The costly mistakes happen before anyone writes a data pipeline. We help you get the platform, the architecture and the plan right first.
A health check of the warehouse you already run: models, pipelines, query patterns, costs and access rules.
We connect the warehouse to the decisions it should support, then plan how to get there in releases rather than one long project.
Kimball, Inmon or Data Vault? Cloud, on-prem or hybrid? One central warehouse or domain-owned marts? We make those calls with you and write down why.
We compare Snowflake, BigQuery, Redshift, Microsoft Fabric and Databricks SQL against your workloads, skills and budget.
Cloud warehouses make it easy to overspend. We find where the money goes and how to get it back without slowing anyone down.
We check whether your warehouse has the definitions, lineage and access controls that audits and AI projects both depend on. Ongoing rules and ownership fall under data governance.
The people advising you design, build and support warehouses for a living, so the plan has to survive contact with real data.
We work on Snowflake, BigQuery, Redshift, Fabric and Databricks. The platform we recommend is the one that fits.
Assessments run on a fixed scope with a set end date. 99% of our projects are delivered on time.
ISO 27001-certified processes, with GDPR and HIPAA requirements part of every design review.
Data engineering, analytics and BI under one roof. If you want us to build what we designed, there’s no handoff gap.
96% employee retention, so the architect who knows your warehouse is still here next year.
We agree on the business questions the warehouse must answer and the decisions this engagement should settle.
We review sources, models, pipelines, query logs and billing, and talk to the people who use the reports.
We shortlist architectures and platforms, score them, and test the close calls on your own data.
You get the design, the cost model and the reasoning, and your team gets time to push back.
We phase the work into releases with dates and owners, starting with one that pays off fast.
Your team builds from the plan, or we do. If the hard part is wider than the warehouse, data engineering consulting covers pipelines and the rest of the platform.
Data warehouse consulting settles what to build and how before the budget is committed. A typical engagement covers five pieces of work: a review of the current warehouse, a target architecture, a platform choice, a three-year cost model and a phased roadmap. Some teams only need one piece, for example a platform decision ahead of a contract renewal.
To scope your own engagement, write down the one decision the warehouse work must settle in the next six months. A single decision usually means a narrow, fixed-scope engagement. Several open decisions point to a full assessment first. If you’re not sure, our team maps it out with you, with nothing to prepare beforehand.
Most companies call in a consultant when the warehouse starts producing arguments instead of answers. Run two checks this week:
If either check fails, an outside review usually pays for itself. If you can’t run the checks yet, our team maps it out with you, with nothing to prepare beforehand. Sometimes the review shows the warehouse only needs tuning, and the report says exactly that.
The cost depends on four variables you can count before the first call:
A fixed-scope assessment is the easiest way to start, with a known budget and end date. Rough figures are enough, and our team fills the gaps during the first call. Send those four numbers when you get in touch, and you’ll receive a cost range for your setup.
A focused assessment delivers ranked findings and a roadmap in 14 days. Adding a target architecture, a platform evaluation and a TCO model extends the work to four to eight weeks, depending on the number of sources and stakeholders. The assessment leaves you with:
Speed depends mostly on access. Check today whether you can grant read access to query logs, billing data and transformation code within two days. If access needs security sign-off, start that request now. Our team works from interviews and exports in the meantime. 99% of our projects are delivered on time, so the roadmap dates hold.
For most companies, a cloud warehouse means less ops work and easier scaling. On-premises or hybrid still fits two situations: regulators require data to stay on your own infrastructure, or you own hardware with years of useful life left.
To check your case, list the data sets under residency rules and the remaining depreciation on current hardware. No restricted data and little hardware value left point clearly to the cloud. A mixed picture usually leads to a hybrid design, with regulated data on-premises and analytics in the cloud. If the rules are unclear, our team maps them out with you, with nothing to prepare beforehand. Either way, the design keeps a path to the cloud open.
The fastest savings come from a small number of expensive workloads. In Snowflake, the usual cause is oversized or always-on virtual warehouses. In BigQuery, the usual cause is full scans on unpartitioned tables.
Run one check today: pull the top 20 queries or dbt models by cost for the last 30 days. Those few workloads often make up most of the bill. Rewriting them and right-sizing compute brings the spend down. Budgets and alerts keep it from drifting back up. If you can’t pull that report yet, our team builds the cost view as the first step of the assessment.
AI needs clean, documented and traceable data, and a well-run warehouse already provides that for structured data. A lakehouse adds cheap storage for raw and unstructured data, such as documents, images and event logs, with warehouse-style controls on top. A lakehouse is therefore the stronger fit for large ML workloads and RAG over documents.
To check your case, list the first two AI use cases on your roadmap and the data each one needs. Tables from ERP, CRM or finance systems point to the warehouse. Documents, text or sensor data point to a lakehouse. When the list is mixed, data lakehouse consulting compares both setups on your use cases and costs.
Many warehouses change hands: the original vendor leaves, or the engineers who built the models move on. A review of an inherited warehouse starts with the code and query logs, not with a rebuild. Our consultants map the models, sources and jobs, flag the fragile parts and document the logic nobody wrote down.
Much of the existing work usually stays. The roadmap targets the models and jobs that break most often or cost the most. Before the first call, gather repository access, a list of recent incidents and any handover notes. Missing documentation is common, and our team reconstructs it from the code. You end up with a warehouse your own team can run, plus a clear order of fixes.
Start with an assessment and see what you’re really working with.
Tech label
Healthcare
Tech label
