As the world of cloud computing modernizes digitally and finds more efficient, security-driven ways to store data continues to evolve; we see the evolution of data architectures everywhere. If you’re in the technology or business industries, you’ve likely heard of data fabric.
In short, data fabric is a relatively new data architecture pattern that operates by linking different data sources in a compact cloud environment. Data fabric allows business applications, data management tools, and end-users to securely access data that your company stores in various target locations.
Data fabric technology secures access to varying data storage systems in any location, whether on-premises, in the cloud or in a hybrid or multi-cloud environment. Data fabric allows your APIs to enable two-way access to your stored data. In short, data fabric acts as a security layer that stretches across your applications and data assets to ensure smooth and easy entry to different systems.
The Purpose of Data Fabric
There are a few targeted purposes of data fabric architecture. Aside from controlled and widespread system security, data fabric focuses on metadata management, data reusability, cross-application access, data standardization and quality, and data discoverability.
Data fabric looks to eliminate the days of one-way integration, making it possible for companies on an ever-evolving portfolio of products to interlink and exchange data between applications. While data warehouse (DWH) and data lake technologies aim to break application information barriers, they typically offer better connectivity and cloud-based storage than anything. For example, the purpose of a data lake is to store data until it’s retrieved for further examination and analysis.
Big data is everything. To be blunt, data-driven companies have more success than those that are not because the answers to their setbacks and roadblocks are right in front of them. There’s no doubt that data is the future, and the rapid growth of big data is proof of that.
As businesses on a global scale continue to migrate toward new data management approaches, the birth of new architecture designed to help work through the constraints of DWH and data lakes is necessary.
The Adoption of Data Fabric Architecture
Data-driven companies show substantial growth in contrast to those that operate on different approaches. Businesses that focus on analytics can anticipate changes in the market and understand consumer intent, creating the ability to outlast the competition and design a flawless customer journey.
It’s no secret that investing in analytics pays off, so why are companies hesitant to take the plunge? Regardless of the circumstances, we naturally want to see positive results, and it’s not uncommon for business owners to overlook the technical constraints that accompany relying on a mix of outdated legacy systems and cloud-native solutions for data management.
New architectures, such as microservices, tend to catch the eye of many leaders as possible resolutions. Still, when it comes to data management and exchanges, many data solutions do not coincide.
The Three Layers of Data Information
Typical modern businesses have three ways, or layers, to produce data-consuming applications. These include on-premise legacy systems, data warehouses to store and organize some data, and cloud-based platforms or integrations.
Most legacy software likely relies on older connectivity standards, while modern applications use newer architectures. Companies typically extract and transform data and load it into a targeted destination, like a data lake or DWH.
Many businesses exist on a half-migrated way of life regarding cloud-based solutions. The desire to make the complete migration is due to the multi-purpose business systems and functions of cloud computing. Customer relationship management and essential needs like accounting and HR systems are interconnected in the cloud, creating a ton of valuable data that ends up in a connected data lake in its raw state or, again, stored in a DWH.
How can businesses stop existing halfway on various data storage and operational platforms? There has to be a way to establish a secure and practical connection between the three layers of data information and fully transform into an organized, data-driven business.
Enter: Data Fabric
This connectivity issue is the exact challenge that data fabric intends to solve. Data fabric is unlike DWHs and data lakes because it doesn’t require businesses to move their data. Instead, data fabric architecture aims for better data monitoring between these connected systems, including on-premise legacy systems, cloud hybrids, or data lakes and warehouses.
How Data Fabric Initiates Change
Today, there’s no shortage of data anywhere, especially in business. Most companies have an extreme amount of data coming in from various locations. It can be incredibly challenging to figure out where to put that data and how to approach the analytics.
Data fabric architecture can help reduce the burden that many companies face regarding the complexities of data and analytics. There is quite a bit that falls under this umbrella.
Company data has to be interoperable and, at the same time, remain compliant with data usage regulations and exhibit strict permissions. It can be hard to accomplish this level of regulated data access without overseeing many users.
Data fabric can help by enforcing the correct data governance practices automatically. The data fabric technology helps to standardize data formats and create codes for user access permissions and all usage rights. Data fabric is the perfect way to build siloed data infrastructures that offer insight into how different services and users consume company data.
Management and Distribution
Perfectly-timed access to data is essential for training AI models and predictive analytics solutions. Corporate insights are crucial for business leaders, but it’s challenging to deliver.
Even in major corporations, very few have analytics fully integrated into daily operations, which borderlines on absurd. Analytics is one of the essential components of making consumer predictions. The fact that giant, global companies don’t embrace them as they should proves that making the digital modernization leap isn’t something that happens overnight.
Data fabric can assist by centralizing data management, backed by data regulations and policies. Development teams can configure data fabric architecture to prevent unbalanced load allocation and optimize data workload assignments within your internal tech structure.
In this situation, data fabric allows users in any location to access the data they need at high speed. Data fabric architecture can provide the predictive analytics solution many companies need to thrive.
Few things are more important than data security, both from a consumer and business owner perspective. Dealing with leaked customer and sensitive business data is never desirable, but the rising rate of cyberattacks would suggest it’s never out of the question.
Security factors have made business owners incredibly reserved regarding which third parties they grant access to their data. Integrating additional partners into an already-sensitive business ecosystem is stressful and overwhelming, no matter how much experience you have in the business world.
As we move into a new way of doing, it will become impossible for companies to remain competitive while embracing a platform-based way of collaborating and exchanging data with differing organizations. It’s expansion at its finest.
Data fabric helps in the way of security by establishing standard security regulations for every connected API. As a result, this architecture can ensure consistent protection across all business data points, managing those security regulations from one platform. Data fabric has the potential to spark an ongoing evaluation of user access credentials and usage patterns. You will have the peace of mind of always knowing what is happening with your data.
With the big data boom came an influx of regulatory compliance rules that companies must follow. Almost every industry faces high regulation, especially healthcare and finance, as consumer data within these fields are undeniably sensitive. Specific constraints have come into play, and as a result, businesses tend to ditch their analytics projects due to the cost of isolating sensitive data.
Data fabric can help with compliance by allowing unified standards when transforming and utilizing collected data. Also, you can configure data fabric architecture to trace data, which is a factor required by compliance provisions. It helps you comply with changing regulations while using your data to increase revenue. You’ll always know where your data rests, stores, and who has access to it.
Data Fabric vs. Data Lakes and DWH
Data fabric architecture does not intend to replace data lakes and DWH. Instead, it complements the issues within these data storage methods while focusing on compliance, access, and implementing analytics.
Data lakes and warehouses each hold their own space in business data storage. Still, they’re full of restrictions, including swamping, a lack of data strategy and management, low tech maturity, limited scalability, and higher operational costs.
Data fabric can fill in the gaps presented by data lakes and DWHs and better connect any application that draws data from them. Data fabric forces a reassessment of management approaches while creating a consistent approach to managing data safely and securely to make sense for big data and big business. In short, there is more than one way to store your data.