Data Management Glossary
AI Data Pipelines
What are AI data pipelines?
AI data pipelines are a process and supporting technology to curate data from multiple sources, prepare the data for proper ingestion, and then mobilize the data to the destination. Data pipelines for unstructured data have special considerations since unstructured data is large, diverse, and difficult to search, organize and move.
What are AI Data Pipeline Requirements for Unstructured Data?
IT organizations need streamlined, automated ways to find and tag data for classification and search and deliver the right datasets to the right tools. AI data pipelines also must include methods to ensure data security and governance. A global file index that can look across all storage facilitates search and curation of unstructured data for AI, including metadata enrichment.
AI data pipelines can also detect sensitive data and move it into secure storage where it cannot be discovered nor ingested into an AI tool. Most organizations have PII (Personal Identifying Information), IP and other sensitive data inadvertently stored in places where it should not live.
Data pipelines can also be configured to move data based on its profile, age, query, or tag into secondary storage—such as cloud object storage where it is significantly cheaper to host and where researchers and data scientists can access it natively for use in cloud-based AI services.
Since unstructured data often lives across storage silos in the enterprise, it’s important to have a plan and a process to manage this data for storage efficiencies, AI, data protection and compliance. Data pipelines aided by a global file index and metadata tagging can help with all these needs.
You’ll need various capabilities, many of which are part of an unstructured data management solution. For example, metadata tagging and enrichment – which can be augmented using AI tools – allows data owners to add context and structure to unstructured data so that it can be easily discovered and segmented.
Read the interview on Blocks & Files: AI Data Pipelines Could Use a Hand from Our Features, Says Komprise
Komprise Smart Data Workflows for AI Data Pipelines
Komprise Smart Data Workflow Manager, included in the Komprise Intelligent Data Management Platform, is a simple UI that allows users without specialized experience to set up, schedule and monitor workflows, including connecting via API to third-party AI services.
Duquesne University used Komprise Smart Data Workflow Manager to create an AI data pipeline for rapid image search across millions of files in its digital archives. The workflow sent images to AWS Rekognition which analyzed file contents to find specific images needed for marketing campaigns, which Komprise then tagged for future search. The process reduced a 300-plus manual hour effort to less than two hours and demonstrated a repeatable use case for other departments.
AI Data Pipeline FAQs
What are AI data pipelines and why does unstructured data make them difficult to build?
An AI data pipeline is an automated process that moves data from source systems through preparation, enrichment, and governance steps before delivering it to an AI model, RAG workflow, or analytics platform. Pipelines handle ingestion, transformation, filtering, and routing so that AI systems receive clean, relevant, and authorized data at the right time.
Unstructured data makes AI pipelines significantly more difficult to build and maintain than structured data pipelines. Unstructured file and object data has no consistent schema, is scattered across multi-vendor NAS and cloud storage environments, and typically lacks the rich metadata that AI systems need to understand what a file contains and whether it is relevant to a given task. Studies show that 80% of the time in modern AI and analytics projects is spent finding the right data and extracting it from distributed storage environments. Komprise addresses this with a combination of the Global Metadatabase, custom tagging, Deep Analytics, and Smart Data Workflows that automate the entire process of finding, curating, and delivering the right unstructured data to AI pipelines at petabyte scale.
How does Komprise help enterprises build and automate AI data pipelines for unstructured data?
Komprise provides four connected capabilities that together automate AI data pipeline preparation for unstructured data:
1. Global Metadatabase. Automatically indexes all file and object data across on-premises and cloud storage, capturing standard system metadata and custom tags as first-class searchable attributes. Tags perform at the same query speed as standard metadata across billions of files, making the entire unstructured data estate searchable by any criteria relevant to an AI use case.
2. Policy-Based Data Mobility and Lifecycle Management. Komprise tiers, migrates, copies, or confines data based on policies driven by data attributes such as last accessed time or based on the results of a Deep Analytics query, which may include attributes such as file type, age, or owner. Tiering uses Transparent Move Technology, which always stores data in its native format on open standards-based object storage with no rehydration penalty, directly differentiating Komprise from storage-based tiering that can trap data in proprietary formats. Migration uses the Komprise engine proven at up to 27x faster than standard transfer methods. Copy and confine workflows run automatically on any schedule based on policy criteria.
3. Deep Analytics and Deep Analytics Actions. Deep Analytics searches the Global Metadatabase using any combination of standard metadata and custom tags to find precise datasets across the entire storage estate. Deep Analytics Actions means a saved query becomes the direct input to a data management policy for tiering, copying, or confining data automatically, closing the loop between discovery and action without manual steps.
4. Smart Data Workflows. A point-and-click UI for building automated data ingestion and curation workflows. Current capabilities include sensitive data detection covering PII and regex-based classification, AI ingestion workflows that deliver curated datasets directly to AI platforms and agents, and KAPPA data services for custom metadata extraction and enrichment from file content.
How does data governance work within an AI data pipeline built on Komprise?
AI data pipelines without governance can expose organizations to significant risk. Models trained on sensitive, restricted, or unauthorized data can produce compliance violations, security incidents, or legal liability. Komprise addresses this by building governance directly into the pipeline rather than treating it as an afterthought.
Deep Analytics queries the Global Metadatabase using metadata and custom tags to identify sensitive data including PII and IP before it enters a pipeline. Smart Data Workflows can be configured to automatically exclude restricted data from AI ingestion, route sensitive files through the Komprise sensitive data detection processor, or apply classification tags that flag data as authorized or unauthorized for specific AI uses. Because tags are first-class metadata in Komprise and are queried at the same performance as standard metadata across billions of files, governance policies scale to the full size of the enterprise data estate without performance trade-offs. The result is an AI data pipeline where what goes in is precisely controlled, auditable, and compliant with regulatory requirements.
How does storage tiering connect to AI data pipeline performance and cost?
The storage tier where AI pipeline source data lives directly affects pipeline latency, throughput, and cost. Data stored on high-performance primary NAS is fast to retrieve but expensive to maintain at scale. Data on cold cloud archive tiers is cheaper to store but can be slow and costly to retrieve for frequent pipeline use. Without an intelligent data management layer, enterprises end up either over-spending on primary storage to keep AI data fast, or accepting pipeline latency from retrieving data off archive tiers.
Komprise Intelligent Tiering manages this automatically by keeping actively queried AI pipeline data on appropriate storage tiers and moving data that is no longer being retrieved to lower-cost alternatives. Because Komprise stores all tiered data in native format with no rehydration required, Smart Data Workflows can access tiered data directly as a pipeline source without any additional processing layer. This means pipeline data placement is continuously optimized for both cost and performance, without requiring manual storage management or pipeline reconfiguration as access patterns change.
How does Komprise support AI data pipelines for agentic AI and multimodal use cases?
Agentic AI systems need to autonomously discover, retrieve, and act on enterprise data across distributed storage environments to complete tasks without human intervention. This requires a metadata layer rich enough to make unstructured data findable by context rather than just by filename or path, and a data mobility layer that can deliver data to agents in real time in response to queries.
Komprise supports agentic AI workflows through the Global Metadatabase, which maintains a continuously updated, vendor-neutral index of all file and object data across hybrid storage environments. Agents can query this index using metadata and tag criteria to locate relevant data across any storage silo. Smart Data Workflows can then automatically copy or move the right data to the destination the agent needs, in native format, whether that destination is an S3 bucket, a vector database, a data lakehouse, or a direct AI model input. For multimodal use cases, Komprise handles documents, images, video, sensor data, and other file types without format conversion, feeding AI systems the full range of unstructured content they need to perform complex, context-aware tasks.
Learn more about Komprise Smart Data Workflow Manager
