Listed as one of Gartner’s top ten data and analytics trends for 2021, a data fabric provides a way to alleviate the data management challenges that enterprises endure today. It also allows enterprises to cost-effectively leverage technologies such as artificial intelligence and the flexibility to scale at pleasure.
What is a Data Fabric?
A data fabric refers to a framework serving as an integrated layer of data and connecting processes. It comprises a unified architecture with technologies and services running on the architecture. A data fabric assists enterprises to oversee their data and aims to maximize the value of data.
To reduce time-to-insight and costs, enterprises use data fabrics to automate and optimize data management processes. Enterprises seeking to be data-driven crave the ability to provide their data analysts and data scientists with simple and quick access to vast data. To do this without infringing or compromising on governance, security, and privacy policies, they use data fabrics.
Enterprises are also using data fabrics to handle massive datasets across diverse locations faster. Data fabrics help them to optimize the whole data lifecycle, thus powering applications that need real-time analytics and creating an environment where machine learning and artificial intelligence can work much more effectively.
Additionally, enterprises are increasingly leveraging multiple clouds. With the realization that their data is spread across multiple clouds, these organizations are adopting data fabrics to manage all of their environments.
Data fabrics are also attractive to enterprises because they simplify data management, improving the speed of delivery of digital services. The result is not only digital transformation, but also the gaining of a competitive advantage. Furthermore, enterprises may use the scalability of data fabrics as a solution to facing the complex challenges of uniting various technology environments.
How Data Fabrics Manage Different Kinds of Data
Data fabrics ingest data from all sources to provide consistency across all the environments of an enterprise. These sources may be on-premises or cloud environments Such as Oracle, SAP, Azure, Google Cloud, AWS, and containerization technologies such as Kubernetes among other examples. All this data can benefit from a data fabric’s rich set of data management capabilities such as automation and faster development, testing, and deployment. Additionally, users may enjoy oversight of such data through the use of self-service data management.
Going beyond just data, data fabrics collect and analyze all forms of metadata to generate contextual information. For instance, a data fabric may use metadata such as business or technical metadata to identify and connect metadata relationships.
To present metadata in a way that is easy to understand, a data fabric builds a graph model after analyzing metadata for key metrics. The graphic depiction is based on relationships that are unique and offer business relevance. Such metadata is then used to train artificial intelligence and machine learning algorithms to improve data management automation as well as suggest further opportunities for better data management.
Data Fabrics and Effective Data Management
Overcoming data silos and data movement
Data silos are proving to be a data management challenge for enterprises today. Contextless and isolated data sources fail to provide the full picture as big data requires an enterprise to holistically have eyes on all data sources to generate actionable insights. Additionally, siloed data may have the same information stored in different databases, threatening data integrity.
Data movement in a traditional data management architecture copies data from a storage system to another, through intermediary servers. This approach is not impervious to errors and is time consuming. The implementation of a data fabric solves the challenges of isolation and data movement by offering a single environment for collection and access to data. It brings together disparate pieces of data from numerous systems to create a network of information to support the applications of a connected enterprise.
Also read: Top Data Management Platforms & Systems 2021
Rapid reaction to changes in data sources and volumes
Since enterprises are generating, consuming, and storing data at an all-time high, maintaining control over the volume requirements and ever-increasing data sources becomes a challenge.
However, through the use of a data fabric, enterprises enjoy a permanent and scalable mechanism that brings together all their data under a single platform. A data fabric enables enterprises to enjoy greater scalability and acclimatize to more applications, rising data volumes, and more data sources.
Support comprehensive end-to-end data management capabilities
Data fabrics should accelerate the business use cases relevant to an enterprise such as risk analytics, customer intelligence. To improve data management, a data fabric’s scope of end-to-end data management should at least encompass data ingestion, preparation, data catalog, integration, and security. Data fabric solutions with use cases that are fitting to an enterprise, as well as automated data management functionality provide even greater value.
Future-proofing data management infrastructure
To avoid periodically falling back into the data management nightmare of struggling to integrate, process, and transform data to generate trustworthy insights, enterprises have to keep up with the latest data management infrastructure. Through the use of data fabrics, existing connections and deployments can be maintained without disruption as it is seamless to introduce new endpoints, data sources, and new technologies.
Optimization and acceleration of data pipelines
Queries on databases with billions of records can take quite some time to return. With a data fabric, enterprises can minimize the effort and time put into data preparation, resulting in quicker time-to-insight, which is appreciated in today’s fast business world.
Data pipelines can be configured, tested, and set up for reuse to speed up data preparation. They can also be automated to automatically carry out data cleansing, transformations, masking, and other operations to increase the data preparation quality.
Reducing dependency on legacy solutions and infrastructures
The technology evolution of today has enterprises transitioning to newer technology, yet enterprises might still depend on legacy infrastructure. Why? Because such infrastructure may still contain critical data and enterprises may not have a suitable plan to move mission-critical data out of the legacy system. As such, enterprises may be tempted to maintain these infrastructures.
In addition to data being ‘trapped’ in these systems, maintaining legacy infrastructure has cost implications. It also presents a security risk as some applications may no longer be able to be updated, introducing points of vulnerability. Through an interconnected web of data, data fabrics ease data management by allowing enterprises to lower their dependence on legacy infrastructure by connecting such systems to modern cloud applications, data lakes as well as data warehouses.
Robust data integration
Data integration issues are a common pain point in data projects. The use of data fabrics can alleviate this pain point by being compatible with numerous techniques of data delivery. For instance, replication, data virtualization, streaming, and ETL among others.
Data fabrics offering robust data integration also improve the efficiency of data management by supporting all kinds of users, often business users and IT users. Furthermore, through ecosystem integration, an enterprise stands to deliver better business value and outcomes as a result of business process optimization and greater flexibility.
Centralized data security and governance
When data governance and security policies fail to be centralized, the intricacy of not only protecting, but also integrating an enterprise’s data increases exponentially. Data fabric solutions with centralized data security and governance policies are implemented consistently across on-premises, hybrid, cloud, and multicloud environments, thus improving the data governance capabilities of an enterprise.
The use of a data fabric eases the approaches to data stewardship, configuring roles for data cleansing or tracing the origin of data to ascertain the integrity and compliance of data.