Data fabric as a service is a category of integration as a service that provides a unified and consistent data layer across different data sources, platforms, and applications. Data fabric as a service enables data integration, management, governance, and analytics in a cloud-based environment, using a combination of technologies such as data virtualization, data catalog, data quality, data lineage, data security, and data orchestration.
Data fabric as a service is a solution for the challenges and opportunities of the data-driven era, where data is the most valuable asset for businesses and organizations. Data fabric as a service can help businesses and organizations to leverage their data assets, improve their data capabilities, and achieve their data goals.
In this article, we will explore what data fabric as a service is, what are its benefits and challenges, what are the best practices and strategies for implementing it, what are some examples and use cases of it, and what are the future trends and opportunities of it.
Data fabric is the future of data management. It is a platform that delivers a set of data services that provide consistent capabilities across a choice of endpoints spanning on-premises and multiple cloud environments.
Donna Burbank, Managing Director of Global Data Strategy
What Is Data Fabric as a Service and How Does It Relate to Integration as a Service?
Data fabric as a service is a type of integration as a service, which is a cloud-based service that provides data integration capabilities, such as data ingestion, transformation, mapping, and delivery, across different data sources, platforms, and applications.
Data fabric as a service goes beyond data integration, and provides a comprehensive data layer that covers the entire data lifecycle, from data discovery, to data preparation, to data analysis, to data governance. Data fabric as a service uses a data virtualization approach, which creates a logical view of the data, without moving or copying it, and allows users to access and query the data in real-time, regardless of its location, format, or structure.
Data fabric as a service also provides a data catalog, which is a metadata repository that stores and organizes the information about the data, such as its schema, quality, lineage, and usage. Data catalog helps users to find, understand, and trust the data, and to collaborate and share the data with others.
Data fabric as a service also provides data quality, which is a process of ensuring that the data is accurate, complete, consistent, and reliable. Data quality helps users to ensure that the data meets the standards and requirements for its intended use, and to identify and resolve any data issues or anomalies.
Data fabric as a service also provides data lineage, which is a record of the data’s origin, history, and transformations. Data lineage helps users to track and audit the data’s provenance, and to understand the impact and dependencies of any data changes.
Data fabric as a service also provides data security, which is a set of measures and policies to protect the data from unauthorized access, use, modification, or disclosure. Data security helps users to comply with the data regulations and laws, and to prevent any data breaches or leaks.
Data fabric as a service also provides data orchestration, which is a process of automating and coordinating the data workflows and tasks, such as data ingestion, transformation, delivery, and analysis. Data orchestration helps users to optimize the data performance and efficiency, and to enable the data-driven decision making and actions.
What Are the Benefits and Challenges of Data Fabric as a Service?
Data fabric is a strategic and competitive advantage for businesses and organizations. It can help them to leverage their data assets, improve their data capabilities, and achieve their data goals.
Mike Ferguson, Managing Director of Intelligent Business Strategies
Data fabric as a service can bring many benefits for businesses and organizations, such as:
Improved data accessibility and availability.
Data fabric as a service can provide a single and consistent data layer that can connect and integrate different data sources, platforms, and applications, and allow users to access and query the data in real-time, regardless of its location, format, or structure. This can improve the data accessibility and availability, and reduce the data silos and latency.
Enhanced data quality and reliability.
Data fabric as a service can provide a data catalog that can store and organize the information about the data, such as its schema, quality, lineage, and usage, and help users to find, understand, and trust the data. Data fabric as a service can also provide data quality that can ensure that the data is accurate, complete, consistent, and reliable, and help users to identify and resolve any data issues or anomalies. This can enhance the data quality and reliability, and increase the data confidence and value.
Increased data security and compliance.
Data fabric as a service can provide data security that can protect the data from unauthorized access, use, modification, or disclosure, and help users to comply with the data regulations and laws, and to prevent any data breaches or leaks. Data fabric as a service can also provide data lineage that can track and audit the data’s provenance, and help users to understand the impact and dependencies of any data changes. This can increase the data security and compliance, and reduce the data risks and liabilities.
Reduced data complexity and cost.
Data fabric as a service can provide data orchestration that can automate and coordinate the data workflows and tasks, such as data ingestion, transformation, delivery, and analysis, and help users to optimize the data performance and efficiency, and to enable the data-driven decision making and actions. Data fabric as a service can also provide data virtualization that can create a logical view of the data, without moving or copying it, and help users to access and query the data in real-time. This can reduce the data complexity and cost, and eliminate the need for data replication and storage.
Data fabric as a service can also pose some challenges for businesses and organizations, such as:
Data governance and ownership.
Data fabric as a service can create a unified and consistent data layer that can connect and integrate different data sources, platforms, and applications, and allow users to access and query the data in real-time. However, this can also raise some questions and issues about the data governance and ownership, such as who owns the data, who controls the data, who can access the data, and who is responsible for the data. Data fabric as a service needs to establish and enforce clear and transparent data governance and ownership policies and rules, and to ensure the data accountability and responsibility.
Data scalability and performance.
Data fabric as a service can provide a data virtualization approach that can create a logical view of the data, without moving or copying it, and allow users to access and query the data in real-time, regardless of its location, format, or structure. However, this can also create some challenges and limitations for the data scalability and performance, such as how to handle the large and diverse data volumes and varieties, how to ensure the data consistency and accuracy, and how to optimize the data query and response time. Data fabric as a service needs to adopt and apply advanced and robust data technologies and architectures, such as distributed and parallel computing, caching and indexing, and machine learning and artificial intelligence, to ensure the data scalability and performance.
Data integration and compatibility.
Data fabric as a service can provide a data orchestration process that can automate and coordinate the data workflows and tasks, such as data ingestion, transformation, delivery, and analysis, and help users to optimize the data performance and efficiency, and to enable the data-driven decision making and actions. However, this can also require some challenges and efforts for the data integration and compatibility, such as how to connect and integrate different data sources, platforms, and applications, how to map and transform the data schemas and formats, and how to deliver and consume the data outputs and insights. Data fabric as a service needs to leverage and utilize standard and common data protocols and formats, such as RESTful APIs, JSON, and XML, to ensure the data integration and compatibility.
What Are the Best Practices and Strategies for Implementing Data Fabric as a Service?
To implement data fabric as a service successfully, businesses and organizations need to follow some best practices and strategies, such as:
Assess the data needs and goals.
The first step of implementing data fabric as a service is to assess the data needs and goals of the business or organization, such as what are the data sources, platforms, and applications that need to be connected and integrated, what are the data capabilities and requirements that need to be improved and fulfilled, and what are the data outcomes and impacts that need to be achieved and measured. This can help to define the scope and direction of data fabric as a service, and to align it with the business or organization’s vision and strategy.
Select the data fabric as a service provider.
The second step of implementing data fabric as a service is to select the data fabric as a service provider that can offer the best solution and service for the business or organization, based on the data needs and goals. There are many data fabric as a service providers in the market, such as Denodo, Talend, Informatica, and IBM, that have different features, functionalities, and prices. Businesses and organizations need to compare and evaluate the data fabric as a service providers, and to choose the one that can meet their expectations and budget.
Design the data fabric as a service architecture.
The third step of implementing data fabric as a service is to design the data fabric as a service architecture that can support and enable the data layer across different data sources, platforms, and applications. The data fabric as a service architecture consists of several components, such as data virtualization, data catalog, data quality, data lineage, data security, and data orchestration, that need to be configured and integrated properly. Businesses and organizations need to plan and design the data fabric as a service architecture, and to consider the data volume, variety, velocity, and veracity, as well as the data governance and ownership, and the data scalability and performance.
Deploy the data fabric as a service solution.
The fourth step of implementing data fabric as a service is to deploy the data fabric as a service solution that can provide the data layer across different data sources, platforms, and applications. The data fabric as a service solution consists of the data fabric as a service provider’s software and service, and the data fabric as a service architecture’s components and configuration. Businesses and organizations need to install and activate the data fabric as a service solution, and to connect and integrate the data sources, platforms, and applications with the data layer.
Use and optimize the data fabric as a service solution.
The fifth and final step of implementing data fabric as a service is to use and optimize the data fabric as a service solution that can enable the data integration, management, governance, and analytics in a cloud-based environment. The data fabric as a service solution allows users to access and query the data in real-time, regardless of its location, format, or structure, and to discover, prepare, and analyze the data using the data catalog, data quality, data lineage, data security, and data orchestration. Businesses and organizations need to use and optimize the data fabric as a service solution, and to monitor and measure the data performance and outcomes, and to identify and implement the data improvements and enhancements.
What Are Some Examples and Use Cases of Data Fabric as a Service?
To illustrate how data fabric as a service can work for businesses and organizations, here are some examples and use cases of data fabric as a service:
Banking and finance.
A banking and finance company can use data fabric as a service to connect and integrate its different data sources, platforms, and applications, such as customer data, transaction data, risk data, regulatory data, and market data, and to provide a unified and consistent data layer that can enable the data integration, management, governance, and analytics. The company can use data fabric as a service to improve its data accessibility and availability, and to access and query the data in real-time, regardless of its location, format, or structure. The company can also use data fabric as a service to enhance its data quality and reliability, and to ensure that the data is accurate, complete, consistent, and reliable, and to identify and resolve any data issues or anomalies. The company can also use data fabric as a service to increase its data security and compliance, and to protect the data from unauthorized access, use, modification, or disclosure, and to comply with the data regulations and laws, and to prevent any data breaches or leaks. The company can also use data fabric as a service to reduce its data complexity and cost, and to automate and coordinate the data workflows and tasks, such as data ingestion, transformation, delivery, and analysis, and to optimize the data performance and efficiency, and to enable the data-driven decision making and actions. The company can use data fabric as a service to achieve its data goals, such as improving its customer experience and satisfaction, increasing its revenue and profitability, reducing its risk and fraud, and enhancing its innovation and differentiation.
Healthcare and life sciences.
A healthcare and life sciences company can use data fabric as a service to connect and integrate its different data sources, platforms, and applications, such as patient data, clinical data, research data, genomic data, and medical device data, and to provide a unified and consistent data layer that can enable the data integration, management, governance, and analytics. The company can use data fabric as a service to improve its data accessibility and availability, and to access and query the data in real-time, regardless of its location, format, or structure. The company can also use data fabric as a service to enhance its data quality and reliability, and to ensure that the data is accurate, complete, consistent, and reliable, and to identify and resolve any data issues or anomalies. The company can also use data fabric as a service to increase its data security and compliance, and to protect the data from unauthorized access, use, modification, or disclosure, and to comply with the data regulations and laws, and to prevent any data breaches or leaks. The company can also use data fabric as a service to reduce its data complexity and cost, and to automate and coordinate the data workflows and tasks, such as data ingestion, transformation, delivery, and analysis, and to optimize the data performance and efficiency, and to enable the data-driven decision making and actions. The company can use data fabric as a service to achieve its data goals, such as improving its patient care and outcomes, increasing its research and development, reducing its costs and errors, and enhancing its innovation and differentiation.
What Are the Future Trends and Opportunities of Data Fabric as a Service?
Data fabric as a service is a solution for the present and the future of the data-driven era, where data is the most valuable asset for businesses and organizations. Data fabric as a service can help businesses and organizations to leverage their data assets, improve their data capabilities, and achieve their data goals.
Data fabric as a service can also create new trends and opportunities for the future of data, such as:
Data democratization and collaboration.
Data fabric as a service can enable data democratization and collaboration, which is the process of making the data accessible and available to everyone, and allowing everyone to use and share the data for their own purposes and benefits. Data fabric as a service can provide a data layer that can connect and integrate different data sources, platforms, and applications, and allow users to access and query the data in real-time, regardless of its location, format, or structure. Data fabric as a service can also provide a data catalog that can store and organize the information about the data, such as its schema, quality, lineage, and usage, and help users to find, understand, and trust the data, and to collaborate and share the data with others. Data fabric as a service can empower users to become data consumers and producers, and to create and exchange data insights and value.
Data fabric is an essential component of any modern data architecture. It enables organizations to break down data silos and deliver data and insights at scale, speed, and agility.
Ravi Shankar, Senior Vice President and Chief Marketing Officer of Denodo
Data intelligence and automation.
Data fabric as a service can enable data intelligence and automation, which is the process of applying machine learning and artificial intelligence to the data, and automating the data workflows and tasks, such as data ingestion, transformation, delivery, and analysis. Data fabric as a service can provide data orchestration that can automate and coordinate the data workflows and tasks, and help users to optimize the data performance and efficiency, and to enable the data-driven decision making and actions. Data fabric as a service can also leverage and utilize advanced and robust data technologies and architectures, such as distributed and parallel computing, caching and indexing, and machine learning and artificial intelligence, to ensure the data scalability and performance, and to generate and deliver data insights and value.
Data innovation and differentiation.
Data fabric as a service can enable data innovation and differentiation, which is the process of creating and offering new and unique data products and services, and gaining a competitive edge and advantage in the market. Data fabric as a service can provide a data layer that can connect and integrate different data sources, platforms, and applications, and allow users to access and query the data in real-time, regardless of its location, format, or structure. Data fabric as a service can also provide data virtualization that can create a logical view of the data, without moving or copying it, and help users to access and query the data in real-time. Data fabric as a service can enable users to explore and experiment with the data, and to discover and create new data opportunities and solutions.
Conclusion
Data fabric as a service is a category of integration as a service that provides a unified and consistent data layer across different data sources, platforms, and applications. Data fabric as a service enables data integration, management, governance, and analytics in a cloud-based environment, using a combination of technologies such as data virtualization, data catalog, data quality, data lineage, data security, and data orchestration.
Data fabric as a service is a solution for the challenges and opportunities of the data-driven era, where data is the most valuable asset for businesses and organizations. Data fabric as a service can help businesses and organizations to leverage their data assets, improve their data capabilities, and achieve their data goals.
To implement data fabric as a service successfully, businesses and organizations need to follow some best practices and strategies, such as assessing the data needs and goals, selecting the data fabric as a service provider, designing the data fabric as a service architecture, deploying the data fabric as a service solution, and using and optimizing the data fabric as a service solution.
Data fabric as a service can also create new trends and opportunities for the future of data, such as data democratization and collaboration, data intelligence and automation, and data innovation and differentiation.
