Getting Started with Data Ingestion in watsonx.data¶
Overview¶
This lab introduces data ingestion into IBM watsonx.data in a finance domain context. You will connect structured financial data sources — such as transaction records, account ledgers, and market feeds — to the watsonx.data lakehouse, making them available for querying, analytics, and downstream AI workloads.
Benefits¶
- Unified Data Layer: Consolidates financial data from disparate sources into a single governed lakehouse platform.
- Open Formats: Stores data in open table formats (Apache Iceberg) that avoid vendor lock-in and support long-term auditability.
- Performance at Scale: Leverages Presto/Spark engines for fast, concurrent querying of large financial datasets.
- Governance Ready: Integrates with IBM Guardium and built-in metadata tagging to meet financial compliance requirements (SOX, GDPR, etc.).
- Cost Efficiency: Separates compute from storage, allowing teams to scale each independently.
What you will do¶
In this lab you will use the watsonx.data Data Manager to ingest a CSV file into a managed Iceberg table using two different methods:
- Local system — upload a CSV file directly from your machine.
- Cloud Object Storage (COS) — load a CSV file from a registered COS bucket.
Pre-requisites¶
- Access to an IBM watsonx.data web console.
- A sample CSV file containing financial records.
- A Cloud Object Storage bucket with the CSV file uploaded (for Part 2).
Step by step hands-on instructions¶
Proceed to the Lab Guide to begin the hands-on data ingestion exercise.