Data Fabric vs Data Warehouse
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David Scott
Data and AI Practice LeadChoose the Right Data Solution for Your Business
Explore the strengths and capabilities of Data Fabric vs Data Warehouse to understand which approach best aligns with your data strategy and goals.
A Data Fabric and a Data Warehouse are both crucial components in modern data management, but they serve different purposes and have distinct characteristics. Here’s a comparison highlighting their differences:
Data purpose and scope
Criteria Data Warehouse Data Fabric Purpose Designed for storing and analysing large volumes of structured data, supporting BI activities like reporting and analysis. Provides a holistic architecture for managing and integrating data across diverse environments and formats. Scope Centrally stores historical data from various sources in a structured format, optimised for query performance and analytics. Focuses on seamless data integration, access, and governance across a distributed data landscape, regardless of location or format. Data integration and latency
Criteria Data Warehouse Data Fabric Integration Typically involves ETL (Extract, Transform, Load) processes to clean, transform, and load data into the warehouse from various sources. Emphasises real-time or near-real-time data integration and access. Utilises technologies like data virtualisation and data orchestration to integrate data without requiring physical movement. Latency Often batch-oriented, meaning data is updated at scheduled intervals. Supports real-time data access and integration, providing up-to-date information as needed. Ability to supply data for multiple workloads, not just reporting and analytics. Data types and sources
Criteria Data Warehouse Data Fabric Types Primarily handles structured data, often from transactional systems (e.g., ERP, CRM). Manages structured, semi-structured, and unstructured data. Can handle diverse data types including text, multimedia, logs, and sensor data. Sources Aggregates data from multiple internal databases and external sources but typically focuses on structured data. Integrates data from a wide variety of sources, including traditional databases, data lakes, cloud storage, APIs, and more. Data flexibility and scalability
Criteria Data Warehouse Data Fabric Flexibility Less flexible due to its predefined schema and structure. Changes in data sources or reporting requirements can require significant adjustments. Highly flexible, supporting dynamic schema changes and integration of new data sources without significant reengineering. Scalability Designed to scale for large volumes of structured data but may struggle with the agility required for rapidly changing data landscapes. Designed to handle exponential data growth across diverse environments, easily adapting to new data sources and formats. Data governance and security
Criteria Data Warehouse Data Fabric Governance Provides strong data governance within its structured environment, with well-defined access controls and data quality measures. Offers comprehensive governance across various data environments, ensuring consistent policies, data lineage, and compliance management. Security Ensures data security within the centralised warehouse environment, but can be limited when data needs to be accessed across different environments. Implements robust security measures across distributed data sources, maintaining consistent access controls and encryption standards. Use cases
Criteria Data Warehouse Data Fabric Ideal for Historical data analysis, BI reporting, batch processing, and structured query performance optimisation. Real-time data integration, unified data access across hybrid environments, enabling advanced analytics and AI, and managing a heterogeneous data ecosystem. In summary, while a Data Warehouse is a centralised repository optimised for structured data storage and analysis, a Data Fabric is a more comprehensive approach that provides integrated and governed data access across a diverse and distributed data landscape, supporting a wider range of data types and real-time use cases.
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