No more waiting : 90% Faster Queries
An Australian enterprise was struggling with critical report delays and long query execution times that hindered decision-making and stalled business initiatives. Skillfield redesigned their internal analytics engine, replacing a sluggish Python-based approach with an optimised Scala solution.
The result: faster data insights, reduced system load, and the ability to scale analytics across massive datasets – empowering teams to act on information when it matters most.
The Problem
Enterprise data teams were facing mounting delays in generating critical reports due to slow-running queries over large datasets.
Built in Python using PySpark, the system struggled to scale, resulting in unresponsive dashboards, queries stuck in a backlog, and cancelled or delayed projects.
Poor maintainability and inefficient partition scanning further compounded the issue, leaving teams without timely access to key insights.
The Solution
Skillfield modernised the platform by rebuilding the query engine in Scala, taking advantage of Spark’s lower-level optimisations and a more maintainable object-oriented structure.
The refactor introduced a smarter partition scanning mechanism, drastically improving performance.
To manage over a terabyte of daily data, an incremental processing job was developed, splitting workloads into smaller segments and enabling long-range queries without overwhelming system memory.
The Outcome
The transition to Scala delivered immediate benefits, with query execution times dropping by up to 90%. Critical use cases were unblocked, and reporting became consistently timely.
The improved architecture simplified code maintenance and accelerated feature delivery.
Most importantly, data-driven decisions could now be made without delay, as business teams gained access to analytics.
