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Data Fabric Architecture

Data’s role as the backbone of any modern business can’t be overstated. Despite the rise in tools to support that, managing data effectively is only becoming more challenging, especially as it spreads across multiple platforms, applications, and locations (aka silos). 

This is where data fabric architecture comes in, offering a comprehensive, unified, and automated solution for managing, integrating, and analyzing data from multiple sources. This architecture can produce a significant reduction in time to insight while facilitating self-service capabilities for users at all levels.

Gartner defines data fabric as “an emerging data management and data integration architecture concept for attaining flexible, reusable, and augmented data integration pipelines, services, and semantics in support of various operational and analytics use cases delivered across multiple deployment and orchestration platforms.” Data fabrics support a combination of different data integration styles and utilize active metadata, knowledge graphs, semantics, and machine learning (ML) to augment various data integration, design, and delivery tasks—and, in some cases, completely automate them.

In this article, we will delve into the concept of data fabric architecture. This includes examining its value proposition, comprehending its structure, emphasizing the significance of metadata intelligence within the data fabric, discussing the challenges associated with implementing a data fabric and exploring methods to optimize value through data fabric architecture. 

Summary of key data fabric architecture concepts

Concept Description
What is a data fabric? A data fabric is a modern approach to data management and integration that utilizes active metadata, knowledge graphs, semantics, and machine learning to achieve flexible, reusable, and augmented data integration pipelines and services. It supports various operational and analytics use cases across multiple deployment and orchestration platforms and can automate many data integration tasks.
Data fabric benefits and use cases
  • Streamlined Integration: Automates and democratizes data integration for easier access
  • Boosted productivity: Enhances scalability and productivity by reducing data access time
  • Better collaboration: Improves cooperation between data producers and consumers
  • Robust governance: Automates data protection and governance using metadata
  • Advanced analytics: Integrates diverse data sources for improved decision-making
  • Diverse data delivery: Supports various data delivery styles, like ETL/ELT, CDC, streaming, and APIs
  • Adaptive modeling: Offers flexible schema inference and data modeling
  • Holistic processing: Facilitates the combined processing of transactional, operational, and analytical data
  • Cost optimization: Standardizes and automates data lifecycle for cost-effectiveness
Components of a data fabric architecture
  • Data integration and preparation
  • Metadata intelligence
  • Augmented data catalog
  • DataOps: governance, monitoring, and observability
  • Data discovery with enhanced semantics
  • Automation and recommendation engine
Metadata intelligence Metadata serves as the cornerstone of a data fabric. Its primary function lies in informing, recommending, and even automating future data integration tasks. This process, integral to the data fabric, elevates the role of metadata from passive to “active.” As “active metadata,” it is not merely cataloged but also analyzed and utilized to drive task recommendations and automation, thereby enhancing the overall efficiency and effectiveness of the data fabric.
Data fabrics, data meshes, and data products Data fabric architecture speeds up the value extraction from data by leveraging automation and metadata, making data product creation more straightforward. In contrast, a data mesh is an organizational method that boosts the value of data through team collaboration and data product utilization. Together, they democratize data product creation and enable smooth integration into a unified data system.
Challenges in the way of creating a  data fabric
  • Lack of metadata
  • Too many moving parts and excessive complexity
  • A lack of talent, training, and enterprise adoption
  • Difficulties integrating with legacy systems and solutions
  • Ensuring data security and governance posture across interacting systems
Maximizing value with data fabrics
  • Take an iterative approach.
  • Start with metadata collection.
  • Foster collaboration and team design.
  • Embrace DataOps and monitor and measure performance.
  • Utilize efficient data cataloging and discovery with a data marketplace.
  • Leverage fit-for-purpose tools to stitch together a data fabric.

The data fabric value proposition

In its most refined form, a data fabric is designed to seamlessly interweave data in any format or location, enabling effortless discovery and utilization by both people and automated systems. This is achieved by harnessing the power of metadata, machine learning, and automation. 

Consider the example of a rapidly expanding grocery delivery app that relies on disparate data from various sources. This data is managed by different teams and utilized to inform the decisions and actions of a broad range of stakeholders, all through an array of different tools. Without a holistic, unified approach to the complete data lifecycle—from integration and preparation to delivery—this complex scenario can become a potential data quagmire.

The data fabric model (source: Nexla)

The data fabric model (source: Nexla)

Let’s delve into how adopting a data fabric architecture can efficiently address and mitigate these challenges.

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Streamlined integration

In the case of the grocery delivery app, a variety of data sources are needed—from user app interaction data to inventory data to logistics data. Data fabric architecture addresses the scaling inefficiencies of “point-to-point” data pipelines by leveraging existing metadata and AI/ML recommendations to prevent overlapping pipelines and to deliver “just-in-time” solutions for all requirements. Automating and democratizing data integration through a data fabric architecture means that this diverse data can be collected, cleaned, and combined in a streamlined fashion. The end result is that data from multiple sources is readily accessible and can be used to drive business decisions and improve the user experience.

Diverse data delivery

A data fabric supports and automatically recommends various data delivery styles, like ETL/ELT, CDC, streaming, and APIs. For instance, real-time inventory data could be streamed into the app to show users the most accurate product availability status. At the same time, change data capture (CDC) can help track inventory changes over time, and APIs can be used to pull in data from external partners or vendors. All of this data, along with metadata, is classified, annotated, and tagged within an augmented data catalog that increases the organization-wide visibility of the diverse data assets held by the company.

Streamlined data integration and diverse data delivery with data fabrics (Source: Nexla)

Streamlined data integration and diverse data delivery with data fabrics (Source: Nexla)

Boosted productivity

With a streamlined data integration system in place, data access time can be significantly reduced. For example, the grocery delivery app’s analytics team no longer needs to spend excessive time requesting, cleaning, and integrating data. This enhances scalability and productivity, enabling the team to focus more on analyzing data and less on preparing it.

Better collaboration

Data fabric architecture improves cooperation between data producers (like the operations and logistics teams that generate delivery data) and data consumers (like the product team that uses this data to improve the app). This improved collaboration can lead to more effective decision-making and, in this example, a more successful grocery delivery app.

Robust governance

With a data fabric, metadata can be leveraged to automate data protection and governance. For the grocery delivery app, this could mean automatically categorizing and securing sensitive customer data, ensuring that it is used appropriately and kept safe.

Advanced analytics

By integrating diverse data sources, a data fabric can provide a complete, unified view of the app’s performance and user behavior. This comprehensive dataset can support advanced analytics, such as predictive modeling or AI-driven recommendations, which can ultimately lead to a better user experience and increased profitability for the grocery delivery app.

Adaptive modeling

Data fabric architecture offers flexible schema inference and data modeling. This allows the data model and, subsequently, the data pipelines of the grocery delivery app to adapt as the business grows, introducing new products or services, entering new markets, or adjusting operations. The ability to flexibly model data and adjust schema drift ensures that the system can evolve with the business. Data fabric architecture allows for deferred data modeling, which can better accommodate unstructured and experimental data and encourages a more collaborative approach to schema development and semantics between business and IT teams. 

Holistic and efficient data processing

With a data fabric, transactional, operational, and analytical data can all be processed together. For the grocery delivery app, this might mean integrating user purchase data (transactional), delivery times and routes (operational), and user behavior patterns (analytical) into a single, holistic view of the business.

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Dynamic pipeline scaling

Utilizing active metadata, a data fabric can auto-scale data pipelines, thereby eliminating bottlenecks during data transformation or output. In the grocery delivery app context, this translates to real-time adjustments in data processing capacity, anticipating and handling traffic surges during peak hours or events, thus ensuring continuous service and real-time insights.

Cost optimization

By standardizing and automating the data lifecycle, a data fabric can reduce the costs associated with data management. For the grocery delivery app, this could mean less time and resources spent on data cleaning and integration, lower storage costs due to efficient data organization, and more cost-effective data usage across the entire organization.

Data fabric components

Now that we understand the immense value that data fabric architecture can unlock, let’s look into the key components of a data fabric and how they come together.

Data fabric components (Source)

Data fabric components (Source)

Augmented data catalog

The foundational step in establishing a data fabric is the augmented data catalog, which utilizes AI/ML or inference engines to systematically inventory distributed data assets across various sources and targets. Think of it as a comprehensive marketplace for data that enables business users to discover, tag, and annotate data assets, providing a clear lineage graph of the data.

The data catalog primarily serves three roles, which are often referred to as the “three Cs”:

  • Curation: The data catalog creates an inventory of distributed data assets that maps out the entire data landscape.
  • Collaboration: By promoting data accountability and governance, the data catalog empowers analysts and line-of-business (LOB) users to collaboratively rank, profile, tag, annotate and assign trust models to data assets. Data stewards can then validate these curated data artifacts.
  • Communication: The data catalog enables users to share profiled data sets, supporting the development of robust queries and integration with analysis tools for improved workflow.

In essence, the data catalog makes data accessible and pertinent, marking the initial stage of data valuation and treating information as a strategic asset.

Metadata intelligence

To maximize the automation capabilities of the data fabric, it is essential to leverage metadata intelligence. A comprehensive data catalog should go beyond technical metadata and be able to collect, analyze, and share all forms of metadata. The data fabric should be capable of analyzing passive metadata to provide recommendations and automate future data integration tasks, transforming it into active metadata. 

The analysis and transformation process involves performing analytics on the collected metadata, the results of which are then used as input for machine learning algorithms that can facilitate the automation of data engineering and integration tasks. This includes recommending data sources to integrate, self-healing data integration jobs, optimizing execution engines, and creating optimal data integration pipelines. We discuss this in detail in a separate section below.

Metadata intelligence for data fabric automation (Source)

Metadata intelligence for data fabric automation (Source)

Data integration and preparation

With metadata captured, data fabrics assure proficient delivery of integrated data from disparate sources to consumers, dynamically adjusting data delivery styles. They tailor connectors to sources and interchange or amalgamate multiple data integration methodologies—such as ETL/ELT, API data integration, data replication, streaming, and data virtualization—responding to data volume and performance optimization requirements. These tasks can now be autonomously managed, offering self-service capabilities.

The data fabric system presents an intuitive interface for business teams, allowing them to process and prepare data before performing analytics. This includes a user-friendly, low/no-code platform for data preparation and operationalizing business experiments. Tools like Nexla show how data fabrics unify and automate data access from diverse sources while providing the ability to auto-detect data and offering a low/no-code data preparation platform.

Data integration and preparation in a data fabric (Source - Tableau box changed)

Data integration and preparation in a data fabric (Source – Tableau box changed)

Data discovery with enhanced semantics

Data discovery is the element of data fabric that simplifies locating required data amid a sea of terabytes or petabytes of data. It allows users to explore the available data sets or conduct searches, previewing data from varied sources to gauge its relevance to their analysis. Data navigation allows users to peruse categories of data, such as business subjects or data sources, that are often laid out hierarchically. Search functions have evolved from basic keyword searches to advanced, AI-powered searches with natural language processing (NLP)—a standard feature in augmented data catalogs within data fabric tools.

Data marketplaces further bolster this process, driving the efficient discovery, annotation, and tagging of heterogeneous datasets across the organization. This feature enhances the overall manageability and accessibility of data, making it a central pillar of a truly effective data fabric system. Nexla’s Data Marketplace capability emphasizes this data discovery capability and is the cornerstone of data fabric design.

Nexla Data Marketplace for data discovery (Source)

Nexla Data Marketplace for data discovery (Source)

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DataOps: Governance, monitoring, and observability

DataOps is an agile data management practice that encourages enhanced communication and integration within data flow systems and promotes automation among managers and consumers of data. In the realm of data fabric design, DataOps plays a pivotal role. It streamlines the management of various components like data pipelines, data environments, data transformations, and data mappings. By automating the design, deployment, and management of data delivery while ensuring robust governance and meticulous metadata handling, DataOps enhances the utilization and value of data in an ever-changing environment. Moreover, it boosts collaboration between data production and consumption teams, which is essential for maximizing the potential of the data fabric. To read more on DataOps and the best practices surrounding it, refer here.

Automation and recommendation engine

Once a data fabric has amassed sufficient metadata and converted it into active metadata, its ML engines begin to inform and automate various data integration and management tasks. This automation is classified into three facets:

  • Engagement: Facilitates easier data discovery and integration for novice integrators and semantic search capabilities for subject matter experts (SMEs) to understand data better.
  • Insight: Automates tagging, annotation, and dynamic schema recognition. It also aids in detecting anomalies, reporting, and identifying sensitive attributes pertinent to regulations like GDPR.
  • Automation: Corrects schema drifts automatically, integrates the most suitable data sources, recommends the best transformations, and enables self-service integration flows to production environments.

The figure below helps visualize how these components would come together in an end-to-end data fabric implementation with a solution accelerator like Nexla.

Data fabric automated data processing and management flow (Source)

Data fabric automated data processing and management flow (Source)

Metadata intelligence in a data fabric

Metadata intelligence refers to the application of advanced techniques and automation in utilizing metadata to enhance data engineering tasks. While the core aspects of data engineering may have remained relatively unchanged over the past decade, the manner in which these tasks are performed has evolved, with metadata playing a pivotal role. We can understand this intelligence better through various examples.

One involves utilizing the arrival timestamp of data to identify patterns and gain insights into instances of delayed data. By analyzing this metadata, data engineers can uncover trends and make informed decisions to optimize data processing workflows.

Another application of metadata intelligence is the inference of data schema from records, enabling the detection of schema drift and errors. This automated process helps maintain data quality and ensures the accuracy of subsequent data processing steps.

Additionally, metadata intelligence can be employed in identifying bottlenecks in data processing and automating complex data engineering tasks. It dynamically allocates resources, such as containers, network capacity, or memory, based on metadata analysis of underperforming areas. This process is further enhanced by enriching metadata with semantics through knowledge graphs, allowing for the exploration of multi-entity relationships and permitting a deeper understanding of data.

Simultaneously, metadata intelligence assists in data engineering by suggesting the next data sources to integrate, enabling self-healing capabilities for data integration jobs, and optimizing data workloads. It accomplishes the latter by automatically integrating common sources, choosing the most appropriate execution engine, or constructing optimal data integration pipelines such as ELT or ETL based on the workload’s demands.

Overall, metadata intelligence substantially streamlines data engineering, automating tasks, enhancing efficiency, and guiding data professionals’ decisions. The table below showcases some potential automation drivers based on this intelligence.

Metadata Automation
Credentials, rate limits, API docs Auto-generated connectors
Data samples, schemas, schema drift, logic Data as a product
Data characteristics, anomalies, transaction logs, past error events, and codes Automated data monitoring
Pipeline instrumentation and performance Auto-scaling pipelines and resource utilization
Query optimizer metadata Join/hash strategy

Data fabric, data mesh, and data products

Data fabric and data mesh are synergistic rather than competing solutions. Each brings unique strengths to the table that, when combined, provide a comprehensive solution for data management and use.

Data fabric, as an architectural approach, is a pioneer in automation and metadata inference. This technology expedites data product creation and management, providing a streamlined process for generating and leveraging these products. The data fabric architecture is pivotal for the automation that is crucial in data operational processes.

On the other hand, data mesh is a process, practice, and framework that promotes a domain-driven federated design. It encourages collaboration, allowing for the accelerated integration and use of data. The data mesh model delivers data products with optimized efficiency to a diverse array of domain teams while empowering domain users with self-service tools to create their own derivative data products. This approach not only supports comprehensive data governance but also cultivates a cooperative cross-functional environment.

Finally, DataOps brings scalability to the operational processes related to data, including development, production, monitoring, and scaling. DataOps notably benefits from the automation resulting from the data fabric architecture, further improving the overall efficiency of the data lifecycle.

Data products, in this scenario, serve as the binding glue between data fabric and data mesh architectures. As depicted in the figure below, the automated data product creation and data management achieved with data fabric lay the groundwork for the optimized delivery of data products to domain teams in a federated design and facilitate self-service data integration for domain teams. This symbiotic relationship between data fabric and data mesh, facilitated by data products, is fundamental to the successful operation of a modern, comprehensive data system.

Data fabric and data mesh working together with data products as the foundation (Source)

Data fabric and data mesh working together with data products as the foundation (Source)

Challenges with data fabric design

While data fabric architecture has significantly transformed data management, its successful deployment often encounters various obstacles, from technical bottlenecks to organizational culture issues. In the following section, we’ll highlight common issues that organizations might encounter while implementing data fabric architecture and offer possible strategies for surmounting them.

Lack of metadata

The initial stages of data management initiatives often involve a dearth of metadata, especially in on-premises deployments. Ensuring the quality of ingested and integrated data is also challenging, with poor data quality potentially leading to inaccuracies, inefficiencies, and poor decision-making. ML-assisted solutions within data fabric tools can be harnessed to continuously monitor data quality and capture metadata, mitigating these issues.

Integration with legacy systems

Data fabric implementations must interact with existing infrastructure and solutions, and this integration process can be complex and daunting. Moreover, data fabric design may need to accommodate these legacy systems, adding another layer of complexity.

Consider a phased approach involving careful assessment of existing systems and the strategic use of intermediary technologies. This, coupled with a gradual transition, continuous training, and robust collaboration between IT and business teams, ensures a smooth and effective integration process for the data fabric.

System complexity and assembly 

Data fabric architecture is not an off-the-shelf solution. It requires thoughtful design and development based on specific use cases and the selection of the right tools. Handling this complexity and the “assembly required” aspect can be daunting, considering the number of moving parts involved. Proper implementation requires a gradual transition, continuous training, and robust collaboration between IT and business teams, ensuring a smooth and effective integration process for the data fabric.

Lack of talent and adoption

Change management often presents significant challenges when implementing new technologies. Employees may resist adopting new tools due to a lack of understanding, fear of change, or both. Additionally, finding talent with expertise in complex capabilities (such as ML-enabled metadata inference, recommendation engines, or knowledge graphs) is a challenge. Solution accelerators like Nexla can help mitigate this challenge, simplifying the path to data fabric implementation with low/no-code capabilities, universal connectors, and automated metadata intelligence.

Data security and governance

Ensuring data security and privacy is a crucial concern when handling vast amounts of data from various sources. The risks of breaches and noncompliance with regulations like GDPR or HIPAA can pose serious threats. Efficiently handling personally identifiable information (PII) in automated systems is another significant aspect of this challenge.

Each of these challenges must be carefully addressed to successfully implement a data fabric design. However, despite these challenges, the benefits of a well-implemented data fabric can significantly outweigh the initial difficulties faced during the setup and transition period.

Maximizing value with data fabrics

The following best practices and recommendations can be implemented to maximize the value of data fabric architecture:

  • Adopt an iterative approach: Begin with your current data sources and systems. Utilize data fabric tools to automate the process, especially if the required skills are lacking internally. This iterative approach allows you to gradually integrate new data sets and refine your data fabric over time.
  • Prioritize metadata collection: The intelligent application of a data fabric hinges on comprehensive metadata collection. Gather and analyze all forms of metadata to enable improved data understanding, lineage tracking, and effective decision-making.
  • Foster collaboration and team design: Create a data product team with members from diverse areas, such as data science, engineering, design, and business analytics. This team should work collaboratively to design, build, and deliver efficient data fabric components, enabling a holistic view and use of data.
  • Embrace DataOps: DataOps principles are crucial to ensuring the smooth running of your data fabric. These principles help monitor and measure performance, making sure that data quality and delivery meet the needs of end-users.
  • Utilize an augmented data catalog: Data catalogs facilitate the discovery and retrieval of data assets within your data fabric. They provide a searchable resource for users to locate required data sets quickly. Consider the concept of a data marketplace, which can assist in curating data assets for easy access and use.
  • Use fit-for-purpose tools for data fabric assembly: Choosing the right tools is vital for effectively stitching together your data fabric. Leveraging purpose-built tools will ensure the successful integration of various data sources and systems. Nexla delivers state-of-the-art solutions for the seamless construction and integration of data fabric architecture and its associated elements. Through Nexla’s assistance, it is possible to envision and actualize an intricately integrated and automated data fabric system that is capable of transforming your data management landscape, as depicted in the following illustration.

Comprehensive data fabric design with Nexla (source)

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Conclusion

In this transformative digital era, data fabric architecture emerges as a promising paradigm, capable of weaving together disparate data sources into a single, easily accessible, and comprehensible structure. As we have seen, it brings to the table a variety of invaluable benefits, encompassing automation, democratization, and enhanced execution speed while also ensuring consistent metadata intelligence.

The seamless integration of data fabrics, data meshes, and data products augments the overall efficiency and resourcefulness of data management, allowing organizations to unlock untapped potential and innovative business solutions. Despite the challenges inherent in setting up a data fabric, the rewards are abundant and transformative as long as a measured, iterative approach is pursued.

In essence, data fabric architecture provides the backbone for efficient and effective data usage, fostering an ecosystem where every user can transition into a proficient data user. As we venture further into this data-driven era, it’s crucial to appreciate and leverage the power of data fabric architecture in fueling data-driven decision-making and innovation.

In closing, remember the words of W. Edwards Deming: “Without data, you’re just another person with an opinion.” In our interconnected world, the data fabric is not just an architectural option; it’s a strategic imperative, paving the path to agile decision-making and, ultimately, driving the future of digital innovation.

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