A data fabric refers to an architecture and approach to data management that encompasses all data integrations in multiple data pipelines and cloud environments, one which uses smart data management and automated systems to govern the entire fabric. Data fabrics are sophisticated, and sometimes intermixed with approaches like data virtualization. However, data virtualization is but one tool used in a data fabric approach enabling the data fabric to reach out to different data sources and integrate its metadata with other data source metadata creating a virtual data layer that can be understood in real-time. Additionally, data fabrics integrate other considerations beyond the mechanical aggregation of data, such as data compliance. In this way, the data fabric is more encompassing than just ETL processes. A data fabric will also need to consider embedded governance and compliance logic, security and privacy measures, and greater data analytics to multiple end user roles.
Data fabric architecture is like a patchwork of a quilt, each tool fits together to form a specific data fabric to fulfill an organization’s needs. A company operating on a hybrid cloud data fabric, for instance, could use AWS for data ingestions, and subsequently use Azure for data transformation and consumption. These cloud providers are somewhat comparable, but not equally so. Data architects who are devising their own data fabrics must be intimately familiar with the capabilities and limitations of vendor products before tying them together.
According to dataops.
, six data fabric layers constitute a framework that enablesData fabric software is a unified data platform that integrates an organization's data and data management procedures. The following are required features of data fabric software.
Data fabric is used to simplify the monitoring and management of data wherever they are. This is not a new goal, however, data fabric is a new approach to achieving a fully visible unified enterprise data operations. Data fabrics leverage the best of all cloud, core, and edge environments, folding into the data fabric on-premise, private, and public clouds. From a centralized platform, teams can monitor all data sources, optimize data pipelines, and gain actionable insights into data operations.
The technical purpose of a data fabric solution is to abstract away the underlying data storage technology from typical Extract, Transform, and Load data collection processes. Several data trends are challenging traditional ETL processes, namely the exponential increase in data generation, collection, and storage. With ETL, the circumstance is that data resides in several silos, such as data lakes, data warehouses, etc. and must be extracted from those sources, transform data into usable formats, and load into a user-friendly system, like a data mart. As data continues to be automatically generated and collected by things such as cellphones, and IoT devices, more of this data remains unreachable, locked away in data centers.
The challenge to ETL tools is finding a way to automate processes more, such as data discovery, which is when the computer locates new data sources rather than an admin notifying the system, or potentially not updating the system and risk incomplete data analysis. Many ETL tools have kept up, however, some data circumstances simply are too vast for admin led ETL processes.
With a data fabric, thousands of data sources can be managed using automated data tools that simplify an organization’s data operations. Data silos that once plagued ETL strategies no longer need to do so.
Data is only useful once it has been placed in context, and then made accessible to users and applications of the company. Data fabrics implemented correctly do this. Three key business benefits of data fabrics:
Despite the ease promised by many data fabric tools, the following list marks common challenging points for data fabric implementation.
Data fabrics are relatively new, but given their immense capabilities and advancements, their abilities and potential use cases have yet to be fully discovered. The following are just a few use cases that data fabrics enable.