The first of its kind, Bodo is a SQL and Python compute engine that leverages high performance computing techniques to bring exceptional efficiency to ETL workloads. Bodo fits into your existing data stack so you can quickly get up and running—while keeping what works in place.
The Bodo compiler and MPI parallelization technologies were developed through years of R&D. Bodo is architecturally up to 20x more efficient than existing data warehousing and Spark data processing engines.Take a peek under the hood
Are large data transformations and joins resulting in long-running and expensive SQL/Python jobs? Bodo's parallel architecture provides the most efficient design for query execution, resulting in far shorter query run times and greatly reduced cloud spending.See our benchmarks
Bodo's efficient architecture delivers a significant performance boost, accelerating compute-intensive ETL queries by 5-10x. It's engineered to scale up to petabytes of data and thousands of cores without any degradation in performance, tackling the challenges of growth and escalating cloud infrastructure costs.Learn more
Bodo seamlessly integrates with your current tech stack with minimal engineering effort via our SDK and connectors. With compatibility across Python, ANSI SQL, Snowflake SQL, Iceberg, and more, Bodo supports the languages and tools you prefer without the need for code modifications.Learn more
This is the real deal. Bodo built on the success of Numba to combine compiled Pandas and automatic parallelism (with MPI) to get incredibly fast data processing using simple syntax. It is particularly great for ETL. It can make your code using Python *fast*—simply.
With a high-performance computing (HPC) framework like Bodo, the execution of numerical operations can be made significantly more efficient without having to provision clusters or extensively rewrite code manually.
With Bodo, we are now able to scale up our MBA (market basket analysis) metrics to longer periods with much better results. On the same infrastructure, Bodo takes around 6 mins for the workload that takes Databricks 1.1 hours.