Data Engineering
Snowflake Cost Optimization That Starts With a Real Audit
A lower Snowflake bill matters only if essential dashboards, pipelines, and operational requests still work. First identify which cost category...
Data Lake Development Services
GroupBWT brings your sources into one data lakehouse. Your reporting and AI teams can use the same infrastructure instead of maintaining separate platforms. We build it around the data they need, with cloud costs and reporting delays in view.
100+
software engineers
15+
years industry experience
$1B-$100B
client annual revenue range
Fortune 500
clients served
We are trusted by global market leaders
Problem → Data Lakehouse Solution → Business Result
Data Silos
Separate systems collect duplicate records. Their reports then give you different answers.
GroupBWT uses open formats to put the sources together in one central platform.
All records remain visible and available, with none lost or duplicated.
Reports arrive late
A late report leaves the decision waiting too.
Fast processing engines handle real-time streams within a layered architecture.
You see fresh analytics as the data arrives, with reports updated instantly.
High Costs
Maintaining separate stores and complicated pipelines adds significant expense.
With our data lake development solutions, storage and compute scale separately to match the workload.
Cloud infrastructure costs fall.
Errors undermine analytics
As errors and outdated files accumulate, people stop trusting the analytics.
The data passes schema validation and automated quality checks, with clear rules for cleaning it.
Reliable and verified data, fully ready for business decision-making.
Access is hard to control
Each separate system keeps its own access list. Those lists drift out of date, and one stale entry can open the door to a breach.
GroupBWT brings detailed permissions and automatically recorded audit logs into one access-management hub.
You control who opens each dataset and can prove it when security requirements are checked.
Data is not ready for model training
Existing data is difficult to prepare for training artificial intelligence models.
Vector search is built in. Data versioning and fast preparation for algorithms are supported too.
Your AI and machine learning projects can start quickly on infrastructure that's already in place.
Data Lakehouse Services & Solutions
GroupBWT provides data lake development services that bring scattered sources into one cloud platform. The services cover each stage of the build, from the first assessment to the numbers on your dashboards.
01/09
01/09
Find the data service you need now
Not sure whether you need a data lake development company or help with one part of your platform? Bring your current setup to a free 30-minute conversation with GroupBWT’s lead data architect. We’ll help you identify which work to start with.
Talk to us:
Write to us:
How You Can Work With Our Team
01
Fixed Price
You agree to the budget and timeline with us before the work starts. This suits a project whose requirements stay the same. It also works for assessments and quick proofs of concept (PoC).
02
Time & Materials
You pay for the hours worked. This suits a project whose requirements are likely to change during delivery. The scope can then move with what you learn along the way.
03
Dedicated Team
You can work with us as your data lake development services company through a dedicated engineering team. The team joins your internal processes to grow the platform over the long term.
04
Managed Services
GroupBWT provides post-launch monitoring and ongoing cloud cost control. Response times and any 24/7 coverage are defined in the agreed service-level agreement (SLA).
Estimate the project investment
GroupBWT scopes the sources, reporting needs, and access controls before estimating custom data lake development costs. The figures below change with that scope.
Build and consulting costs
Full builds tend to fall in the $150,000 to $500,000 range. The work itself takes 5 to 10 months. A smaller lakehouse with few sources and basic reporting stays near the lower end. Add real-time feeds, strict security, or difficult source systems, and an enterprise build can reach $300,000 to $500,000+. That’s where the larger build gets expensive. Staffing changes the total too. Nearshore or offshore work: $25 – $50 per hour is common. Specialized mid-market agencies: expect closer to $80 – $150+ per hour.
What cloud storage costs
Storage costs relatively little. For hot data, $0.02 per GB each month is a practical estimate across AWS S3, Azure ADLS, or Google Cloud Storage. $0.012 is the cooler-history estimate. Archives: $0.002. Those are the lower storage estimates. Storage is not the whole bill. Processing, file activity, and querying add to it.
Allow for unexpected costs
Requirements that arrive late often push data projects over budget. You won’t see every expense in the first estimate. Keep some money back for what hasn’t been estimated. 15% to 25% belongs in reserve for costs the initial estimate can’t foresee. Access rules may prove harder than expected. Some records may be missing when your compliance review checks them. Connecting Power BI, Tableau, or a machine learning model may also take more work than planned.
When a custom lake is unnecessary
If you hold under 50 TB and your reports are simple, a custom data lake may be more than you need. In that case, look at Snowflake, BigQuery, or Amazon Redshift first, since they cost less at that size.
What changes the estimate
Start with volume: the terabytes you hold now, and how quickly that number climbs. Then count your sources: each CRM, SQL database, ERP, and API that feeds the lake. If hourly or daily batches are too slow, the data needs to arrive live.
Why GroupBWT
Each difference below comes from a delivered client project, with the result it produced.
Market Signals Without Vendor Feeds
The vendor feeds behind a multinational strategy team kept missing market signals. We built a data lake that pulls competitor updates, price changes, and event notices from each source directly, and a change now reaches their dashboards within 15 minutes.
New Sources Without New Scripts
Instead of a script per retailer, we designed one reusable pipeline template for a cosmetics manufacturer that sells in 24+ countries. Because the template maps every file onto one common record, a new source now takes about a development day.
AI Scoring Without Wasted Compute
We engineered the pipeline that turns raw data from seven sources into city scores for a travel startup. Our rule-based filters remove spam before the AI model runs, which keeps compute near $0.16 per city for each refresh.
Our Awards and Partnerships
Our Cases
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.
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.
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
Should we keep one copy of the data for BI and another for AI?
No. There’s one flexible store for tables, documents, and logs, and BI and AI both read from it. Reporting still gets warehouse-grade controls on that same data. Your data scientists read the governed records that feed the dashboards. Model inputs and reported numbers then come from one source.
Do we get a security record for GDPR or HIPAA reviews?
Yes. Security goes into the architecture before anything launches. Access follows roles, and we encrypt data throughout its path. Your compliance team can look up who opened a dataset and when, because the platform logs every access on its own. Dataset owners and retention rules are written down as part of the same record.
What keeps the cloud bill under control?
Storage grows on one meter and compute on another, so a bigger archive doesn’t mean a bigger compute bill. Compute stays on just as long as a query or pipeline needs it. You pay for processing only while that work actually runs. Older records go into lower-cost storage tiers.
When can we expect a working Data Lakehouse?
We begin with the parts that carry risk. An assessment and proof of concept (PoC) tests them over 2 to 4 weeks. Core reporting comes next, in a working Minimum Viable Product (MVP) at about 2 to 3 months. New sources and controls can follow in smaller releases.
Where does our team fit in, during the build and after it?
Mostly as the people who make the calls: a business stakeholder or product owner gives us 1-2 hours per week while we build. That time goes on priorities and on the domain questions only your side can answer. We handle the engineering side: design, development, testing, and deployment. When the build is done, the source code and admin access are yours. Your team also gets documentation and training. Your team can run it from there, or keep GroupBWT on for Managed Services and technical support.
You have an idea?
We handle all the rest.
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