Driving High-Performance Time-Series Analytics
We’re enabling organisations to process vast amounts of real-time and historical data with exceptional speed and efficiency, solving latency issues and delivering immediate insights for better decision-making. Continue reading ↓
Did You Know?
We optimised a partner’s PySpark benchmark, achieving a 10x performance improvement in time-series analytics while reducing total cost of ownership (TCO) - empowering them to showcase significant efficiency gains to their customers.
This story represents Caspian One’s ability to solve complex performance challenges in real-time data analytics, enabling organisations to maximise efficiency and scale with confidence. By integrating deep expertise in PySpark and time-series analytics, we refined benchmarking frameworks, optimised system performance, and provided clear, repeatable insights that strengthened our partner’s market positioning.
Beyond technical gains, our structured approach empowered them to demonstrate measurable value to customers, drive adoption, and establish their platform as a leader in high-performance data processing.
The Challenge
An organisation specialising in real-time data analytics needed to optimise their PySpark benchmarking framework for a key partner integration. Their platform, known for solving latency challenges and enabling real-time and historical data analysis, needed to demonstrate measurable efficiency gains for customers using time-series analytics at scale.
However, they faced several challenges:
Their internal team lacked the bandwidth to execute the required optimisations
PySpark’s default performance wasn’t sufficient, and integration with their Python-based analytics library needed significant tuning
They needed clear, repeatable benchmarking results to quantify the speed and cost advantages of their platform
To meet these demands, they needed an expert partner to refine their benchmarking, integration, and performance tracking, ensuring they could showcase real-world efficiency improvements to their clients.
Our Approach
Caspian One provided specialist expertise in PySpark and time-series analytics, embedding a highly skilled data engineer into the project to lead the optimisation efforts.
Our first priority was to integrate their Python-based analytics library with the Spark DataFrame API, ensuring the system could scale efficiently across multiple executors while minimising memory consumption. By eliminating inefficient RDD usage, we improved the efficiency of large-scale time-series data processing.
Next, we conducted rigorous benchmarking, focusing on critical time-series analytics functions such as asof-joins and time-weighted moving averages. We systematically compared performance across multiple configurations, ensuring the optimised framework delivered clear, repeatable performance gains.
Beyond technical optimisation, we developed a comprehensive suite of documentation and interactive notebooks, ensuring that internal teams and external clients could easily understand and replicate the efficiency improvements. With clear reporting and structured benchmarking, the organisation was fully equipped to demonstrate the power of their platform to customers and stakeholders.
The Outcome
The results were transformative. Our optimisations achieved a 10x performance improvement, dramatically reducing query times and memory usage while also lowering total cost of ownership. The newly optimised PySpark integration became a key differentiator, allowing the organisation to present a clear, data-backed value proposition to their partner’s customers.
With this success, they were able to:
Expand their customer reach, introducing their platform to a new market segment
Showcase tangible efficiency gains, making adoption easier for prospective client
Position their solution as the leading choice for high-performance time-series data processing
By the end of the engagement, the organisation had a fully optimised benchmarking framework and a repeatable model for demonstrating performance and cost savings - ensuring that their analytics solution remained ahead of the competition.
What This Meant for the Client:
For this organisation, the project wasn’t just about technical optimisation - it was a market-shaping success.
With clear performance benchmarks, structured documentation, and repeatable testing frameworks, they could now confidently position their real-time analytics technology as the best choice for high-speed, cost-effective data processing.
More importantly, they could directly demonstrate ROI to customers, using real-world efficiency gains to drive adoption, revenue growth, and long-term platform success.
Key Details & Expertise
Keywords: PySpark, Python Analytics Library, Spark DataFrame API, Time-Series Analytics, Benchmarking, 10x Performance Improvement, Data Processing Framework, TCO (Total Cost of Ownership), Performance Optimisation, Data Lakehouse Integration
Primary Areas of Expertise: Data & Analytics
Secondary Areas: PySpark, Time-Series Analytics, Real-Time Analytics, Performance Monitoring, SQL/Big Data
Resource Roles: Data Engineer, Data Scientist, Quant Developer
Looking for Similar Expertise?
Caspian One specialises in high-performance data processing solutions, helping organisations optimise analytics, reduce costs, and accelerate performance. If you’re looking to transform your data framework, get in touch to see how we can support your project.
