Web Scraping
15 Web Scraping Use Cases Delivering Hard ROI in 2026
Introduction The web scraper software market sat at USD 814.4 million in 2025. By 2033 the same forecast lands at...
Data Warehouse Design for Analytics
Data warehouse design services build the data model, storage, and governance layer a company needs for fast reporting, unified numbers, and governed data ready for BI and AI workloads. Done right, reports stay fast as data grows and the same foundation trains models.
100+
software engineers
15+
years industry experience
$1 - 100 bln
working with clients having
Fortune 500
clients served
We are trusted by global market leaders
What Our Data Warehouse Design Services Include
Most teams know the reports they want; the harder question is the model underneath — and that is where our data warehouse design solutions earn their place.
Data Architecture Planning and Modeling
We map sources, workloads, and growth before drawing a table, so the architecture fits the decisions it must drive.
Schema Design for Analytics and Reporting
We pick the schema — star, snowflake, Data Vault, or Medallion — that fits how your team queries, not habit.
Storage and Query Optimization
Tiered storage and partitioning are designed in from the start, so query performance holds steady as data volumes climb.
Governance, Security, and Access Design
Lineage, role-based access, and regulatory fields are modeled in, not bolted on the week before an audit.
Source Integration and Reconciliation
We model how every source maps in, including the ones with no shared key, so reconciliation is built into the design.
Custom and Enterprise Warehouse Design
When the workload is unusual, the model starts from your business logic, not a recycled template.
Why Businesses Need Data Warehouse Design
Most warehouse problems trace back to the model, not the hardware. A schema built for one reporting need buckles under the next — and the fix is a rebuild, not more compute.
Fragmented Data and Inconsistent Reporting
Pricing sits in one system, inventory in another, and finance in a third. Until one model reconciles them, every report opens with an argument over whose number is right.
Poor Schema Design and Slow Query Performance
A schema built for transactions chokes on analytics; we have watched dashboards time out at volumes a properly modeled warehouse serves in seconds. The fix is structural, not more hardware.
Legacy Architectures That Limit Scalability
On-prem warehouses that cannot scale elastically turn every new source into a capacity project. A North American agricultural supplier hit that wall at roughly 20 databases before redesigning around a lakehouse.
A Model That Demos Well but Caps the Roadmap
A schema picked to look clean in a sales demo often cannot answer the second team’s question a year later, and retrofitting costs more than designing it right once. As our lead architect puts it: a clean-looking schema quietly caps your roadmap the day a second team needs a different grain.
BI vs AI: Which Model Your Warehouse Needs
A reporting warehouse and a prediction warehouse store data differently, so a model bolted onto a BI schema trains on numbers that no longer match production. For BI, a dimensional model usually wins; for AI, the layers are designed so training data and live data stay in sync, with lineage traced back to the model. GroupBWT chooses after seeing your query patterns, so the right data warehouse design solutions follow the workload, not a default.
Cloud or On-Prem — and Which Platform
For most teams the question is no longer whether to move to the cloud but which platform earns the workload — Snowflake, Databricks, and Redshift each win on a different one. We test each against your query patterns, retention, and cost curve, rule the wrong ones out before any build, and tell you exactly why. On-prem still makes sense where data residency or sunk hardware says so, and GroupBWT designs for that case too — the platform follows the workload, not a partner badge.
Migrating Your Warehouse Without Downtime
Keeping reports live during a migration is the default, not a premium add-on. GroupBWT designs the target model and the cutover path together, then moves sources in stages behind the running system, so each feed is validated before it carries a report. One North American agricultural supplier moved its legacy SQL Server estate into a governed lakehouse this way — in stages, without losing a reporting day.
What Drives the Cost and Timeline of a Warehouse Design
Three things move it most: how many sources you have and how messy they are, how complex the model is, and how fresh the data must stay. A clean three-source warehouse takes weeks; a 20-source legacy migration is a phased program over months. Building in-house can look cheaper until you count the senior engineering time it ties up and the rebuild after a wrong early model — we grade your sources in the first week, so the estimate rests on real data, not a guess.
Will Your Warehouse Scale?
Pick the review that fits: get a warehouse design risk review or send your sources and SLAs for an architecture assessment
Fragmented Data and Inconsistent Reporting
Pricing sits in one system, inventory in another, and finance in a third. Until one model reconciles them, every report opens with an argument over whose number is right.
Poor Schema Design and Slow Query Performance
A schema built for transactions chokes on analytics; we have watched dashboards time out at volumes a properly modeled warehouse serves in seconds. The fix is structural, not more hardware.
Legacy Architectures That Limit Scalability
On-prem warehouses that cannot scale elastically turn every new source into a capacity project. A North American agricultural supplier hit that wall at roughly 20 databases before redesigning around a lakehouse.
A Model That Demos Well but Caps the Roadmap
A schema picked to look clean in a sales demo often cannot answer the second team’s question a year later, and retrofitting costs more than designing it right once. As our lead architect puts it: a clean-looking schema quietly caps your roadmap the day a second team needs a different grain.
BI vs AI: Which Model Your Warehouse Needs
A reporting warehouse and a prediction warehouse store data differently, so a model bolted onto a BI schema trains on numbers that no longer match production. For BI, a dimensional model usually wins; for AI, the layers are designed so training data and live data stay in sync, with lineage traced back to the model. GroupBWT chooses after seeing your query patterns, so the right data warehouse design solutions follow the workload, not a default.
Cloud or On-Prem — and Which Platform
For most teams the question is no longer whether to move to the cloud but which platform earns the workload — Snowflake, Databricks, and Redshift each win on a different one. We test each against your query patterns, retention, and cost curve, rule the wrong ones out before any build, and tell you exactly why. On-prem still makes sense where data residency or sunk hardware says so, and GroupBWT designs for that case too — the platform follows the workload, not a partner badge.
Migrating Your Warehouse Without Downtime
Keeping reports live during a migration is the default, not a premium add-on. GroupBWT designs the target model and the cutover path together, then moves sources in stages behind the running system, so each feed is validated before it carries a report. One North American agricultural supplier moved its legacy SQL Server estate into a governed lakehouse this way — in stages, without losing a reporting day.
What Drives the Cost and Timeline of a Warehouse Design
Three things move it most: how many sources you have and how messy they are, how complex the model is, and how fresh the data must stay. A clean three-source warehouse takes weeks; a 20-source legacy migration is a phased program over months. Building in-house can look cheaper until you count the senior engineering time it ties up and the rebuild after a wrong early model — we grade your sources in the first week, so the estimate rests on real data, not a guess.
Will Your Warehouse Scale?
Pick the review that fits: get a warehouse design risk review or send your sources and SLAs for an architecture assessment
Looking for a fast, expert response?
Send us your request — our team will review it and get back to you with a tailored solution within 24 hours.
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Data Warehouse Design by Industry
Our Data Warehouse Design Process
Five phases, each with a documented deliverable. The GroupBWT architects who design the model stay through handover — end-to-end data warehouse systems design and implementation solutions, not a slide deck.
01/05
01/05
Why GroupBWT for Data
Warehouse Design Services
Build it in-house, hire a reseller or a slide-deck consultancy, or bring in a dedicated engineering team — only one hands you a model you can run. GroupBWT designs data warehouse models your team can build on the next morning.
Proven in Production
The patterns we reuse are proven across 140+ production systems still running today, not theorized in a deck.
Vendor-Neutral on Selection
We build on cloud lakehouses, columnar warehouses, or self-managed stacks — the platform follows your workload, not a partner badge.
Designed for the Workload
Every model is shaped by the decision it has to serve, which separates our DWH design services from template work.
The Architects Stay On
The architects who built your model stay on the line when a source shifts shape — no handoff to a pool that never saw your data.
Our Cases
Our partnerships and awards
What Our Clients Say
What do you like best?
Their deep understanding of our needs and how to craft a solution that provides more opportunities for managing our data. Their data solution, enhanced with AI features, allows us to easily manage diverse data sources and quickly get actionable insights from data.
What do you dislike?
It took some time to align the a multi-source data scraping platform functionality with our specific workflows. But we quickly adapted and the final result fully met our requirements.
What do you like best?
It was incredible how they could build precisely what we wanted. They were genuine experts in data scraping; project management was also great, and each phase of the project was on time, with quick feedback.
What do you dislike?
We have no comments on the work performed.
What do you like best?
GroupBWT is the preferred choice for competitive intelligence through complex data extraction. Their approach, technical skills, and customization options make them valuable partners. Nevertheless, be prepared to invest time in initial solution development.
What do you dislike?
GroupBWT provided us with a solution to collect real-time data on competitor micro-mobility services so we could monitor vehicle availability and locations. This data has given us a clear view of the market in specific areas, allowing us to refine our operational strategy and stay competitive.
What do you like best?
The company's dedication to understanding our needs for collecting competitor data was exemplary. Their methodology for extracting complex data sets was methodical and precise. What impressed me most was their adaptability and collaboration with our team, ensuring the data was relevant and actionable for our market analysis.
What do you dislike?
Finding a downside is challenging, as they consistently met our expectations and provided timely updates. If anything, I would have appreciated an even more detailed roadmap at the project's outset. However, this didn't hamper our overall experience.
What do you like best?
GroupBWT excels at providing tailored data scraping solutions perfectly suited to our specific needs for competitor analysis and market research. The flexibility of the platform they created allows us to track a wide range of data, from price changes to product modifications and customer reviews, making it a great fit for our needs. This high level of personalization delivers timely, valuable insights that enable us to stay competitive and make proactive decisions
What do you dislike?
Given the complexity and customization of our project, we later decided that we needed a few additional sources after the project had started.
What do you like best?
What we liked most was how GroupBWT created a flexible system that efficiently handles large amounts of data. Their innovative technology and expertise helped us quickly understand market trends and make smarter decisions
What do you dislike?
The entire process was easy and fast, so there were no downsides
What do you like best?
Their deep understanding of our needs and how to craft a solution that provides more opportunities for managing our data. Their data solution, enhanced with AI features, allows us to easily manage diverse data sources and quickly get actionable insights from data.
What do you dislike?
It took some time to align the a multi-source data scraping platform functionality with our specific workflows. But we quickly adapted and the final result fully met our requirements.
What do you like best?
It was incredible how they could build precisely what we wanted. They were genuine experts in data scraping; project management was also great, and each phase of the project was on time, with quick feedback.
What do you dislike?
We have no comments on the work performed.
What do you like best?
GroupBWT is the preferred choice for competitive intelligence through complex data extraction. Their approach, technical skills, and customization options make them valuable partners. Nevertheless, be prepared to invest time in initial solution development.
What do you dislike?
GroupBWT provided us with a solution to collect real-time data on competitor micro-mobility services so we could monitor vehicle availability and locations. This data has given us a clear view of the market in specific areas, allowing us to refine our operational strategy and stay competitive.
What do you like best?
The company's dedication to understanding our needs for collecting competitor data was exemplary. Their methodology for extracting complex data sets was methodical and precise. What impressed me most was their adaptability and collaboration with our team, ensuring the data was relevant and actionable for our market analysis.
What do you dislike?
Finding a downside is challenging, as they consistently met our expectations and provided timely updates. If anything, I would have appreciated an even more detailed roadmap at the project's outset. However, this didn't hamper our overall experience.
What do you like best?
GroupBWT excels at providing tailored data scraping solutions perfectly suited to our specific needs for competitor analysis and market research. The flexibility of the platform they created allows us to track a wide range of data, from price changes to product modifications and customer reviews, making it a great fit for our needs. This high level of personalization delivers timely, valuable insights that enable us to stay competitive and make proactive decisions
What do you dislike?
Given the complexity and customization of our project, we later decided that we needed a few additional sources after the project had started.
What do you like best?
What we liked most was how GroupBWT created a flexible system that efficiently handles large amounts of data. Their innovative technology and expertise helped us quickly understand market trends and make smarter decisions
What do you dislike?
The entire process was easy and fast, so there were no downsides
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FAQ
What is the difference between a data warehouse, a data lake, and a lakehouse?
A data warehouse stores modeled, query-ready data for reporting; a data lake holds raw files of any shape for later use; a lakehouse keeps both on one platform, so raw and modeled data sit side by side. Most teams do not need to choose in the abstract — the right answer follows what you report on and how fresh the data must be. We map your sources and workloads first, then design the model on whichever of the three fits your situation, so you are not paying to store data one way and query it another.
How is warehouse design different from development?
Design decides the architecture, schema, grain, and governance; development builds the pipelines that load it. Skip the design and you usually pay for it twice — once to build, once to tear down and rebuild. We handle both — data warehouse (DWH) development and design services, not build-only.
We have the design — who builds the warehouse?
We do. The design is not a slide deck you hand to someone else to interpret — the same GroupBWT team carries it into build through our Data Engineering, where the pipelines that load and validate your warehouse are written from the same business logic as the model. We also carries the model into Data Governance and Business Intelligence, so governance and dashboards read from the one model you designed instead of drifting from it. Building under the same roof that designed it keeps the model and the pipelines in step, so there is no second discovery phase when the build starts. You can also take the documented design to your own engineers — it is built to be handed over either way.
Do you design inside our own cloud, and do we keep the data?
Yes. We design and deploy the warehouse inside your own AWS, Azure, or GCP account, and the data is yours from day one. It is built to be handed over — documentation, lineage, and runbooks included — so your team can run it without us. As a B2B engineering partner, that handover is the point, not a lock-in.
You have an idea?
We handle all the rest.
How can we help you?
