection with your organization's business goals, such as improving operational efficiency, enhancing customer insights, or enabling data-driven decision-making.

Understand data sources

Identify the various data sources you need to integrate with your data warehouse:

  • Internal sources: Inventory your internal data sources, such as transactional databases, ERP systems, CRM platforms, and log files. Assess the volume, variety, and velocity of data these sources generate.

  • External sources: Consider external data sources that you may need to incorporate, such as social media feeds, market research data, or partner data. Evaluate the APIs, connectors, and data formats required for integration.

  • Data quality: Assess the quality and consistency of your data sources. Identify any data quality issues, such as duplicates, missing values, or inconsistent formats, that may need to be addressed during the data integration process.

  • Data governance: Review your organization's data governance policies and ensure that the data warehouse aligns with these requirements, including data security, privacy, and compliance standards.

Scalability and elasticity

Evaluate the scalability and elasticity of the data warehouse solution:

  • Vertical scalability: Assess the data warehouse's ability to scale vertically by adding more resources (e.g., CPU, memory) to handle increased workloads. This is important for managing growing data volumes and query complexity.

  • Horizontal scalability: Consider the data warehouse's ability to scale horizontally by distributing data and processing across multiple nodes or clusters. This enables the system to handle higher concurrency and throughput.

  • Elasticity: Evaluate the data warehouse's ability to automatically adjust resources based on workload demands. Cloud-based solutions often provide elastic scaling capabilities, allowing you to scale up during peak periods and down during low-demand times.

  • Performance impact: Assess how scaling actions may impact query performance and data availability. Ensure that the data warehouse can maintain consistent performance as it scales.

Security and compliance

Data security and compliance are critical considerations when choosing a data warehouse:

  • Authentication and access control: Evaluate the data warehouse's authentication mechanisms and access control features. Ensure that it supports secure user authentication and granular access controls based on user roles and permissions.

  • Data encryption: Assess the data warehouse's data encryption capabilities, both at rest and in transit. Look for solutions that offer robust encryption algorithms and key management options.

  • Audit and logging: Consider the audit and logging features available in the data warehouse. Ensure that it provides detailed audit trails and logs for tracking data access, modifications, and system events.

  • Regulatory compliance: If you operate in a regulated industry or handle sensitive data, ensure the data warehouse complies with relevant standards and regulations, such as GDPR, HIPAA, or PCI-DSS. Look for certifications and attestations that demonstrate the vendor's compliance.

  • Data backup and disaster recovery: Evaluate the data warehouse's data backup and disaster recovery capabilities. Ensure that it provides adequate data protection, regular backups, and robust recovery mechanisms to minimize data loss and downtime.

Cost structure

Understand the cost structure of the data warehouse solutions you are considering:

  • Storage costs: Assess the costs associated with storing data in the data warehouse. Consider factors such as the volume of data, data retention periods, and storage tier options (e.g., hot, warm, cold storage).

  • Data ingestion costs: Evaluate the costs for data ingestion, including data transfer fees, API usage charges, and any data transformation or cleansing costs.

  • Query and processing costs: Understand the pricing model for querying and processing data in the data warehouse. Some solutions charge based on the amount of data scanned or the number of queries executed, while others offer flat-rate pricing or reserved capacity options.

  • Additional features and services: Consider the costs for any extra features or services you may require, such as data integration tools, analytics platforms, or support and maintenance services.

  • Scalability costs: Assess how costs may vary as your data volumes and workloads grow. Evaluate the cost implications of scaling resources, both vertically and horizontally.

  • Long-term cost projections: Develop long-term cost projections based on your expected data growth, usage patterns, and business requirements. Compare the total cost of ownership (TCO) of different solutions over an extended period.

By taking a comprehensive approach to data warehouse selection, you can ensure that you choose a solution that aligns with your organization's goals, budget, and technical requirements. Remember that the correct data warehouse will provide a solid foundation for effective data management, analytics, and decision-making, enabling your organization to derive maximum value from its data assets.

Establish the data warehouse as a single source of truth with Segment

Segment enhances your data warehousing strategy by simplifying collection, streamlining data flows, and enabling downstream activation. Segment gathers data from various sources (websites, apps, CRM systems, etc.) and transforms it into a consistent format.

This standardized data is then sent to your data warehouse, ensuring data integrity and providing a reliable foundation for analysis.

Schematized data for better querying and performance

Segment understands the importance of well-structured data. Schemas with the format <source>.<collection>.<property> are created within your data warehouse, optimizing the organization of your data for efficient querying and improved analytic performance.

 

data-warehouse-schema

 

For instance, a schema like "website.pageviews.url" clearly indicates the data source (website), the collection (pageviews), and the specific property (url). This structured approach allows for faster querying, as the data warehouse can quickly locate and retrieve the required information based on the logical schema organization. Additionally, well-defined schemas enable better data compression and indexing techniques, enhancing query performance and reducing storage costs.

Leveraging Segment's schematized data approach, you can ensure your data warehouse is optimized for efficient querying and high-performance analytics. This will empower your team to derive valuable insights and make data-driven decisions easily.

Have fine-grained control over what data is sent to the warehouse

Segment gives you precise control over the data flowing into your warehouse. You can easily select which events, properties, and user traits to include or exclude. This helps prioritize relevant data, manage storage costs, and maintain data quality. 

 

segment-selective-sync

 

For example, you might only want to send data regarding high-value customer interactions, such as product purchases or subscription upgrades, while excluding less critical events like page views or email opens. With Segment's fine-grained control, you can define custom rules and filters to ensure that only the most pertinent data is sent to your warehouse, reducing noise and storage requirements.

By selectively controlling the data sent to your warehouse, you can strike the right balance between data value and privacy. This will enable focused analysis and insights while maintaining trust and security.

Activate data in downstream tools with reverse ETL

Segment's reverse ETL functionality empowers you to strategically activate insights from your data warehouse. Imagine personalizing marketing campaigns based on customer segments identified in your warehouse or retargeting website visitors with highly relevant content based on browsing behavior.

 

reverse-etl

 

For example, you could use reverse ETL to send customer data from your warehouse to your email marketing platform. This would allow you to create targeted email campaigns based on customer preferences, purchase history, or engagement levels. By leveraging the rich data in your warehouse, you can deliver more personalized and effective marketing messages, improving customer engagement and conversion rates.

Similarly, reverse ETL can feed customer data into your CRM system, empowering your sales team with valuable insights to tailor their outreach and prioritize high-value prospects. By synchronizing data between your warehouse and downstream tools, you can ensure that all teams can access the most up-to-date and comprehensive customer information, enabling data-driven decision-making across your organization.

With Segment's reverse ETL capabilities, you can unlock the full potential of your data warehouse, turning insights into action and driving measurable business outcomes through targeted data-informed initiatives.

 


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Frequently asked questions

 
  1. Data volume and scalability: Ensure the data warehouse can handle your current and future data volume and scale as your data grows.
  2. Data sources and integration: Verify that the data warehouse is compatible with your existing data sources and can easily integrate with your systems and applications.
  3. Query and analytics complexity: Evaluate the data warehouse's ability to support the complexity of your queries and analytics requirements.
  4. Security and compliance: Choose a data warehouse with robust security features that comply with relevant industry regulations and standards.
  5. Cost structure: Understand the data warehouse's cost implications, including storage, querying, and any additional fees, and ensure that they align with your budget and long-term cost expectations.

A data warehouse is a centralized repository that stores and manages an organization's data from various sources. It is designed to support complex queries, data analysis, and reporting across the entire enterprise. In contrast, a data mart is a subset of a data warehouse that focuses on a specific business function, department, or subject area, catering to the analytical needs of a particular user group or business unit.

Key differences:

  1. Scope: Data warehouses have a broad, enterprise-wide scope, while data marts are focused on specific business functions or departments.

  2. Size: Data warehouses are typically more extensive and comprehensive than data marts, which contain a smaller, more focused dataset.

  3. Implementation: Data warehouses often require more upfront planning, design, and implementation effort than data marts, which can be faster to deploy and deliver value.

  4. Cost: Data warehouses are generally more expensive to implement and maintain than data marts due to their extensive scale and complexity.

     

The three main types of data warehouses are:

  1. Enterprise data warehouse (EDW): A centralized repository that stores and manages an organization's data from various sources, designed to support complex queries, data analysis, and reporting across the entire enterprise.

  2. Operational data store (ODS): A type of data warehouse that focuses on current, operational data, designed to support real-time decision-making and operational reporting.

  3. Cloud data warehouse: A data warehousing solution that leverages cloud computing infrastructure and services, offering scalability, flexibility, and cost-efficiency compared to traditional on-premises solutions.

While data marts are sometimes considered a type of data warehouse, they are more accurately described as a subset or specialized version of a data warehouse focused on a specific business function or department.