Separating ETL and data quality is wasting your time—here’s how to fix it

If your engineering team uses one system to move enterprise data and a completely separate tool—or suite of custom scripts—to clean and profile it, your data pipelines are running far slower than they should.

The typical approach to data processing has required a multi-step sequence:

1. An ETL job extracts raw data from a source system and loads it into a temporary staging table.
2. A secondary data quality tool or script kicks off to parse addresses, validate fields, and flag errors.
3. Once the data is scrubbed, another process pushes the clean records into a cloud warehouse or reporting data mart.

Running these processes in separate environments creates immediate operational friction. Sequential batch runs double your pipeline execution time. Staging raw, unscrubbed data in cloud storage inflates compute and storage costs. Worst of all, when a pipeline fails, developers have to jump between different consoles and parse disconnected logs just to figure out which step broke.

With the release of the ibi^™ WebFOCUS^® Data & Analytics Platform version 9.3.8, we have eliminated this division by embedding native ibi Data Quality routines directly inside ibi Data Migrator ETL pipelines.

Here is how combining these workflows transforms the daily build experience for data engineering teams.

Single-pass execution: validating data while it moves

In many modern cloud environments, platforms load raw, unverified data straight into target data warehouses and rely on downstream SQL scripts to clean it up after the fact. While this gets data into the cloud quickly, it creates a dangerous window where unverified records sit in production storage—wasting cloud compute and risking exposure to generative AI models or executive dashboards.

Version 9.3.8 takes a different approach by running validation, address matching, and structural profiling routines directly inside active ibi Data Migrator execution loops.

Traditional Multi-Tool Process:

Source System → ETL Extraction → Temporary Staging Table → Separate Data Quality Run → Target Warehouse

WebFOCUS 9.3.8 Combined Workflow:

Source System → Data Migrator [ Inline Validation + Address Matching + Profiling ] → Target Warehouse

Data quality checks now happen in flight as records move through the flow. Incorrect address formats, missing key values, or unexpected field types are standardized or routed to exception queues instantly—guaranteeing that unverified data never lands in your target cloud data warehouse or AI ingestion layer.

What this means for teams using ibi Data Migrator

For organizations already using ibi Data Migrator, bringing data quality directly into the ETL canvas delivers three practical advantages:

• Eliminate intermediate staging tables: Because field validation and record scrubbing happen during transport, you no longer need to write raw data to temporary staging tables before cleaning it. This reduces storage footprint and speeds up execution windows.
• Tool unification: Engineering teams no longer need to shift between two disparate data tools, streamlining the entire DataOps process.
• Simplify pipeline troubleshooting: When all transformation and data quality logic lives in a single canvas, testing and debugging become straightforward. Developers can trace data flow, inspect validation rules, and isolate errors in one place.

A more efficient pipeline foundation

Building reliable data pipelines shouldn't require stitching together isolated tools for data movement and data cleansing.

By unifying data quality routines inside active ETL flows, WebFOCUS gives data engineers a single, efficient environment to move, clean, and deliver trusted enterprise data—without the extra steps.