Turn scattered data into decisions you can trust

It depends on the number and complexity of source systems, data volume, the target platform, and how many dashboards you need. A focused warehouse-and-BI build is very different from a multi-source legacy migration. We start with a free consultation and a data assessment, then send back a fixed, itemized quote — broken out by phase so you can stage the investment.

A single-source warehouse with a handful of dashboards typically runs 6-10 weeks. A full OLTP-to-OLAP migration across multiple systems with governance and BI rollout usually lands in 4-6 months. We deliver in phases, so you get a working Gold-layer mart and first dashboards well before the final cutover.

There's no universal answer. Snowflake suits SQL-heavy analytics teams who want minimal platform administration. Databricks wins when you have Spark workloads, unstructured data, or ML ambitions. BigQuery is a strong fit if you're already on GCP. Synapse makes sense when you're committed to Azure and using ADF and Power BI. We assess your workload, team skills, and existing cloud spend, then recommend — our engineers are Databricks-certified and experienced across the other platforms, with no incentive to push one over another.

Yes. We handle ingestion, modeling, pipeline build, warehouse deployment, semantic layer, dashboard development, and user training as one engagement with one accountable team. You can also engage us for a single layer — a pipeline rebuild or a BI-only project on an existing warehouse — if that's what you actually need.

Yes, and we build for it. We develop a proper semantic layer so business users can drag and drop measures without writing SQL, then run role-specific training for report authors, analysts, and executives. You get documentation and recordings, so onboarding the next hire doesn't require another engagement.

Yes — we work with clients across the USA, UK, Australia, Canada, and the UAE. Our time zone is genuinely useful for data work: overnight batch windows in the US and UK are our working day, so pipeline failures get fixed while your team sleeps, and you start the morning with loads already validated. We keep deliberate overlap hours for standups and reviews.

Yes — including SSIS and DataStage estates, on-prem SQL Server and Oracle warehouses, and systems where the original documentation and developers are long gone. Our method: reverse-engineer the existing transformation logic, document it, validate it against live output, and only then rebuild it on the target platform. Hybrid setups where some sources stay on-prem are fully supported.

We offer ongoing managed support covering pipeline monitoring, failure response, data quality scorecards, and performance and cost tuning — available for dashboard extensions and new source onboarding as your needs grow. If you'd rather run it yourself, we hand over full documentation, runbooks, and code, and train your engineers to take it on — no lock-in either way.