Comparing Data Lakes and Data Fabric: Use Cases to Inform Your Decision Making Process
In today's data-driven world, managing vast amounts of information scattered across different environments and business applications can pose significant challenges for companies. These challenges often revolve around data quality, regulatory compliance, and broader operational concerns.
To tackle these issues, companies are turning to innovative solutions like data fabrics. Centrica, a UK-based supplier of gas and electricity, is one such example, implementing a data fabric to streamline data discovery and analysis of billions of rows of data stored across disparate systems.
Data fabric and data lakes are two popular strategies in data management, each with its unique features and purposes.
A data lake is a centralised repository designed to store large volumes of raw data in its native format (structured, semi-structured, unstructured). It supports flexible schema-on-read processing and is primarily used for big data storage, data science, exploration, and batch or streaming ingestion. However, without strong governance, data lakes can become disorganised, often referred to as "data swamps."
On the other hand, data fabric is an architectural approach and unified data layer that integrates data across lakes, warehouses, databases, and SaaS systems, providing seamless real-time access, orchestration, and governance through AI/ML-driven automation. It serves as a self-service marketplace for data consumption, enhancing data discovery, integration, and governance while supporting multi-cloud environments and operationalising data pipelines.
Data lakes are best suited for storing large volumes of diverse raw data to support data science, machine learning training, and big data analytics where schema flexibility and cost-effective storage are priorities. In contrast, data fabrics are employed to create unified, governed, and easily accessible data environments that reduce data silos and manual efforts, enabling real-time insights, cross-system data integration, 360-degree views (e.g., customer profiles), and improved collaboration across business units.
AP Pension, a Denmark-based pension company, and Heritage Grocers Group, an American food retailer, are among the many companies that have adopted data fabric solutions. AP Pension aimed for a consolidated and democratized approach to analytical data, aligning with its principles and digital strategy. Meanwhile, Heritage Grocers Group implemented a data fabric complemented by an AI data analytics framework to gather and analyse point-of-sale (POS) data efficiently, studying consumer behaviours more accurately.
Wipro, an Indian-based multinational technology company, and Nestlé USA have also leveraged data lakes, integrating them with business intelligence platforms to establish consolidated data environments, implement data access controls, and accelerate business processes associated with reporting. Nestlé USA's initiative enabled 800-plus sales representatives to analyse in-store visits accurately, contributing to a 3% increase in sales.
In essence, data lakes focus on scalable raw data storage and processing, while data fabrics provide the integration, automation, governance, and unified access layer that make diverse data assets actionable and manageable enterprise-wide. Data fabric can be seen as a next-generation platform that builds on and extends beyond data lakes (and lakehouses) to solve broader data management challenges.
An AI-enabled data fabric can help a company build comprehensive customer profiles to gain a 360-degree customer view, better understand customer behaviour and preferences, and serve their needs more efficiently. It can also work as a central platform for managing, integrating, and analysing vast amounts of customer data, helping companies build comprehensive customer profiles and understand customer behaviour.
By adopting these innovative data management solutions, companies can streamline their data processes, make data-driven decisions more efficiently, and ultimately drive business growth.
[1] Gartner. (2021). Market Guide for Data Fabric. Retrieved from https://www.gartner.com/en/information-management/research/market-guide-data-fabric [2] O'Neil, C. (2020). What is a data lake and how does it work? Retrieved from https://www.techtarget.com/whatis/definition/data-lake [3] ZDNet. (2020). Data fabric: The next-gen data management platform. Retrieved from https://www.zdnet.com/article/data-fabric-the-next-gen-data-management-platform/
- Data fabric solutions, like those implemented by Centricia and AP Pension, aim to streamline data discovery and analysis across various systems, addressing the data quality and regulatory compliance challenges faced by companies.
- Data lakes and data fabrics are two popular strategies in data management, with data lakes providing scalable raw data storage and data fabrics offering data integration, automation, governance, and unified access.
- The disorganization of data lakes, which can result in 'data swamps,' contrasts with the structured, governed, and easily accessible data environments created by data fabrics.
- Data fabrics can help companies build comprehensive customer profiles, providing a 360-degree view of customers, understanding their behaviors and preferences, and serving their needs more efficiently.
- By adopting data-and-cloud-computing solutions like data fabrics, companies can drive operational efficiencies, reduce manual efforts, and make data-driven decisions more quickly, ultimately promoting business growth.
- To learn more about data fabrics, one can refer to Gartner's Market Guide for Data Fabric, O'Neil's article on data lakes, or ZDNet's article on the next-gen data management platform (data fabric).
