数据结构

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 Forrester, six data fabric layers constitute a framework that enables dataops.

1. Data Management layer: The data management layer is responsible for data governance and security of data.
2. Data Ingestion Layer: The data ingestion layer begins to stitch cloud data together, finding connections between structured and unstructured data.
3. Data Processing: The data processing layer refines the data to ensure that only relevant data is surfaced for data extraction.
4. Data Orchestration: The data orchestration layer critically conducts some of the most important jobs for the data fabric—transforming, integrating, and cleansing the data, making it usable for teams across the business.
5. Data Discovery: The data discovery surfaces new opportunities to integrate disparate data sources.
6. Data Access: The data access layer allows for the consumption of data, ensuring the right permissions for certain teams to comply with government regulations. Also, this layer helps surface relevant data through the use of dashboards and other data visualization tools.

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.

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:

• Enable self-service data consumption — With a fully integrated data fabric, users can visit the data warehouse, dipping into the data to extract what data is useful for them at the moment. In short, data fabrics quickly deliver specific insights to those most in need of it.
• Supports data through automated governance, data security and protection — An active governance layer, with policy enforcement, immediately ensures transparency and instills trust in data.
• Enables automated data engineering tasks — Data fabric automations eliminate human error and provide virtually any data access or data processing. Additionally, metadata can be used to optimize data access and delivery.

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.

• Innovation — Data fabrics are feature rich and can open new paths of innovation for enterprises. In particular, with the inclusion of AI and machine learning, data fabrics can accelerate data and analytics lifecycles.
• Preventative maintenance — Data fabrics and automations can be used to conduct preventive maintenance based on data analytics of schedules, performance, and various other data points that can be calculated in predictive models.
• Elimination data silos — One of the main purposes of data fabrics is to make visible and transparent all data sources, in effect level data silos.
• Deep insights — Data fabrics can compile data and distinguish different sets of related data from multiple sources, often viewing it from multiple angles. Applied in areas, such as customer satisfaction, analytics can be used to enhance each individual customer’s overall experience.
• Regulatory compliance — With the advent of automations, and data discovery, regulatory compliance has become manageable over millions of personal records, a feat not easily possible before.