A Global Enterprise Built 60+ ETL and ELT Workflows to Process 25M+ Records Daily Across Its Modern Data Environment

The Challenge

The organisation relied on data from multiple systems to support business intelligence, financial reporting, operational analysis, and strategic decision-making.

However, source systems stored information in different structures and formats. Before datasets could be analysed together, teams needed to extract, standardise, transform, validate, and load them into appropriate analytical environments.

Existing ETL processes had been built over several years using different development approaches. Some transformations occurred before loading, while newer cloud workloads were better suited to an ELT model where raw data could be loaded first and transformed using scalable cloud processing.

Growing volumes created further pressure. Workflows that had performed adequately with smaller datasets began taking longer to complete, affecting the availability of refreshed information for downstream reporting.

Schema changes, missing records, duplicates, inconsistent formats, and transformation failures also created data quality risks. Without effective monitoring, teams could spend considerable time tracing problems through multiple processing stages.

The client needed a flexible data engineering model capable of applying ETL or ELT based on each workload rather than forcing every data requirement through the same processing architecture.

What Did KGS Do

KGS began by assessing the client's source systems, existing data workflows, transformation logic, target platforms, processing volumes, refresh requirements, and downstream analytical dependencies.

Our data engineering specialists developed more than 60 ETL and ELT workflows integrating information from 30+ enterprise, cloud, database, API, and legacy sources.

For workloads requiring transformation before delivery, KGS implemented ETL pipelines that extracted source data, applied defined business and quality rules, and loaded prepared datasets into target environments. For cloud-scale analytical workloads, ELT patterns allowed data to be loaded first and transformed using the processing capabilities of modern data platforms.

Reusable transformation components and standard development practices reduced duplication across workflows. Validation controls identified missing information, duplicate records, unexpected formats, schema changes, and transformation issues before affected data progressed downstream.

Monitoring, logging, and exception workflows provided clearer visibility into processing status and failures. This allowed data teams to identify where issues occurred and focus their attention on exceptions rather than manually checking routine pipeline execution.