Our client required a comprehensive overhaul of
their existing analytics pipeline to improve data quality, streamline
processing, and extract more meaningful insights from their vast datasets.
The challenge encompassed identifying and
resolving edge cases that were causing discrepancies in the data flow.
Additionally, there was a need to revamp data modelling, warehousing, and
processing techniques to align with the evolving business requirements.
The project began with a meticulous pipeline
refactoring process, addressing edge cases and debugging issues that had
previously hindered smooth data processing. This optimization greatly enhanced
the pipeline's reliability and ensured consistent data flow.
Next, data modelling was meticulously
revisited, resulting in the creation of more accurate and insightful data
representations. The revamped models better captured the underlying business
logic, enabling more precise analysis.
Custom string data processing solutions were
developed to convert unstructured textual data into structured information.
RESULTS & ADVANTAGES
The solution led to a marked improvement in
data quality, accuracy, and processing efficiency. The refined processes have
paved the way for a more agile and data-driven organizational environment.
SQL, Python, R, bash; Data modelling &
warehousing, pipeline optimization.