Has your data lake grown faster than anyone can govern it? Or are you starting fresh and want to get the foundation right the first time? We tell you whether a lakehouse is the right move, which platform fits your workloads and how to get there without disrupting reporting.
A lakehouse isn’t right for everyone. We look at your workloads, data types and team skills, and give you a straight answer on whether it pays off.
We design the layers, formats and governance model, usually as a medallion architecture with bronze, silver and gold zones.
Databricks, Microsoft Fabric, Snowflake or an open stack on AWS? Delta Lake or Apache Iceberg? We compare them on your data and your team.
Most teams don’t start from zero. We plan how to move from an existing lake, Hadoop cluster or warehouse in stages, with BI working the whole time. For Databricks-bound moves, our Databricks migration team handles the execution.
One platform for every team only works if access is tight. We design governance with Unity Catalog, Microsoft Purview or Lake Formation.
Cheap storage for raw data of any type, mostly used by data engineers and data scientists.
Structured, modeled data for fast SQL, used mostly by analysts and BI teams.
Lake storage with warehouse features added in layers, so BI and ML work on the same data.
If a well-run warehouse covers your needs, the assessment says so. Every recommendation comes with the cost and workload figures behind it.
We build on Databricks, Fabric, Snowflake and open formats, so we compare them on your data, not on a partner target.
The architects who design your lakehouse also build and run lakehouses for clients through our data engineering services.
ISO 27001-certified processes, with GDPR and HIPAA controls designed into the catalog and access model.
99% of our projects are delivered on time, so the roadmap dates are dates you can plan around.
When the plan moves to build, Relevant cuts delivery time by up to 50% with up to 20% fewer bugs through AI-assisted development.
We review your current lake, warehouse, workloads and costs, and decide together whether a lakehouse is worth it.
Medallion zones, table format, catalog and access model, written up with the trade-offs.
A scorecard and, where needed, a proof of concept on your data. If Databricks wins, Databricks implementation picks up from there.
One domain or use case picked to prove value in weeks, with success criteria agreed up front.
Waves, dates and owners for moving the rest, with BI kept live throughout.
A lakehouse pays off when a company runs a data lake and a warehouse side by side and copies data between them. The pattern usually comes with growing ML or AI plans that need raw files, documents or event data next to the modeled tables BI depends on. A lakehouse puts both on one platform, with warehouse-style transactions and access control on top of lake storage. Run a quick check today:
Many copy jobs, a high combined bill and AI plans built on unstructured data point to a lakehouse. If the warehouse already covers your reporting and the AI plans run on tables, the readiness check says so, and the warehouse stays. If you can’t pull these numbers yet, our team maps it out with you, with nothing to prepare beforehand.
The cost comes down to five variables you can estimate before the first call:
A readiness check followed by one pilot domain keeps early spend small and shows real costs before a full commitment. Rough figures are enough, and our team fills the gaps on the first call. Send your data volume, source count and current platforms when you talk to us, and you’ll get a cost range for your setup.
A readiness check and a reference architecture take a few weeks and end with a go or no-go decision and a roadmap. A pilot on one domain usually takes a couple of months. A full migration runs in waves over several quarters, and each wave delivers something your teams can use.
To speed up the start, pick a pilot candidate today: one domain with a clear owner, a handful of sources and a use case that needs both BI and ML. If no domain stands out, the readiness check picks one with you. 99% of our projects are delivered on time, so the wave dates hold.
Many teams keep the warehouse for core BI and move raw data, ML workloads and new sources to the lakehouse first. Workloads then shift to where they run best, at a pace the business sets.
To see what could move, sort your warehouse workloads into three groups: finance and regulatory reporting, ad hoc analytics, and ML feature preparation. Stable reporting usually stays put. ML preparation moves first. If the split is unclear, our team maps it out with you, with nothing to prepare beforehand. Each wave runs in parallel with reconciliation, so every number matches before cutover.
Platform fit follows the workloads and skills you already have. Databricks suits teams heavy on Spark, ML and streaming. Fabric makes sense when Power BI and Microsoft 365 are central to the business. Snowflake works well for SQL-first teams already on Snowflake that want to add Iceberg tables. An open AWS stack with Iceberg, Glue and Athena gives the most freedom and asks more of your engineers.
For a quick check, list the languages your data team writes every day and the BI tool leadership uses. Python and Spark lean toward Databricks. SQL leans toward Snowflake. Power BI leans toward Fabric. When two options score close, our consultants run a proof of concept on your data before you commit.
The table format follows the engines that will read the data. Delta Lake is the default on Databricks and Fabric, with mature tooling. Apache Iceberg has broader support across engines such as Snowflake, Athena, Trino and Spark. That breadth makes Iceberg the safer choice when avoiding lock-in matters. Interoperability between the formats keeps improving, so the decision is less permanent than it was a few years ago.
List every engine that queries your data today and every one planned for the next two years. One engine family points to its native format. A mix of engines points to Iceberg. If the list is unclear, platform selection settles the format together with the platform.
Most dashboards keep working. Power BI, Tableau and Looker connect to lakehouse SQL endpoints the same way they connect to a warehouse. Changes appear only where the data model changes. The migration plan maps every affected report and schedules its update inside the wave, so each dashboard shows the same numbers on cutover day.
Export the list of dashboards with their data sources and view counts from your BI tool. High-traffic reports on tables that move get reconciled first. If usage data isn’t available, our team builds the inventory during the assessment.
Many lakehouse projects change hands midway. A vendor delivers the first zones and leaves, or the engineers who designed the layout move on. A review of an inherited platform starts with the tables, pipelines and catalog as they are, not with a rebuild. Our consultants map zones, owners and lineage, flag tables without schemas or quality checks and document the rules nobody wrote down.
Much of the existing work usually stays. The roadmap targets the gaps in ownership and data quality management that cause most incidents. Before the first call, gather workspace access, a list of recent incidents and any handover notes. Missing documentation is common, and our team rebuilds it from the code and the catalog.
A readiness check shows whether a lakehouse will actually save you money.
Tech label
Healthcare
Tech label
