In the AI ​​era, NoETL alone is not enough

In the AI ​​era, NoETL alone is not enough  -  the new paradigm of data management calls for a data weaving architecture.

In the AI ​​era, data has become the core fuel for driving intelligence. In order to gain insights from data faster, NoETL ( NoExtract, Transform, Load ) is an emerging data integration concept that emphasizes the use of data virtualization technology to achieve " ready-to-use " , reduce data duplication, and improve data access efficiency. However, although NoETL has broken through some of the limitations of traditional ETL in data integration, it is far from meeting the all-round requirements of AI for data management.

1. New requirements for data in the AI ​​era

Artificial intelligence systems, especially large models, require not only access to raw data , but also high-quality, semantically clear, and contextually relevant data assets . This leads to the five core requirements for data in the AI ​​era:

lSemantic  understanding and unification: Data must have unified business semantics to facilitate AI understanding, normalization, and reasoning. 

l  Dynamic governance and quality assurance: Data must have real-time data quality assessment and governance mechanisms to ensure the credibility of training and reasoning. 

l  Context awareness and relationship modeling: The associations between data need to be explicitly expressed so that AI can establish causal relationships and knowledge chains. 

l  Real-time and flexible access: Data must have low latency and high concurrency access capabilities to meet the AI ​​system's demand for real-time data. 

lSecurity  compliance and fine-grained control of permissions:  AI systems need to process highly sensitive business data, and permission management must be strict and flexible.

NoETL only solves the problem of " how to access " , but is unable to respond to questions such as " whether the accessed data is meaningful, reliable, inferable, and combinable " .

2. Capability boundaries of NoETL

Typical NoETL representatives such as Trino and Presto query engines can achieve cross-source query and logical integration, but they have the following significant shortcomings:

l  Lack of unified metadata view: does not support automatic perception of data structure, field meaning and business semantics. 

l  Unable to support knowledge-driven data access: Queries need to be written manually, and there is no question-oriented question answering supported by knowledge graphs. 

l  Lack of governance capabilities: Lack of built-in support for data quality, sensitive information identification, data lineage, etc. 

l  Coarse-grained permission management: It is difficult to handle fine-grained permission control and dynamic authorization in complex enterprise environments. 

l  Lack of native AI capabilities: There is no embedded support for AI Agent or large model calling capabilities, and data insights cannot be discovered proactively. 

3. Data Fabric : Data Infrastructure in the AI ​​Era

DataFabric , as a new generation of data management architecture, makes up for the shortcomings of NoETL and provides full-chain capabilities from " access " to " understanding " , " governance " and " integration " :

lBuilt  -in semantic fusion and knowledge graph: Unify the modeling of metadata, business vocabulary, and attribute vocabulary to achieve semantic-level data access. 

l  Active AI hub: supports large model / agent access and has capabilities such as automatic query, data insight, and graph reasoning. 

l  Intelligent governance engine: perceives data quality, field sensitivity, life cycle, etc. in real time, and automatically recommends processing strategies. 

Unification of  logical integration and physical integration: Combine NoETL 's virtual integration and ETL 's data entry into the lake to achieve hybrid scenario support. 

 lIntegrated security and policy engine: supports enterprise-level security requirements such as fine-grained data permissions, policy compliance, and dynamic auditing . 

IV. Conclusion: NoETL+ Data Fabric is the full-stack solution for AI data

In today's AI wave, if enterprises only stay at the NoETL stage, it is equivalent to " opening the door to data but not lighting the light of data " . What really makes data an AI asset is not " connection " but " understanding and governance " . Only by incorporating NoETL capabilities into a larger data weaving architecture , with semantic drive, intelligent collaboration, and global control as the core concepts, can AI really enter enterprises, land in industries, and release value.