Data Fabric in Action: How watsonx.data Virtualizes Data Across Hybrid Cloud

Introduction: Why Data Silos Are Slowing You Down

#

In today’s world, data is the new oil. Companies rely on it to make decisions, build products, and serve customers better. But here’s the problem: most organizations don’t have “one” dataset—they have hundreds, sometimes thousands, scattered across different systems.

Think about it:

• Customer profiles may live in PostgreSQL (an open-source database).
• Financial transactions might be stored in IBM DB2, an enterprise-grade system.
• IoT devices stream sensor readings into Amazon S3 buckets.
• Teams experiment with data science models in Hadoop, or even newer cloud warehouses.

Each of these systems is like a separate island of data. When data is stuck on islands, companies face:

• Slow reporting. Analysts wait days for a complete view.
• High costs. Data is copied multiple times across systems.
• Risk. Moving sensitive data around increases compliance and security concerns.

This challenge is known as data silos. Breaking those silos—without creating new problems—is where IBM watsonx.data Lakehouse shines.

What is a Data Fabric? (Explained Simply)

#

A data fabric is a fancy term, but the concept is simple. Imagine your company’s data is a bunch of books scattered across many libraries. Traditionally, to read them, you’d gather all those books into one big central library. That’s what data warehouses and lakes tried to do.

But what if you could stay in your chair and instantly read any book from any library—without moving it? That’s a data fabric.

In the data world, a fabric:

• Connects multiple systems (databases, cloud storage, apps).
• Lets you query data where it lives instead of copying it.
• Applies security, governance, and metadata consistently across sources.

This means a data analyst, business manager, or AI model can access PostgreSQL, DB2, and S3 data in one place, without waiting for IT to build pipelines.

watsonx.data: IBM’s Data Lakehouse with a Twist

#

At the heart of IBM’s data fabric is watsonx.data, often described as an IBM data lakehouse. But what does that mean?

Lakehouse in Simple Terms

#

Traditionally:

• A data warehouse = structured, organized, great for reporting, but rigid and expensive.
• A data lake = flexible, stores raw data, but often messy and slow to query.

A lakehouse combines both:

• The flexibility of a lake (store any kind of data, often in cheap open formats like Parquet).
• The performance of a warehouse (fast queries, indexing, optimization).

Why watsonx.data is Different

#

Unlike closed systems, watsonx.data is:

• Open. It supports Apache Iceberg lakehouse tables, Parquet, and open SQL engines.
• Hybrid. Deploy it on cloud, on-premises, or both.
• Federated. Run queries across PostgreSQL, DB2, S3, and more—without duplication.
• AI-Ready. Seamlessly connect to watsonx.ai to train and deploy AI models.

In short, watsonx.data is not just another place to store data—it’s the control plane for how you access, govern, and analyze it.

watsonx.data Tutorial: Query PostgreSQL + DB2 + S3 in One Shot

#

Let’s walk through an example.

The Challenge

#

A retail company wants a 360° view of customers:

• Profiles in PostgreSQL
• Transactions in DB2
• Web clickstream logs in S3

Traditionally, IT would spend weeks building ETL pipelines (Extract, Transform, Load) to copy this data into a warehouse. That means: high cost, long delays, and outdated reports.

Here is sequence diagram showing a BI query hitting watsonx.data, which consults governance, orchestrates Presto/Spark pushdown to PostgreSQL/DB2/S3, merges results, and returns governed output.

sequenceDiagram
  participant User as Analyst or BI
  participant WX as watsonx data
  participant ENG as Presto or Spark
  participant P as PostgreSQL
  participant DB2 as IBM DB2
  participant S3 as Amazon S3
  participant IKC as IBM Knowledge Catalog

  User->>WX: Submit SQL join across PostgreSQL DB2 S3
  WX->>IKC: Check policies and metadata
  IKC-->>WX: Return allowed access and rules
  WX->>ENG: Plan and optimize federated query
  ENG->>P: Push filters and aggregations
  ENG->>DB2: Push filters and aggregations
  ENG->>S3: Read parquet or iceberg partitions
  P-->>ENG: Partial results
  DB2-->>ENG: Partial results
  S3-->>ENG: Partitioned results
  ENG->>WX: Merge and finalize
  WX-->>User: Return governed dataset

The watsonx.data Way

#

With watsonx.data, you don’t move the data—you query it in place:

SELECT c.customer_id, c.name, t.txn_amount, s.page_view
FROM postgresql.customers c
JOIN db2.transactions t ON c.customer_id = t.customer_id
JOIN s3.clickstream s ON c.customer_id = s.customer_id
WHERE t.txn_date > CURRENT_DATE - 30;

✅ One query.
✅ Three systems.
✅ No replication.

The result: real-time insights across multiple systems—something impossible with traditional pipelines.

flowchart LR
  subgraph Sources["Data Sources"]
    P["PostgreSQL"]
    D["IBM DB2"]
    S3["Amazon S3"]
    M["MySQL or MongoDB"]
  end

  subgraph Fabric["Data Fabric and Lakehouse"]
    WX["watsonx.data
(Open, federated lakehouse)"]
    ICE["Apache Iceberg / Parquet
(Managed tables)"]
    WKC["IBM Knowledge Catalog
(Lineage, Policies, Glossary)"]
    PRE["Presto / Spark engines
(Query federation & pushdown)"]
  end

  subgraph ETL["Optional Ingestion"]
    DS["IBM DataStage
 (ETL)"]
    SS["IBM StreamSets
 (Realtime Ingestion)"]
  end

  subgraph Consumers["Consumers"]
    BI["Analysts and BI Tools"]
    DSci["Data Scientists using watsonx ai"]
    APP["Apps and APIs"]
  end

  %% Virtualized access
  P --> WX
  D --> WX
  S3 --> WX
  M --> WX

  %% Managed tables path
  WX --> ICE
  ICE --> WX

  %% Engines and governance
  WX --> PRE
  WKC --> WX
  WKC --> ICE

  %% Optional ingestion into managed tables
  P --> DS --> ICE
  S3 --> SS --> ICE
  D --> DS
  M --> SS

  %% Consumers
  BI --> WX
  DSci --> WX
  APP --> WX

Architecture showing PostgreSQL, DB2, S3, and other sources queried virtually by watsonx.data, governed by IBM Knowledge Catalog, optionally landing in Apache Iceberg/Parquet, with Presto/Spark engines serving BI, data science (watsonx.ai), and apps

Breaking Down the Concepts (For Non-Technical Readers)

#

• Data Virtualization: Like using a universal remote that controls all your TVs, speakers, and lights. One interface, multiple systems.
• Federated Queries: Instead of asking three people separately and combining answers, you ask one person who gathers all answers instantly.
• Presto Spark: An open-source query engine that runs queries in parallel across systems—like splitting a big job among 100 workers instead of 1.
• Apache Iceberg Lakehouse: A modern way of organizing data in tables. Think of it as adding an “index” and “chapters” to your messy data lake, making it easy to search and update.

Business Value: Why This Matters Beyond IT

#

The benefits of watsonx.data go far beyond the IT department:

• Speed. Analysts get reports in minutes, not days.
• Cost. Stop paying for duplicate storage and endless pipelines.
• AI-ready. Data scientists can train models directly on fresh data.
• Compliance. Sensitive data stays in PostgreSQL or DB2, governed at source.

Companies adopting IBM’s lakehouse + fabric often cut costs by 30–50% while accelerating decision-making.

Industry Use Cases

#

Telecom: Open Source + CRM

#

Customer records sit in PostgreSQL, billing data in DB2, and usage logs in S3. With watsonx.data, a telecom can analyze churn risk in near real time without migration.

Government: Smart Cities

#

City planners combine PostgreSQL open data (population stats) with IoT traffic feeds in S3 to optimize transport systems.

Finance: Compliance

#

Banks run fraud detection queries across DB2 transactions, PostgreSQL profiles, and Iceberg audit logs in S3. No sensitive data leaves its system.

Best Practices: Mixing Open Source and Enterprise Data

#

1. Start small. Connect 2–3 systems first (e.g., PostgreSQL + DB2 + S3).
2. Use ETL wisely. Use DataStage ETL for heavy data transformations, but lean on watsonx.data for analytics.
3. Standardize formats. Adopt Apache Iceberg lakehouse for scalable, reliable storage.
4. Govern everything. Use IBM Knowledge Catalog to ensure metadata, policies, and lineage are clear.
5. Optimize queries. Enable pushdown so filtering happens in PostgreSQL or DB2 directly, reducing load.

watsonx.data + Presto Spark: Why It Scales

#

At the core of watsonx.data’s power is Presto Spark.

Here’s why it matters:

• Parallelization. Breaks a large query into smaller tasks, runs them at the same time.
• Pushdown. Pushes filtering and joins to the database itself—less data moves.
• Caching. Frequently accessed results are stored temporarily for faster response.

This means watsonx.data can run federated queries across petabytes of data from multiple open-source and enterprise systems without choking your network.

Conclusion: watsonx.data as the Bridge Between Open Source and Enterprise

#

Most organizations live in a hybrid world: PostgreSQL, MySQL, DB2, S3, Hadoop, and more. The problem isn’t lack of data—it’s lack of connected, trusted access.

watsonx.data solves this by:

• Virtualizing data across systems.
• Running federated queries at scale with Presto Spark.
• Supporting modern open formats like Apache Iceberg lakehouse.
• Enabling AI pipelines that bridge open-source agility with enterprise compliance.

👉 Whether you’re an engineer, a data scientist, or a business leader, watsonx.data offers a smarter, faster, safer way to put your data to work.

If you’re new, start small: connect PostgreSQL and DB2 in watsonx.data, run a federated query, and see how fast insights flow. From there, scale to your full hybrid cloud.