Are you relying on default settings and ad‑hoc tweaks? Ready to achieve predictable, right‑sized performance across your lakehouse and query engines? Our team has tuned columnar systems since the early days at Full 360. OmniArcs offers Upshift: a focused engagement to benchmark, tune, and operationalize performance.

Modern Performance Engineering (Upshift)

Business Challenge

Modern engines (DuckDB, Databricks SQL/Spark, Trino/Presto, Redshift, BigQuery, Snowflake) deliver major gains, but many implementations underperform due to modeling, partitioning, workload management, and operational gaps. Without systematic tuning and governance, costs rise and predictability suffers.

Business Solution

OmniArcs runs a comprehensive, workload‑based benchmark, then optimizes models (stars/snowflakes), storage formats (Parquet), table layouts (Iceberg partitions/sorts), and workload management. We provide pre/post metrics, right‑size clusters/serverless configs, and recommend query/pipeline changes. We align governance (catalog policies, lineage) and observability to maintain performance over time.

Customer Outcomes

• Higher and more predictable performance across engines
• Operational continuity during tuning
• Lower TCO via right‑sizing and lifecycle policies
• Clear performance roadmap, SLOs, and guardrails
• Governance and observability integrated into operations

Timelines & Costs

Typical tuning cycles complete in ~4–8 weeks per environment, depending on data volume and workload complexity.

Customers We Serve

Teams operating lakehouse/table formats (Iceberg/Delta), query engines (DuckDB, Databricks, Trino/Presto), and/or columnar warehouses seeking reliable performance and cost control across analytics and BI workloads.