Founded in 2019, Speedata's APU currently targets Apache Spark workloads, with plans to support all major data analytics platforms. The startup aims to make APUs the standard processor for data analytics, similar to how GPUs became the default for AI training. Speedata has several large companies testing its APU, with an official product launch set for the Databricks’ Data & AI Summit. The company claims a significant speed improvement in processing workloads, such as completing a pharmaceutical workload in 19 minutes compared to 90 hours with non-specialized units. Speedata has finalized the design and manufacturing of its first APU and is ready to scale its market operations.
Key takeaways:
- Speedata, a Tel Aviv-based startup, has raised a $44M Series B funding round, bringing its total capital raised to $114M, to develop an analytics processing unit (APU) for big data and AI workloads.
- The APU is designed to address specific bottlenecks in data analytics, offering a purpose-built solution that can replace racks of servers and deliver better performance compared to general-purpose processors or GPUs.
- Speedata's APU targets Apache Spark workloads and aims to become the standard processor for data analytics across all major platforms, with plans to showcase the product at the Databricks’ Data & AI Summit.
- The startup has achieved significant milestones, including finalizing the design and manufacturing of its first APU, and claims a 280x speed improvement in a specific pharmaceutical workload compared to non-specialized processing units.