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What is a Data Fabric?

Data fabric architecture provides consistent data access and unified capabilities across distributed apps, systems, and environments. With the ability to provide large amounts of data across many different platforms extremely quickly, a data fabric provides automated, intelligent system integration to break down data and communication silos within your organization.

Apache Kafka was originally built to become the ultimate Data Fabric solution. Confluent’s complete data streaming platform + cloud-native Kafka was built to break down disparate lines of business each with its own technology vertical, interconnections, and duplicated data. These tools help to take the task of creating brittle transformations and the copying of data and turn it into a robust data streaming system built for performance and scalability.

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How it Works Data Fabric vs Data Mesh Benefits Challenges Why Data Streaming

How it Works

Data fabric enables your system to have a defined process to access and share data across distributed systems or disparate, multi-cloud infrastructure. It allows teams to have a single and consistent framework to manage how your systems are designed and set up to share data without it becoming siloed. It also allows your teams to select the tools and platforms they need to process, transform, and aggregate data to enable their line of business.

Data Fabric vs Data Mesh

While a data fabric and data mesh are often compared, the two should not be confused. Data fabric deals with the breaking down of information silos, while a data mesh architecture is structured to reduce bottlenecks with your data analysis procedure.

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Advantages of Data Fabric Architecture

Data fabric architecture allows data to flow across geographically diverse locations. Providing low latency, high bandwidth, and reliable communication, data fabric standardizes your data management across cloud, on-premise, and edge devices.

Here are the most common benefits of a data fabric architecture:

Simplified system integrations: get a single, unified view of all your data sources
Better business insights: get a complete view of your data, regardless of where it resides
Improved data governance: improved data access and control across distributed apps, systems, and users
Faster digital innovation: the ability to create new workflows and processes by subscribing to existing data and systems
Improved security and protection: being able to have system-wide controls and checks so you know who is accessing and processing your data
Data scalability and performance: ability to scale both in terms of data volume and number of producers and consumers

Challenges Implementing a Data Fabric

One of the biggest challenges in setting up data fabric typically boils down to a matter of timing. For most organizations, setting up a data fabric isn’t needed while they are small and their systems are relatively simple. However, as more systems are introduced, and additional locations (physical or virtual), a data fabric is key to helping companies scale and understand their data architecture.

As companies grow, so does the number of systems creating and accessing that data. This typically turns into disparate systems that are siloed from each other with brittle or finicky connections to share data. These systems often don’t scale and are difficult to maintain.

To solve these challenges, most companies turn to a system like Apache Kafka. Kafka provides a stream of events that any number of applications can subscribe to. It acts as a fault-tolerant storage system for your data that allows you to process and reprocess data as needed. Having Kafka as the central nervous system allows your system to easily scale and share data in real time, regardless of how disparate your systems are.

How Multi-Cloud Data Streaming Simplifies Data Fabric Architecture

There are six aspects you should consider when creating your data fabric:

Data management: specifically data governance and security
Data ingestion: how is your data is created or arrives in your system
Data processing: once your data is in the system ensure that only the useful or needed data is surfaced
Data orchestration: transforming, integrating, and cleaning of data for use in your system
Data discovery: providing a way for your business to find and connect different systems in the correct format
Data access: granting secure access to each application or service, including visualization tools

Confluent provides a unified solution for all six and allows the following benefits for your system:

Highly performant: provides real-time data streaming getting your data in and where it needs to be. It is also optimized for sequential writes and reads as well as the expiration of data to keep your system fast and performant
Scalable and elastic: enables the addition of capacity in a cloud service as needed to meet spikes in demand, and subsequent reduction during periods of low usage.
Faster time to market for applications: built-in tools to replicate and set up multi-regional locations with minimized latency
Off-the-shelf and support for build-your-own connectors: the ability to connect the services and systems you need and get the data where it needs to be

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