Your CRM knows one part of the story and your ERP knows another. We bring both, and everything else you run, into one AI-ready warehouse. What do you get out of it? Automated forecasts, predictive insights, real-time reports. Enough to stay a step ahead of competitors.
Some clients hand us the whole job, planning through support after go-live. Others already have engineers or a warehouse that mostly works, so full-scope data warehouse services would be more than they need. They bring us in for the one stage causing trouble: the plan, the data model, or the testing. Wherever we start, the silos go and you’re left with one source of truth, plus numbers built from raw transactions that your teams will actually act on.
Our data warehouse advisory services begin by auditing the infrastructure you have now. No stack gets recommended before that. What gets built first? The audit tells us. Which technology stacks and cloud platforms are even worth comparing? The audit again. Where do you want to be in a few years? That decides the final pick. Before any budget is committed, we map how your sources, access rules, and reports depend on each other. Every risk we find, from cost overruns to queries that slow down after launch, gets a score and a named owner. If the plan is all you need, our data warehouse consulting services cover it as a standalone project.
Key Capabilities: Solution architecture design, platform evaluation, data governance planning, and ROI business case creation.
Here our engineers build a custom warehouse from scratch. That covers the database schemas and the automated pipelines your enterprise analytics will lean on. And we size it for growth, because the data volume you have on launch day won’t stay that small. The data model follows the questions your teams actually ask. We work out where each source fits, even systems with no shared ID, so reports stay quick as volumes grow and a new department’s request does not mean starting over. This modeling work is also available on its own as data warehouse design services.
Key Capabilities: Data source mapping, dimensional modeling, automated ETL/ELT pipeline creation, and data mart setup.
Still running on a legacy on-premises platform or some other outdated system? We’ll get you onto a modern cloud data warehouse. Cutover happens after hours. Most of your staff will never know anything moved. Need more storage later? Add it when that day comes. And the upkeep bill shrinks. Your team can ship changes sooner, too.
Key Capabilities: On-premises to cloud migration, platform-to-platform migration (e.g., Teradata to Snowflake), cloud cluster cost optimization, and zero-downtime cutover planning.
Five systems, five customer counts. The CRM has one. The ERP has another. Ask e-commerce, the marketing platforms, and the financial software, and that’s three more. We wire them into one synchronized dataset. Two teams comparing numbers after that are finally comparing the same thing.
Key Capabilities: Multi-source data consolidation, native API integration, Change Data Capture (CDC), and automated data validation.
A daily report is too slow for some decisions. For those, there are our real-time data warehouse services. We process the data as it streams in. Did something shift in operations? Managers know right then. No waiting a day for the next report.
Key Capabilities: Streaming ingestion pipelines, event-driven processing, low-latency analytics, and real-time operational reporting.
We’re still around after launch, doing maintenance and testing. Testing covers every step between the source system and the dashboard, net revenue rules and other business logic included, so a renamed column or a changed date format is caught before it breaks a report leadership relies on. This validation is also available as a separate project through our data warehouse testing services. If a query starts dragging, we tune it. Your data quality bar stays where you set it. Then there’s cloud spend. Idle capacity still gets billed. That waste? Ours to find, ours to cut.
Key Capabilities: Query performance tuning, ETL/ELT quality assurance testing, cloud expense optimization, and regulatory security patching.
Follow one record from the scattered tool it starts in to the report someone finally acts on. It makes four stops.
01
Collect
Raw data comes in from each separate app you use (sales, finance, CRM, web).
02
Clean & Format
Mistakes and duplicates get stripped out. Then everything is reshaped into one structure.
03
Store
Yesterday’s records and today’s go into one secure place. That’s your “single source of truth”.
04
Analyze
Clean data lands in dashboards and reports. Those are what people open when a decision can’t wait.
Data Consolidated: Policy details, claims histories, agent reports, payout records, and third-party property or telematics data.
Business Value: Every claim can be read next to the agent’s report and the payout record. Fraud patterns that used to hide between them come out earlier. Underwriting moves onto automated workflows. Claims get closed sooner, and there is less work in solvency reporting.
Start by telling us which tools you’re on. We’ll look at the setup and point out where data is getting trapped in silos between your CRM, ERP, and operational tools.
We build a central warehouse in stages, turning data split across business tools into one reliable source of truth. Each of our data warehouse services follows the steps below, which keeps disruption to your daily operations low.
Where the raw data starts: the transactional apps your business runs on every day.
CRM and Support Platforms
Salesforce, HubSpot, Zendesk.
ERP and Commerce Systems
SAP, Oracle ERP, Microsoft Dynamics 365, Shopify.
This is where records leave their source systems. Pipelines pull them out automatically and fix errors and formats on the way, so what reaches central storage is already standardized.
Databricks
Delta Live Tables and Spark.
Integration Services
Azure Data Factory, AWS Glue, Fivetran, Talend, Informatica, Matillion.
Orchestration
Apache Airflow.
Strip any data warehouse solution down and this layer is what’s left. It’s a single repository designed to keep huge volumes of business history and still return analytical queries quickly.
Databricks
Lakehouse and Databricks SQL.
Cloud Data Warehouses
Snowflake, Google BigQuery, Amazon Redshift, Microsoft Azure Synapse Analytics / Fabric.
Raw tables? Hard to read unless you built them. This layer fixes that. It turns them into business entities such as sales or customers, and a metric gets defined here once. After that, every department counts it the same way.
Transformation Modeling
dbt (data build tool).
Semantic Models
Looker Semantic Layer, Cube, Microsoft Fabric Semantic Models.
Monday morning report? You’re looking at this layer. Interactive dashboards and trend reports sit here, for decision-makers and analysts. The other layers? They exist so these numbers are right.
Dashboard Platforms
Microsoft Power BI, Tableau, QlikView.
Cloud BI Services
Looker Studio, Amazon QuickSight.
The last layer wraps around the rest. Access security, regulatory compliance, and data quality audits are handled here. It’s also where the predictive machine learning models run.
Databricks
Unity Catalog and Mosaic AI.
Access and Key Management
Azure Active Directory / Key Vault, AWS KMS.
Data Quality and ML Services
Acceldata, Amazon SageMaker, Azure AI Services.
Plenty of teams can build a database. The harder part is turning scattered data into a business advantage you can measure. Why do leading enterprises trust GroupBWT as their strategic data engineering partner? A few reasons.
Our Related Expertise
One store for raw structured and unstructured data, with replayable history.
Moving existing ETL jobs to cloud platforms with parallel runs and reconciliation.
Legacy warehouses, data lakes, and pipelines moved onto Databricks, with a cutover we control one step at a time.
Sales, stock, and user-flow data analyzed together in a single view.
Setting the rules for access control, data quality, and compliance.
Metric definitions every team shares, plus dashboards that run on warehouse data.
What's a data warehouse, and does our business need one?
In short, a cleaned and organized home for all your business data. Your CRM sends data there. So does your ERP, and so do billing software and your other operational tools. From then on, ask any department for a number and you get the same one. Nobody has to stitch spreadsheets or export files together by hand anymore. There’s also a less obvious benefit: heavy reporting runs on the warehouse instead of your transactional databases, so pulling a big report doesn’t slow down the systems your business runs on every day.
What signs show that a company needs data warehouse services?
It tends to happen once reports keep stalling because your systems don’t talk to each other. The usual signs:
What will we get back, in benefits and in ROI?
What changes first? The everyday stuff. Executives get reports without waiting, nobody rebuilds the same report by hand, and a few manual steps drop out of the workflow. Then there’s the money. Industry studies show companies on modern analytics platforms reaching a 209% ROI over three years and paying back the cost in under six months, and most of that comes from spending far less labor on data prep. For modern cloud data warehouses, reported returns run over 400%. Why that high? Leaner operations, mostly. Plus quicker answers when the market moves.
Cloud, our own servers, or both - where does the warehouse go?
Cloud Data Warehouses (such as Snowflake, Amazon Redshift, or Google BigQuery) run on someone else’s hardware, and you rent what you need. Busy month? Take more. Quiet one? Hand it back. That suits a business with seasonal peaks. No servers to buy, either. What you use is what you pay.
On-Premises Deployments flip that. The servers are yours, bought outright and kept in your building. Who goes this way? Mostly companies with strict data residency rules. Also anyone whose on-site systems can’t tolerate much latency.
Hybrid Deployments split the job. Sensitive data? Kept on your own servers. Analytics? Off to the cloud, where you add or drop capacity as needed.
How long do data warehouse services take, and what's the price tag?
Focused Projects (MVP): Picture one or two primary data sources with working dashboards on top. These usually wrap up in 6 to 12 weeks and cost $20,000 to $75,000.
Enterprise-Wide Systems: Lots of sources now. A legacy migration. Custom data pipelines and strict compliance controls on top. That kind of build typically needs 3 to 6 months. Expect $75,000 to $200,000+.
Maintenance: Each year, upkeep generally comes to 15% to 25% of the build cost. It covers system adjustments and pipeline changes.
What about our sensitive data?
Built right, a warehouse usually keeps it safer than scattered files do. Access is set role by role, right down to single fields. Data in transit? Encrypted. At rest? Also encrypted. No reason to see a sensitive value? Then you see it masked. Audit trails and lineage tracking, too, plus regulatory compliance (such as GDPR, HIPAA, or SOC 2). Much easier to enforce in one governed system.
What does a data warehouse have to do with AI?
BI dashboards are only part of it. Your AI tools lean on it just as hard. Think about where machine learning models and autonomous agents get their lessons. From your company’s own history. History that’s messy, ungoverned, or unstructured? They’ll learn from that too, and badly. Would you let the result touch your business workflows? We wouldn’t.
How can we help you?
We’ll review your goals and suggest the right solution — technical, scalable, and tailored to you.
What do you need extracted, structured, or automated?
