Differentiating Data Engineering and AI: How Inefficient Data Handling Impairs Artificial Intelligence Performance
In the rapidly evolving world of artificial intelligence (AI), traditional data storage solutions are being challenged to keep pace with the demands of agentic AI systems. Companies such as DataDirect Networks, IBM Storage Scale, Weka, and other storage vendors are positioning themselves as AI-first and AI-friendly, recognising the need for a shift towards AI-native infrastructure.
Many organisations still rely on traditional extract, transform, and load (ETL) data pipelines, separate vector databases, and batch-based access controls. However, as enterprises begin to re-evaluate how and where inference happens, there is a growing emphasis on AI-native infrastructure that can support real-time, permissioned data access.
Agentic AI systems necessitate real-time data to inform autonomous decisions, often requiring streaming data and low-latency processing frameworks. However, these systems often access highly sensitive information such as financial records, health data, or proprietary insights, making data privacy and regulatory compliance paramount.
Ensuring only authorized agents have access to specific data segments demands permissioned data architectures—employing role-based access controls, encryption, and audit trails to prevent unauthorized disclosure and mitigate breach risks. Integration challenges arise from the need for agentic AI to work seamlessly with existing enterprise workflows and legacy systems.
Security concerns specific to agentic AI architectures include classical system-layer threats such as misconfigurations and software vulnerabilities, which can be exploited to bypass data access controls or disrupt services. Addressing these involves thorough security audits, patching, and supply-chain risk management.
Governance frameworks contribute to solutions by defining ethical guidelines, compliance policies, and roles for overseeing agentic AI deployment and operation. Human-in-the-loop approaches serve as additional safeguards, allowing humans to intervene in critical or high-risk decision points within the AI lifecycle to correct errors or prevent unauthorized actions.
In an effort to set itself apart, Vast aims to build a native vector capability and structured data layer with additional capabilities designed for policy-aware, real-time inference inside the storage layer. Vast Data's approach coalesces storage intelligence into a centrally managed space for deep learning computing infrastructures.
The intelligence quotient of AI is largely determined by the work done at the backend. Adopting technologies such as vLLM, LMCache, and NvidiaGPU Direct Storage can reduce time-to-first-token delays to 1.5 seconds or less, helping to close the gap between enterprise data and 'agentic thought'.
Few organisations have the architecture in place to support the AI inference requirements of today and tomorrow. Pure Storage, NetApp, HPE, Dell Technologies, and cloud hyperscalers like Microsoft Azure, Google Cloud, and Amazon Web Services are also players in the AI data storage market.
In summary, enabling secure, real-time, permissioned data access in agentic AI for mission-critical applications demands a holistic approach combining advanced distributed data processing architectures, stringent security and privacy controls, integrative system design, and comprehensive governance and oversight mechanisms.
- Data engineering, data science, technology, and data-and-cloud-computing are crucial for organizations as they transition towards AI-native infrastructure, which supports real-time, permissioned data access for agentic AI systems.
- To address the demands of agentic AI, companies are adopting advanced technologies such as vLLM, LMCache, and NvidiaGPU Direct Storage to reduce time-to-first-token delays and bridge the gap between enterprise data and 'agentic thought', demonstrating the importance of data engineering and data science in the world of AI.
