SAS Quantome

The Upstream Bottleneck

Why Not Just BigQuery, Arrow, or Parquet?

Cloud data warehouses like Google BigQuery and columnar ecosystems like Apache Arrow, Parquet, DuckDB, Zarr, and TileDB are exceptional at what they do: downstream analytical queries, distributed state management, and high-speed random access. SAS Quantome does not replace them—It feeds them.

Today’s "ELT" paradigm encourages teams to dump messy, fragmented raw data directly into BigQuery or cloud object storage and pay massive compute bills to clean it via SQL or heavy JVM orchestrators. Before your data ever reaches a cloud warehouse or an analytical format, chaotic raw inputs need deterministic refinement. I engineer the upstream stage that resolves schema drift, normalizes ontologies, and aligns variables into contiguous binary parallel arrays at a fraction of cloud compute costs.

By applying the UNIX philosophy strictly where it excels—sequential streaming ingestion and deterministic transformation—my statically linked Go binaries process raw streams via stdin/stdout with predictable memory footprints and air-gapped security.

Zero-Lock-In Wire Format: Every refined array uses a transparent, self-verifying binary layout readable natively in Python, C/C++, or Go without proprietary drivers: an 8-byte header (4-byte "BYTE" magic string + 4-byte integer timestamp in minutes), followed by the contiguous typed payload (e.g., uint8, float64, or string), and closed with a 4-byte trailing MD5 integrity hash. Data scientists can memory-map the payload directly into Python (NumPy / PyTorch) at an 8-byte offset or load it effortlessly into BigQuery, Arrow, or Parquet—clean on arrival.

Zero Bus-Factor Architecture

Hiring a solo principal consultant carries risk only if you are handed a black box that requires ongoing vendor maintenance. My engagements are structured for immediate client self-sufficiency:

• You buy an outcome, not a dependency: The deliverable is your refined data state plus the self-contained, statically compiled Go tools engineered specifically to transform your raw sources.
• Trivial Cross-Language Readability: Because each array is simply an 8-byte timestamped header, a flat typed payload, and a 4-byte checksum footer, any Python, C, or Go developer can parse or mmap your refined data in ~10 lines of standard library code—even without my open-source library.
• 100% Transparency (Apache 2.0): The underlying synthesis engine (interlace-go) is permissive open-source software. There are no proprietary daemons, hidden telemetry, or runtime licenses.
• Direct Principal Execution: You work directly with me—no account managers, no hand-offs to junior subcontractors, and zero agency overhead. Your internal developers can audit, modify, and maintain the resulting Go code in-house from day one.

Pricing, Scope & Acceptance Criteria

Whether you are a biotech lab normalizing high-dimensional multi-omics tracks or a growing B2B team struggling to parse and reconcile disparate data sources before loading into BigQuery, engagements are structured into verifiable, fixed-rate sprints.

• Standard Engineering Sprint: €4,500 EUR / week (excl. VAT)
• Contractual Acceptance Criteria: One week is too short for vague goals. Before any sprint begins, we co-define a deterministic Sprint Specification—locking in exact input sample schemas, transformation rules, target array layouts, and pass/fail validation scripts.
• Milestone-Gated: A deliverable is accepted when the compiled binary passes the agreed validation suite and trailing MD5 integrity checks against your data. Work on any subsequent week only begins after sign-off.
• Typical Engagement: 1 week (single-source B2B parsing & schema harmonization) to 4–6 weeks (multi-track genomic or enterprise ML refinement pipelines).

Open Source Foundation & Stack

Engineered for lean, auditable infrastructure across regulated environments—from genomics and clinical research to legal tech and supply chain.

• Core Languages & Infra: Go (Golang), Python, C/C++, POSIX/UNIX streams, Bash, Slurm HPC, Google Cloud.
• Downstream Targets Supported: Google BigQuery, Python (NumPy / PyTorch zero-copy mmap), Apache Arrow/Parquet, DuckDB, PostgreSQL, Elasticsearch.
• License: Apache 2.0.

Reference Open Source Repositories:

• interlace-go: Core binary parallel array synthesis library.
• interlace-do: CLI transformation and inspection toolset.
• interlace-ex: High-dimensional genomic data refinement reference implementation.

Principal: Mario Foglio

• Role: Founder & Principal Data Engineer.
• Academic Background: BS Molecular Biology (UC San Diego) · MS Software Engineering.
• Domain Track Record: 20+ years architecting high-throughput data harmonization and bioinformatics pipelines across global research hubs.
• Research Impact: 40+ peer-reviewed publications (Oxford, EMBL-EBI, Institut Pasteur, McGill, CNG).
• Field Deployments: Former Forward-Deployed Engineer at McGill University & Kyoto University.
• Verification: LinkedIn · Google Scholar · GitHub

Initiate a Sprint Specification

Let’s turn fragmented raw inputs into deterministic, production-ready arrays.

To scope a 1-week refinement sprint and establish acceptance criteria, email me directly at connections@sas-quantome.com with:

• De-identified raw data sample (representing your messiest or widest input structures).
• Source inventory (disparate file formats, API payloads, and aggregate volumes).
• Target downstream consumer (e.g., BigQuery, Elastic, DuckDB, Arrow/Parquet, PostgreSQL, or direct memory-mapping in Python/NumPy, C, and Go).

Security note: For strict compliance, please do not transmit live credentials, PII/PHI, VPN keys, or internal repository access during initial scoping.