Cloud Data Warehousing Platform - Picked, Implemented, and Tuned for Your Workloads.

No - we optimize for your workloads, not our partner tier. About half our customer base is on Snowflake, a quarter on Databricks, the rest split across BigQuery, Redshift, and emerging Iceberg-on-S3 patterns. Each warehouse wins for different workload profiles: Snowflake for SQL-heavy enterprise analytics, Databricks for ML-adjacent and Spark-native, BigQuery for ad-tech-style aggregation and Google ecosystem alignment, Redshift for AWS-deep stacks. We take partner certifications across all four because customers expect it, but we don't take partner kickbacks that would compromise our recommendation. Our principal architects are credentialed across all four warehouses and rotate to maintain neutrality.

2-4 weeks. Week 1: workload extraction and characterization (top 50-100 representative queries, plus typical concurrency, schema sizes, and data volumes). Weeks 2-3: benchmark execution on each candidate warehouse (Snowflake, Databricks, BigQuery, Redshift), with realistic compute sizing and concurrency. Week 4: TCO analysis (compute, storage, egress, operational lift, vendor support), capability-fit assessment (does the warehouse handle your specific workload patterns), and recommendation. Output is a written report defensible to your CFO and architecture review board. Benchmark is fixed-fee, runs $40-80K depending on workload complexity, and is creditable against implementation if you proceed with us.

Sure - we implement on Snowflake, Databricks, BigQuery, or Redshift directly without the comparison phase if you've already decided. Implementation includes: source-system ingestion, transformation orchestration (dbt, Spark, Python), governance (catalog, lineage, access control), observability, cost telemetry, and CI/CD for data pipelines. Mid-market implementation (single warehouse, 50-100 pipelines) typically runs 12-16 weeks fixed-fee at $200-500K. Enterprise implementation (multi-warehouse, regulated, 200+ pipelines) runs 6-9 months at $1-3M fixed-fee. We don't push the benchmark on customers who've already chosen - that's an integrity matter, not a sales tactic.

Yes - proven playbooks for Snowflake↔Databricks, Databricks↔BigQuery, Redshift→Snowflake, Teradata/Netezza/Oracle→cloud, and on-prem Hadoop→cloud lakehouse. Migration includes parity validation (every report regenerated and reconciled cent-for-cent), parallel running until every dependent stakeholder signs off, and phased cutover (BU by BU, not big-bang). Migration timeline: 6-12 months for cloud-to-cloud, 12-18 months for on-prem-to-cloud at enterprise scale. Migration is fixed-fee at the milestone level and includes a documented rollback procedure rehearsed before cutover. Most customers stay multi-warehouse permanently for different workload classes.

20-40% in the first year - primarily from compute right-sizing (most warehouses are over-provisioned by 30-50% on day one), query optimization (the top 10 queries usually account for 40-60% of compute spend, and most can be optimized), and workload separation (BI vs ML vs ingestion on appropriately sized compute). Year-2 savings are smaller (5-15%) as the easy wins are captured, but governance and reliability improvements compound. We benchmark your baseline in week one and measure savings against your numbers, not industry averages - so the savings claim survives CFO scrutiny. Savings are net of Logiciel platform fees.

Yes - optional managed operations tier with US-aligned 24/7 coverage. Standard tier (8x5 US business hours, named US TAM, P1 < 1hr first response) starts at $40K monthly. Enterprise tier (24/7, P1 < 15min, US-citizen pool, dedicated principal architect, quarterly executive QBRs) starts at $120K monthly. Managed operations covers pipeline reliability, cost optimization, governance changes, capacity planning, and incident response. Customers often start self-operated and add managed operations after 6-12 months as the footprint grows. Most don't need fully-managed indefinitely - knowledge transfer is a contractual deliverable, not optional.

Heavily - most modern customers want Iceberg as the storage layer with multiple compute engines on top (Snowflake, Databricks, Trino, Spark, Athena), avoiding lock-in to a single warehouse vendor. Logiciel manages Iceberg tables (compaction, snapshot expiration, partitioning, schema evolution) and federates queries across compute engines on the same tables. Delta Lake and Hudi are also supported for customers committed to those formats. About 40% of our 2026 customer onboarding involves an Iceberg-first architecture - typically as part of a multi-engine strategy (Snowflake for SQL analytics, Databricks for ML, Trino for federated query) on shared storage.