1# Import required libraries2from theseus.cluster import GatewayWorkerCluster3import ibis45# Initialize a Theseus cluster with 1 worker6tc = GatewayWorkerCluster(n_workers=1).__enter__()78# Connect to Theseus using Ibis and the cluster's location9con = ibis.theseus.connect(tc._location)1011# Read a Parquet file into Theseus12con.read_parquet('example.parquet', table_name='example')1314# Execute a SQL query and convert results to pandas DataFrame15con.sql('select * from example limit 10').to_pandas()
[ Lower is faster, to the left is cheaper ]
By rethinking query execution for GPUs, Theseus parallelizes complex queries and outperforms leading engines, delivering results in seconds.
Process data and drive LLM workflows on the same GPU. Theseus adds GPU‑powered UDFs to SQL, enabling full pipelines, from embedding and vector search to RAG in Dataframes and SQL.
Theseus provides fast, in-place queries for data lakes, lakehouses, and warehouses, and supports Apache Iceberg and standard file formats.
Powered by a Kubernetes-native control plane, Theseus deploys across public and private clouds using existing authentication and air-gapped support for maximum security and compliance.
Built for heterogeneous, accelerator-native environments, the Theseus Query Profiler monitors the engine at run-time, enabling real-time analysis of query performance at CPU, GPU, and I/O layers with zero overhead.
Run locally. Conda install Theseus and start querying your data immediately.
Setup time: ~5 minutes
1conda install --override-channels \2 -c voltrondata-nightly -c rapidsai -c conda-forge \3 theseus-dev quenta cuda-version-12.8 python=3.10
Evaluate performance on larger datasets using our cloud-based testing environment.
Setup time: ~1 minute
Deploy Theseus in production with enterprise scale, white-glove support, and custom deployment options.
Contact sales for setup