Case Study: Malwarebytes achieves accelerated threat detection and reduced cloud costs with Qubole

Predicting, Detecting, And Eliminating Online Threats: Malwarebytes

Malwarebytes, a cybersecurity company that uses ML and AI to detect and remediate malware, was struggling to process billions of daily telemetry records with an on‑premises pipeline that was slow, costly, and poorly supported—ETL jobs could take days, queries were sluggish, and rising data volumes created frequent capacity and cost issues.

Malwarebytes modernized its platform with Qubole alongside Kafka for ingestion and an AWS S3 data lake, decoupling compute from storage and using autoscaling and low‑cost Spot instances to spin clusters up and down in minutes. The change accelerated ETL and analytics (aggregating 20–48 TB raw/day into 2–3 TB actionable data), cut costs and admin time dramatically, improved GDPR compliance, and delivered rapid ROI and more powerful insights for threat prediction and remediation.

Malwarebytes

Manju Vasishta

Director of Data Science and Engineering