IBM watsonx.data

Why IBM watsonx.data?

AI needs data, but data is everywhere. Data silos prevent AI projects. Data warehouse too expensive for all data. Data lakes lack governance. AI models need trusted data.

IBM watsonx.data is a data lakehouse for AI workloads. Open formats - no vendor lock-in. Query federation - access data in place. 50% cost reduction - optimized storage. AI-ready - trusted data for watsonx.ai.

How does it work?

Lakehouse Architecture

Best of both:

• Lake flexibility
• Warehouse performance
• Open formats
• ACID transactions
• Single platform

Query Federation

Data in place:

• No data movement
• Cross-source queries
• Unified access
• Real-time
• Reduced complexity

Open Standards

No lock-in:

• Apache Iceberg
• Parquet format
• Presto engine
• Standard SQL
• Portable data

Key Features

Data Access

• Multi-source federation
• Cloud + on-prem
• Real-time queries
• Data virtualization
• Unified catalog

Cost Optimization

• Tiered storage
• Compression
• Workload optimization
• Resource management
• 50% cost reduction

AI Integration

• watsonx.ai native
• Feature store
• Data preparation
• Quality monitoring
• Lineage tracking

Supported Sources

Category	Examples
Cloud	AWS S3, Azure, GCP
Databases	Db2, PostgreSQL, Oracle
Data Lakes	Hadoop, Delta Lake
Warehouses	Snowflake, Redshift

Use Cases

AI Data Preparation:

• Feature engineering
• Training data
• Data quality
• Governance

Analytics:

• Cross-source analysis
• Business intelligence
• Data exploration
• Reporting

Data Consolidation:

• Eliminate silos
• Unified access
• Single source of truth
• Governance

Specifications

Architecture	Data Lakehouse
Formats	Iceberg, Parquet
Query	Presto, Spark
Integration	watsonx.ai native

Who is it for?

• Data teams preparing data for AI
• Organizations with data silos
• Enterprises optimizing storage costs
• AI projects needing trusted data

Benefits

For Data Teams: Unified access, open formats, no data movement

For AI Projects: Trusted data, feature store, lineage

For Finance: 50% cost reduction, optimized storage

FAQ

What is a data lakehouse? Lake + warehouse. Flexibility + performance + governance.

Why do open formats matter? No vendor lock-in. Portable data. Ecosystem compatibility.

How does query federation work? Query multiple sources without moving data.

Where does the 50% cost reduction come from? Tiered storage, compression, workload optimization.

Does it require watsonx.ai? No. Standalone or integrated.

What sources does it support? Cloud storage, databases, existing lakes/warehouses.

How does it integrate with watsonx.ai? Native integration. Feature store, data prep, quality.

Does it support Spark? Yes. Presto and Spark engines.

What is the deployment model? IBM Cloud, AWS, on-prem options.

How does support work? IBM support. nFlo offers data architecture and implementation.