IBM watsonx.data Premium: The Next-Generation Data Lakehouse for Gen AI

Contibutors: Anusha Garlapati, Phani Chodavarapu

Introduction

Generative AI (GenAI) is rapidly reshaping the enterprise landscape. Businesses are eager to unlock richer analytics, smarter automation, and transformative user experiences powered by advanced AI. At the centre of this transformation is enterprise data - a resource that must be accessible, secure, and AI-ready.  IBM watsonx.data Premium stands out as a next-generation data Lakehouse platform designed for this precise moment: unifying structured and unstructured data, integrating best-in-class governance, and enabling high-value GenAI applications at scale.

The Problem with Conventional RAG Systems: Why Conventional RAG Falls Short for Enterprise GenAI?

Retrieval-Augmented Generation (RAG) is the backbone of many early enterprise GenAI deployments - powering everything from semantic search to internal document Q&A. However, most conventional RAG architectures rely solely on basic vector embeddings, which have critical limitations in complex enterprise environments:

•         Weak governance and access control: Many RAG setups struggle to enforce fine-grained security and business-specific policies, leaving sensitive data at risk.
•        Unoptimized for unstructured data: Traditional platforms excel at structured data (like SQL tables), but fall short in extracting value from the vast sea of documents, emails, and reports that dominate most enterprise data estates.

The market’s leading platforms, such as Databricks’ Lakehouse AI, are strong for structured data - and are now adding vector search. But without deep, integrated governance and smart data understanding, they can't fully support nuanced, high-stakes enterprise GenAI.

IBM’s Advantage: watsonx.data Premium: A Unified Platform for AI-Ready Data

IBM watsonx.data Premium is a hybrid, generative AI (gen AI)-enabled data Lakehouse platform designed for complex, distributed enterprise data environments.

•  Lakehouse storage (flexible, scalable, object-store-based)
• Data fabric (federated access, metadata unification, lineage)
• AI intelligence (semantic enrichment, LLM metadata generation)
• Multi-engine querying (Spark, Presto, Milvus, Db2, Netezza)

Key Benefits

•        AI-Ready Architecture: Native support for structured, semi-structured, and unstructured data, making it ideal for Gen AI and advanced AI workloads.
•        Hybrid & Multi-Cloud Deployments: Deploy on-premises, on IBM Cloud, or in your own cloud (BYOC).
•        Open Standards: Prevents vendor lock-in through support for open formats like Apache Iceberg and connectors for 25+ external sources.
•       Multiple Optimized Engines: Use Spark, Presto (Java, C++), Milvus, and others - each suited for diverse data processing and analytics needs.
•        Seamless Data Governance: Unified metadata, access controls, and automated lineage - ensuring compliance and trusted data use for Gen AI.

What Sets watsonx.data Premium Apart?

1. Multi-Engine and Multi-Modal Architecture

• Optimized Processing: Select the right engine for every need—high-speed batch (Spark), fast interactive queries (Presto), cutting-edge vector search and retrieval (Milvus), or powerful warehousing (Db2Wh).
• Seamless AI/GenAI Integration: Native connectors to LLMs and RAG pipelines make it easy to build, deploy, and govern AI-powered applications—no custom glue code required.

2. End-to-End Data Fabric

From data ingestion, enrichment, and unstructured/structured data federation, to robust governance and AI-powered insight delivery, the Premium edition includes:

• Data Integration (batch, streaming, replication)
• Unified governance (Apache Ranger, ACLs)
• Embedded vector database (Milvus) for scalable RAG and Gen AI.

3. AI-Driven Metadata and Semantic Enrichment

• Automatically generate descriptive metadata, business context, and improve precision of term assignments using Large Language Models (LLMs). This accelerates data curation and links meaning across your data assets for smarter, faster AI.

4. Advanced Security and Compliance

Unlike conventional vector stores, watsonx.data Premium builds unified governance and security into every layer:

• Attribute-Based Access Control: Fine-grained, policy-driven data access down to the record, document, or entity - ensuring compliance and protecting sensitive data.
• Unified Metadata and Auditing: Full lineage tracking, business glossary integration, and automated semantic tagging - making it easy to discover, trust, and manage your data assets.

watsonx BI: AI-Powered Business Insights

watsonx BI serves as an AI-powered business insights agent that complements the data Lakehouse architecture with advanced analytics capabilities:

Core Pillars of watsonx BI:

This integration ensures that the advanced data processing capabilities of watsonx.data Premium translate into actionable business intelligence that drives decision-making across the enterprise.

Unstructured Data Governance and Lineage: AI-Powered Metadata and Semantic Enrichment

Unstructured Data Governance - Lineage provides critical visibility into how unstructured assets flow through AI development pipelines:

What it Provides:

• Comprehensive lineage tracking across import and enrichment pipelines from initial ingestion to governance.
• Rich runtime metadata including ratios of ingested/curated unstructured documents and pipeline performance metrics.
• Historical tracking to support trend analysis, change tracking, and audit readiness.

Why It Matters for AI Applications:

• For LLMs and RAG systems, understanding where unstructured data came from, how it was modified, and how it's being used is critical. Without data lineage, organizations risk exposing models to outdated, non-compliant, or sensitive content.

Key Capabilities:

• Traceability: Track every transformation from Amazon S3 storage through import, enrichment, and embedding processes
• Transparency: Detailed visibility into which document sources are used and how they're transformed
• Control: Enable confident and responsible use of governed content in AI and LLM workflows
• Compliance: Ensure regulatory compliance by making unstructured data transformations auditable

The lineage system captures the complete flow: Base document set → Unstructured data import → UD integration flow → Embeddings collection → Document processing → Entity extraction → Final document library, all tracked through Presto and integrated with watsonx.data's unified catalog.

This advanced lineage capability ensures trust, accountability, and regulatory compliance while enabling organizations to leverage unstructured data confidently in their AI initiatives.

AI-Powered Metadata and Semantic Enrichment: Watsonx.data + IKC Embedded LLM-based Metadata Enrichment

What We're Introducing

• New - Automatically generate meaningful column names and descriptions with context: The system can intelligently create business-relevant metadata that makes sense to users, not just technical column names
• Enhanced - Assign terms based on semantic meaning and context: Rather than simple pattern matching, the system understands the actual business meaning of data elements
• Early results - Internal testing showed double the number of correct column mappings (reduction in false positives), and improved precision of term assignments with LLMs

Key Interface Features:

• AI-generated contextual column name: Automatically suggests meaningful names like "Account holders" instead of technical identifiers
• AI-generated description: Provides business context such as "Table containing data of debit account opened from March 2023 to today"
• Confidence scores for AI generated content: Shows reliability indicators (like "94% confidence") so users can trust the automated suggestions
• Source attribution: Clear indication of "DB2 Bank > BANK3" showing data lineage
• Asset details panel: Comprehensive view of enrichment details, governance information, and metadata

Why This Matters:

Accelerate data curation through increased accuracy and precision of auto term assignments using AI and LLMs. This capability:

• Reduces manual effort in cataloguing and organizing enterprise data
• Improves data discoverability across the organization
• Unifies metadata business context between Knowledge Catalog and watsonx.data
• Enables faster time-to-value for AI and analytics projects

Unlocking High-Impact Use Cases: From Operational Analytics to Gen AI

With these capabilities, watsonx.data Premium powers a new generation of GenAI for the enterprise:

• Semantic Search: Let employees find precise answers - across contracts, emails, and reports - in their own language, with results tailored to their role and permissions.
• Context-Aware Document Q&A: Accelerate legal review, regulatory compliance, and knowledge management by enriching RAG with business meaning and document structure.
• Real-Time, Trustworthy Insights: Proactively surface contextual recommendations and risks - in dashboards, apps, and chatbots business teams already use.
• Data Engineering: Ingest, transform, and store all types of data for BI, ML, and analytics.
• Business Intelligence: Democratize data access with rich, governed metadata and cataloguing
• Data Governance: Consistently apply access controls, masking, and classification policies - even on unstructured data.

Feature Comparison: watsonx.data vs. watsonx.data Premium

Differentiation: watsonx.data Premium vs. Competitors

Real-World Example: Building RAG Applications at Scale

By Q4 2024, Milvus as a service was integrated, enabling watsonx.data Premium users to easily create Retrieval-Augmented Generation (RAG) applications - vital for modern Gen AI workflows. As unstructured data from sources like invoices, contracts, and customer communications is ingested, watsonx.data Premium leverages smart document understanding, embeds text, and delivers improved search and analytics. This results in richer, more accurate AI models and applications.

Why Choose watsonx.data Premium?

• 40% more accurate AI: Compared to conventional RAG approaches.
• Enterprise-Grade AI Platform: Tailored for hybrid environments- enabling simplified AI, analytics, and governance.
• Accelerate AI Readiness: Automated data curation, semantic enrichment, and scalable vector search deliver Gen AI value faster.
• Reduce Cost & Complexity: Optimize compute, storage, and security while simplifying multi-cloud data access.
• Broad Persona Support: Built for data engineers, scientists, stewards, and AI app developers - each benefits from a unified, governed, high-performance platform.
• Trusted by Leading Enterprises: Built on IBM’s open, developer-first principles with ongoing enhancements informed by real customer needs.

Unlocking the Power of IBM watsonx.data Premium Capabilities: SUMMARY

Benchmarking engine performance Comparison results for IBM watsonx.data vs. Premium

This slide presents a comparative analysis between watsonx.data and watsonx.data Premium, using a standardized workload on identical datasets and configurations.

In the above analysis, 

A performance benchmark was conducted comparing IBM watsonx.data and watsonx.data Premium under identical configurations. Both versions were tested with three input files, using 33 total cores and 97 GB of memory for the driver and executors. In this setup, watsonx.data completed the workload in 1 minute 31 seconds, while watsonx.data Premium achieved the same task in just 34 seconds, demonstrating significantly improved processing speed. A secondary comparison was also carried out to evaluate the performance of Spark and Presto ingestion processes when handling four CSV files. The transformation stage took 1 minute 46 seconds, while ingestion required 1 minute 55 seconds for watsonx.data and 1 minute 47 seconds for watsonx.data Premium. These results show that the Premium variant delivers consistently faster ingestion and transformation times compared to the standard version.

This evidences that IBM watsonx.data Premium provides superior performance for large-scale data ingestion and transformation compared to the standard offering, under equivalent hardware and workload conditions. The technical details and UI screenshots support the benchmark process, and the tabular analysis helps quantify the acceleration and scalability improvements.

Authors:

• Anusha.Garlapati@ibm.com
• Phani.Chodavarapu@ibm.com

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