Thesis: Performance evaluation of time-series and traditional databases for high frequency workloads

Growing use of IoT devices is generating increasing volumes of time-stamped data, creating new demands on database performance and storage. This thesis will assess when specialized time-series databases are preferable to traditional relational databases for large-scale, high-frequency workloads.

The assignment

• Compare time-series and relational databases across ingestion rates, query latency, storage efficiency and resource usage.
• Evaluate how data volumes and access patterns affect performance, and identify workloads where time-series databases gain an advantage.
• Examine trade-offs between scalability, performance, operational complexity and resource consumption.
• Determine which database technologies suit different time-series use cases.
• Investigate table designs and indexing strategies for large time-series datasets in both database categories.
• Explore whether a simple extension optimized for time-series data can be developed.

Who we are looking for

• Bachelor of Science background in Computer Science or Engineering.
• Expected graduation in June 2027.
• Fluent written and spoken Swedish and English.

What we offer

• An opportunity to conduct a thesis at Knightec Group, which brings together Semcon and Knightec and works with product and digital service development.

Practical details

Based at the Sundsvall office. Start in January or March 2027. Apply through the company's careers page as soon as possible, no later than 30 November 2026. Questions can be sent to johanna.edstrom@knightec.se.