Snowflake Consulting Services
Move from siloed systems and unreliable reports to a unified Snowflake data platform. We handle the assessment, architecture, and migration so you can focus on faster insights, lower costs, and AI-ready analytics.
Snowflake: What You Need to Know Before You Invest
How can we know if Snowflake is the right data platform for our organization and use cases?
Snowflake is usually a good fit if you want to centralize data from multiple systems, scale analytics and AI quickly, and avoid managing infrastructure. It makes the most sense when you have – or plan to have – multiple data domains, many users, and diverse workloads (BI, data science, AI applications) that need consistent, governed access to shared data.
What are the realistic alternatives to Snowflake, and how do they compare for our needs?
Alternatives typically include cloud-native warehouses and lakehouses from hyperscalers (platforms tightly integrated with AWS, Azure, or GCP) and open data lakehouse stacks. These may be better if you want deep integration with a single cloud provider or prefer DIY control, whereas Snowflake is often chosen for its cloud neutrality, simpler operations, and stronger focus on data sharing and AI-ready architecture.
How does Snowflake compare to Databricks, and when should we use which platform?
Snowflake is usually the first choice when the priority is a governed, SQL‑centric analytics and BI platform that is easy to operate and adopt across the business, while Databricks is often preferred when you need a more engineering‑heavy, open lakehouse for advanced data engineering, streaming and large‑scale ML.
In practice, many organizations use both: Snowflake as the core analytics and data sharing platform, and Databricks for heavy data science, feature engineering and ML workloads, with data flowing between them as part of one modern data stack.
Read more: Databricks vs Snowflake: How to Choose the Right Platform for Enterprise AI
What business value and ROI can Snowflake deliver for us in the next 6–12 months?
In the first year, organizations typically see faster access to data, fewer manual exports, and shorter delivery times for analytics and AI use cases. Cost savings often come from retiring legacy warehouses and tools, reducing maintenance overhead, and improving decision-making with more reliable, timely data.
How will Snowflake help us prepare our data for AI, machine learning, and advanced analytics while keeping performance, costs, and governance under control?
Snowflake provides a single, governed repository for data that feeds BI, ML, and AI – so teams work from consistent, well-controlled datasets instead of ad-hoc extracts. Because compute and storage are separated and workloads can be isolated, you can tune performance, apply cost controls, and enforce security and governance policies while still giving AI and analytics teams the freedom to experiment.
How can we migrate to Snowflake without disrupting critical reports, analytics, and operations?
A low-risk migration starts with an assessment of current data sources, workloads, and reports, then prioritizes must-have use cases for a phased cutover. Critical dashboards are replicated and tested in Snowflake in parallel, with automated validation checks, before any legacy systems are switched off, ensuring users experience a smooth transition rather than a disruptive big bang.
Clients that trusted us
What our clients say
The communication process was smooth, which assured us that Addepto was devoted to preparing a high-quality product.
Director of Machine Learning – Wealth Management Company – Wealth Management Company
The collaborative culture during the project was unique and significantly facilitated seamless implementation. Addepto has completely satisfied our requirements.
Dawid Majcherek – Nexteer Advanced Technology Engineer – Nexteer Automotive
Addepto’s flexible team reacts to tasks rapidly.
Head of IT – SMEO S.A.
From Strategy to Execution: Snowflake Consulting on Your Terms
Start with a clear, vendor‑independent roadmap you can execute in‑house, or extend seamlessly into full Snowflake deployment and optimization with our team.
Discovery & Fit Check
Understand business goals, use cases (BI, AI, real‑time), current pain points and constraints.
Assess whether Snowflake is the right platform and when alternatives (e.g. Databricks‑centric or other stacks) might be better.
Assessment & Target Architecture
Audit existing data landscape: sources, warehouses/lakes, pipelines, BI, costs, skills and governance gaps.
Design the target Snowflake architecture, security model, integration patterns and a pragmatic, use‑case‑driven roadmap.
Roadmap & Delivery Options
Build a 3–6–12‑month plan: quick wins, foundation work, migrations, governance and AI‑related milestones.
At this point, the consulting engagement can end with a complete roadmap and recommendations your team executes in‑house, or you can choose to move into implementation with us as your delivery partner.
Implementation & Migration (Optional Next Step)
Set up and harden Snowflake environments, roles, networking and monitoring according to the blueprint.
Deliver an MVP/pilot and then iteratively migrate and modernize priority workloads, dashboards and data products.
Optimization, Governance & AI Enablement (Optional Next Step)
Tune performance and costs, introduce workload isolation, observability and cost guardrails.
Roll out governance, documentation and enablement, and prepare Snowflake as the backbone for AI and advanced analytics, often in tandem with platforms like Databricks.
Why work with us
AI and Data Experts on board
Databricks certified Experts
We are part of a group of over 200 digital experts
Different industries we work with
Recognitions & awards
Aviation – Operations, Revenue & Loyalty Optimization on Snowflake
Improve on‑time performance and operations
Centralize flight, crew and ground‑handling data in Snowflake so operations leaders can see the true drivers of delays and turnaround issues and take targeted action.
Protect and grow route profitability
Give network and revenue management teams a single source of truth for demand, yields and ancillary revenue, supporting better route, pricing and capacity decisions.
Enhance passenger experience and loyalty value
Unify booking, travel and loyalty data to design more relevant offers, recognize high‑value customers across channels and increase lifetime value.
Automotive – Connected Vehicle & After‑Sales Intelligence on Snowflake
Create profitable, data‑driven services
Use Snowflake as the backbone for connected‑car and after‑sales analytics, helping design new subscription services, warranties and maintenance packages that reflect real usage.
Increase workshop and dealer efficiency
Combine service, warranty and parts data to identify bottlenecks, improve workshop throughput and ensure the right parts are available when vehicles arrive.
Deepen customer loyalty and retention
Build a 360‑degree view of drivers and fleets to personalize offers, service reminders and loyalty programs, increasing repeat business and brand stickiness.
Manufacturing – Operational Excellence with Snowflake
Reduce production downtime and scrap
Use Snowflake to centralize production, quality and maintenance data, so operations teams can spot issues earlier and act before they turn into costly line stops.
Optimize inventory and working capital
Give planners a single view of demand, stock levels and supplier performance in Snowflake, enabling smarter ordering decisions and lower safety stocks without increasing risk.
Improve supplier reliability
Track on‑time delivery, lead times and shortages in one place, helping procurement negotiate better terms and switch suppliers faster when needed.
Engineering & Technology – Product and Platform Analytics on Snowflake
Accelerate product decisions with real usage data
Bring product, usage and revenue data together in Snowflake so product leaders can see which features drive adoption and churn, and prioritize the roadmap accordingly.
Monetize data and services more effectively
Provide finance and sales with consistent, up‑to‑date metrics on ARR, renewals and upsell opportunities, based on unified data rather than spreadsheets and extracts.
Scale self‑service analytics for teams
Give engineers, PMs and business stakeholders governed access to the same trusted metrics in Snowflake, reducing ad‑hoc requests and decision delays.
Aviation – Operations, Revenue & Loyalty Optimization on Snowflake
Improve on‑time performance and operations
Centralize flight, crew and ground‑handling data in Snowflake so operations leaders can see the true drivers of delays and turnaround issues and take targeted action.
Protect and grow route profitability
Give network and revenue management teams a single source of truth for demand, yields and ancillary revenue, supporting better route, pricing and capacity decisions.
Enhance passenger experience and loyalty value
Unify booking, travel and loyalty data to design more relevant offers, recognize high‑value customers across channels and increase lifetime value.
Automotive – Connected Vehicle & After‑Sales Intelligence on Snowflake
Create profitable, data‑driven services
Use Snowflake as the backbone for connected‑car and after‑sales analytics, helping design new subscription services, warranties and maintenance packages that reflect real usage.
Increase workshop and dealer efficiency
Combine service, warranty and parts data to identify bottlenecks, improve workshop throughput and ensure the right parts are available when vehicles arrive.
Deepen customer loyalty and retention
Build a 360‑degree view of drivers and fleets to personalize offers, service reminders and loyalty programs, increasing repeat business and brand stickiness.
Manufacturing – Operational Excellence with Snowflake
Reduce production downtime and scrap
Use Snowflake to centralize production, quality and maintenance data, so operations teams can spot issues earlier and act before they turn into costly line stops.
Optimize inventory and working capital
Give planners a single view of demand, stock levels and supplier performance in Snowflake, enabling smarter ordering decisions and lower safety stocks without increasing risk.
Improve supplier reliability
Track on‑time delivery, lead times and shortages in one place, helping procurement negotiate better terms and switch suppliers faster when needed.
Engineering & Technology – Product and Platform Analytics on Snowflake
Accelerate product decisions with real usage data
Bring product, usage and revenue data together in Snowflake so product leaders can see which features drive adoption and churn, and prioritize the roadmap accordingly.
Monetize data and services more effectively
Provide finance and sales with consistent, up‑to‑date metrics on ARR, renewals and upsell opportunities, based on unified data rather than spreadsheets and extracts.
Scale self‑service analytics for teams
Give engineers, PMs and business stakeholders governed access to the same trusted metrics in Snowflake, reducing ad‑hoc requests and decision delays.
Snowflake Core Benefits: Speed, Savings, and Security
Faster decisions and innovation
Snowflake centralizes data from multiple systems into one governed, high-performance platform, shortening the time from question to insight for business teams. This accelerates decisions on pricing, operations, product, and customer initiatives, and makes it easier to experiment with new analytics and AI use cases.
Lower total cost and reduced IT overhead
Because Snowflake is fully managed and separates storage from compute, organizations avoid large upfront infrastructure investments and pay only for the resources they actually use. This reduces maintenance and administration workload for internal teams, allowing them to focus on higher-value work instead of running and patching data warehouses.
Better governance, security, and data sharing
Snowflake provides built-in security, fine-grained access control, and secure data sharing, helping companies meet compliance requirements while still making data accessible across the business. This supports safer collaboration across departments and with external partners, increasing trust in data and enabling new data-driven products and partnerships.
Proven Data Engineering Success – Foundation for Snowflake Projects
1. Enhancing Data Infrastructure to Improve Business Analytics
Addepto revamped the data infrastructure of a retail and logistics company by consolidating fragmented data pipelines into a scalable, unified data warehouse. The project improved data quality, strengthened integration, and enabled self-service analytics — building a solid foundation for BI, AI, and future Snowflake adoption.
2. Real-Time Fraud Detection Platform for Renewable Energy Certificates
For a renewable-energy trading marketplace, Addepto built a high-performance, real-time fraud detection engine capable of validating certificate transfers under strict sequencing and auditability requirements. The solution demonstrates expertise in real-time pipelines, streaming architecture, and compliance — all critical for advanced Snowflake-based analytics and ML workloads.
3. Real-Time Baggage Tracking and Prediction Platform for Airlines
Addepto developed a real-time baggage tracking and prediction platform for an airline, enabling proactive monitoring and reducing misrouting risks. The project highlights capabilities in building scalable, real-time data processing and predictive analytics solutions — directly relevant to operational analytics on Snowflake.
What our clients say
I am impressed with their openness to new solutions and dedication to making the tool effective and easy to maintain.
Narayana Pappu CEO – Zendata
Addepto delivered a platform with AI models that resulted in time savings. The team addressed the client's core problem and went above and beyond to understand the client's tech stack. Their technological expertise and state-of-the-art technologies complemented their business-oriented approach.
Michelle Medeiros Sr Director of Data & ML – Western Governors University
They didn't just "do requirements", they investigated our needs and advised on the best processes to achieve our objectives. They were mindful of costs and gave suggestions that would be great long term solutions. But most of all, they felt like a part of our team!
CEO of SimpleCater
Communication with the Addepto team was easy and fast. Continuous monitoring and measurement of our workflow helped to identify areas for improvement. Their approach from the start allowed us to improve the product taking into account all our requirements. The obtained result fully satisfied our company.
CPO of KeyedIn Solutions
Snowflake Consulting Services - FAQ
Is our data volume and complexity “big enough” to justify Snowflake?
Snowflake usually makes sense once you have multiple data sources, several teams needing analytics, and a pipeline of BI/AI use cases—not only “big tech” or petabyte‑scale workloads. It becomes especially valuable when spreadsheets, on‑prem warehouses or DIY lakes start slowing you down, creating data silos and making governance difficult.
Will Snowflake lock us into one cloud or vendor?
Snowflake runs on major clouds and is designed to be cloud‑agnostic, so you can choose or change providers without redesigning your entire data platform. In practice this gives you more flexibility than traditional warehouses tied to one environment and lets you align with your broader cloud strategy.
How can we keep Snowflake costs under control over time?
Snowflake separates storage and compute and lets you scale warehouses up and down or turn them off, which gives very fine‑grained cost control. With proper architecture, role‑based access, resource monitors and regular usage reviews, most organizations can avoid bill shocks and steadily improve cost‑to‑value.
Our data landscape is messy. Do we need to clean everything before moving to Snowflake?
You do not need a “perfect” data environment before adopting Snowflake; it can actually be the place where you standardize and improve it. The key is to prioritize domains and use cases, migrate them in phases, and use the move as a trigger to clarify ownership, definitions and quality rules rather than trying to fix everything upfront.
How does Snowflake fit with our existing BI and AI tools?
Snowflake is designed to plug into popular BI, notebook and ML platforms, so you usually keep your preferred tools and point them to a better, more governed data source. That way, analysts, data scientists and business users work on the same trusted datasets while using interfaces and tools they already know.
