Scale beyond legacy systems
AI, real-time analytics, and compliance demands are pushing legacy data stacks to their limits. Business leaders want results, but data teams are stuck firefighting.
Snowstack bridges that gap with its Snowflake-first architecture, built for performance, governance, and AI readiness from day one. We modernize your infrastructure so you can move fast, stay compliant, and make smarter decisions without the overhead and enterprise costs.
Fast and cost-efficient execution
Get enterprise-grade results fast and cost-efficiently
Delivered by best-in-class engineering team
Certified Snowflake experts who deliver results right the first time
Built-in security and compliance
Governance controls, access policies, and audit trails embedded into each project
Turn your data into competitive advantage
From migration to ongoing maintenance and integration, we deliver the full spectrum of Snowflake expertise your team needs. Fast implementation, built-in security, and continuous support that adapts to your business growth
Snowflake implementation
End-to-end setup of scalable, secure, and AI-ready data infrastructure — built natively on Snowflake.
Platform team as a service
Get a dedicated, senior Snowflake team to manage, optimize, and scale your data platform without hiring in-house.
Migrations & integrations
Seamlessly move from legacy systems and connect the tools your business relies on — with zero disruption.
AI-ready healthcare data
Ensure your data is structured, secure, and compliant — ready to power AI, ML, and analytics at scale.
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FinOps
Gain full visibility and control over your Snowflake spend with cost monitoring, optimization, and forecasting.
Why leading companies choose Snowflake
Get answers in seconds
Reports that used to take all day now complete in seconds, so your team can make faster decisions with current data.
Strong ROI with smart costs
Companies get their money by paying only for what they use, with automatic optimization that cuts costs by up to 65%.
Scale with your business
Handle any data volume or user count without slowdowns—your platform automatically scales resources based on actual demand.
Deep expertise in data-intensive industries
Consumer goods
We help FMCG companies consolidate data from multiple systems into one platform, transforming fragmented information into reliable insights.
Healthcare & pharma
We unify clinical data on enterprise-grade platforms accelerating both patient care delivery and research outcomes.
Financial services
We help banks, insurers, and investment firms streamline daily reporting and enhance data security with secure platforms that save time.
How our Snowflake consulting transforms data operations
How a $45B FMCG leader regained control of their Snowflake platform with Snowstack
Companies that fail to master their data platforms in 2025 will not just fall behind. They will become irrelevant as AI-native competitors rewrite the rules of the market.
Companies that fail to master their data platforms in 2025 will not just fall behind. They will become irrelevant as AI-native competitors rewrite the rules of the market. One global FMCG manufacturer recognized this early on. By partnering with us, they turned their underperforming Snowflake environment into an innovation engine.
Key outcomes:
- 30% reduction in Snowflake costs through intelligent optimization
- 60% faster incident resolution with 24/7 monitoring
- 5+ new AI/BI use cases unlocked from reliable, curated datasets
- 100% audit readiness for SOC 2 and GDPR frameworks
Having a dedicated Snowflake team that truly understands our platform made all the difference. We no longer chase incidents or firefight pipeline issues - we’re focused on enabling the business. Their ownership, responsiveness, and expertise elevated our data platform from a bottleneck to a strategic asset. - Senior Director, Data Platforms
Client overview
The client is a multinational Fast-Moving Consumer Goods (FMCG) manufacturer operating in over 180 countries through both corporate offices and an extensive franchise network. With global revenues exceeding $45 billion and more than 6,300 employees worldwide, they manage a diverse product portfolio distributed through complex regional supply chains.
The challenge
Despite investing in modern cloud infrastructure, the client was stuck. Their internal teams lacked the specialized expertise needed to run the platform. When key engineers left, so did the expertise. This resulted in growing technical debt. Critical pipelines regularly failed or ran late. Compliance and audit demands became difficult to satisfy due to inconsistent governance. Without proper optimization, Snowflake costs increased. As a result, the platform’s reputation fell from being seen as an innovation enabler to becoming a business blocker.
What made things even harder was the seasonal nature of FMCG operations. Demand for data engineering resources fluctuated throughout the year. Resource needs spiked during busy times and dropped during slow periods. This led to ongoing hiring and retention challenges. Meanwhile, competitors kept moving forward with steady expertise and data strategies.
Our solution
The client wanted a better way to manage their data and prepare for future growth. They asked us to provide a full Snowflake delivery team that could handle the project from start to finish. Instead of hiring separate contractors, they gained a team of Snowflake-certified experts who worked together to deliver the solution quickly.
Our execution
With our support, the client regained platform stability, resolved recurring system issues, and accelerated the delivery of new data solutions. The Snowflake environment became easier to manage, more predictable, and better aligned with business priorities.
Structured collaboration
The client led a phased rollout, supported by bi-weekly service reviews and backlog planning sessions. We worked directly within their workflows (Slack, Teams, Jira) and joined daily stand-ups and steering meetings. To help address long-standing challenges with knowledge retention, we introduced clear RACI ownership and thorough documentation practices.
SLA-Driven Support Model
The engagement featured a service model tailored to the client’s operational needs. Platform support was aligned to business hours, extended hours, or 24/7 coverage depending on requirements. SLAs were defined by incident severity, with guaranteed response and resolution times in place. To give the client real-time visibility and control, we implemented automated monitoring and alerting.
Platform optimisation and future-proofing
The client was committed to building a Snowflake environment that could scale with the business. With our support, they focused on optimising performance, controlling costs, and staying ahead of future demands.
Faster delivery, greater impact
We supported ongoing initiatives by onboarding new data sources, integrating BI tools and APIs, and maintaining platform standards across internal and third-party teams. Automation and reusable pipelines cut source-to-Snowflake integration time from weeks to days.
Continuous improvement and strategic reporting
Monthly platform reports provided clear visibility into KPIs, usage trends, incidents, and optimisation opportunities. This helped the client move from reactive support to a proactive and data-driven platform management.
Governance and security practices
To support regulatory and internal compliance requirements, we implemented platform-wide governance controls. These included RBAC, data masking policies, access audits, and full alignment with SOC 2 and GDPR frameworks.
The results
Strategic value
Owning a data platform is not the goal. Making it work for the business is.
This partnership showed how Team as a Service can turn a complex platform into a strategic asset. By working directly inside the client’s operations, our certified Snowflake experts turned a complex, high-maintenance platform into a scalable foundation for growth.
Now, they are ready to take on AI and advanced analytics, backed by an architecture built to grow with the business.
At Snowstack, we don’t just help companies manage Snowflake. Our model helps enterprises stay ahead in a data environment that keeps changing
Ready to turn your Snowflake platform into a competitive advantage?
Let’s talk about how our team can help you get there
How a top global logistics leader boosted BI performance by 65% with Snowflake
One wrong move during their Snowflake migration could have brought down hundreds of BI applications and reports. With legacy systems built over 15 years and rising maintenance costs putting operations at risk, this top-5 global logistics company faced its most critical data challenge yet.
One wrong move during their Snowflake migration could have brought down hundreds of BI applications and reports. With legacy systems built over 15 years and rising maintenance costs putting operations at risk, this top-5 global logistics company faced its most critical data challenge yet.
Our experts at Snowstack stepped in to navigate this complex transformation. The outcome? A smooth migration that turned the company’s greatest risk into a long-term competitive advantage.
Key outcomes:
- Report performance improved by 65%, with dashboards running in minutes instead of hours.
- Infrastructure costs fell by 40% while system performance increased.
- The migration achieved zero disruption, maintaining 100% uptime.
- Over 65% of legacy SQL was converted automatically, saving months of effort.
- More than 40 developers were trained and upskilled on Snowflake.
Over the years, our BI teams developed an effective approach to data modeling, which had long been a strength. However, with the ongoing migration of the central data warehouse to Snowflake, we knew that adopting new tools could take months, if not years. We urgently needed support from Snowflake professionals to guide the adoption process and help our BI teams incorporate the new technology into their workflows. - Lead Data Architect
Client overview
The client operates as one of the top 5 key players in the industry, managing supply chains that span multiple continents and serve millions of customers worldwide. Their data ecosystem had evolved organically, supporting hundreds of BI applications that power everything from real-time shipment tracking to route optimization algorithms.
The client’s BI reports weren't just internal dashboard. They powered customer-facing systems that enterprise clients used to track shipments worth millions of dollars. Any disruption to these systems could trigger contract penalties and damage relationships with major accounts.
The challenge
15 years of business growth had created a BI environment that was difficult to manage. Hundreds of reports were built independently by different teams with varying skill levels. Although they all drew from the same data warehouse, each team applied its own transformation logic within separate BI systems. What began as team-specific solutions had grown into a web of technical debt that no one fully understood.
Our solution
Recognizing the critical need for modernization, the client made the strategic decision to unify their data model and move it to Snowflake alongside their ongoing data warehouse migration. We guided the client through five steps.
Step 1: identifying the foundation
Together with the client, we analysed their extensive BI landscape to identify the datasets most frequently used across reports. This joint assessment defined a minimum viable product (MVP) scope that would deliver immediate value and build momentum for the broader transformation.
Step 2: building the Snowflake environment
We worked with the client to establish a dedicated Snowflake environment designed specifically for BI collaboration. Together, we implemented:
- Standardized schemas and roles to ensure consistent data access patterns across teams
- Compute scaling strategies optimized for BI workloads
- Role-based access control (RBAC) to strengthen governance
- BI-specific access patterns tailored to Snowflake datasets
Step 3: automating the migration process
To accelerate the transition and protect prior investments, we partnered with the client to implement automated migration scripts that converted legacy SQL into Snowflake SQL. This achieved a 65% automatic refactor success rate, dramatically reducing manual work while preserving business logic.
Step 4: orchestrating seamless integration
In close collaboration, we designed and deployed new orchestration pipelines that synchronized Snowflake model builds with BI report refreshes. These pipelines were integrated with the client’s existing technology stack, including:
- Airflow Snowflake Operator for workflow management
- AWS SNS for notifications
- AWS S3 for data staging
- Git for version control
Step 5: investing in the team
Recognizing that technology transformation must go hand-in-hand with people transformation, we partnered with the client to deliver training for more than 40 BI developers. This knowledge transfer ensured teams could confidently work with Snowflake as their new backend, embedding long-term value into the organization.
Foundation for Future Innovation
Still running hundreds of disconnected BI reports with inconsistent data models?
Upgrading your BI architecture is no longer a matter of if. The real question is how quickly you can create a one source of truth before competitors pull so far ahead you can’t catch up. The companies winning today are those replacing broken reporting with accurate, unified data that every team can trust. Each month you delay, they improve decision accuracy and grow their market share.
We help you close that gap fast. Our Snowflake-certified experts bring years of experience and a proven approach to modern BI transformation. We can take years of messy, disconnected systems and turn them into a single, reliable analytics platform in months. With one source of truth in place, your teams spend less time fixing reports, more time acting on accurate information, and deliver faster business decisions.
Ready to unify your BI architecture on Snowflake?
The expert-led delivery framework
No big bang, no black boxes
You never wait months with nothing to show. Instead of one risky launch work is delivered in steady steps you can test and trust.
Progress you can follow
You don’t get vague updates or empty promises. Regular checkpoints, demos, and updates keep you in the loop.
Flexibility when priorities change
Your priorities can change. Our approach adapts without slowing down so new requirements or scope changes never stop delivery.
Commitment you can rely on
You gain a partner, not just a vendor. With solid planning and experienced teams, we keep projects aligned and accountable.
Speed with quality built in
You reach production with no shortcut. We deliver your solution to production quickly with quality and security built in.
Value that drives your business forward
Every step delivers measurable impact. We focus on what reduces cost, speeds up insight, and drives real business growth.
Designed to move fast
Whether you’re building a modern data warehouse, governed data sharing, or AI-driven use cases - our Snowflake-native accelerators eliminate months of development while embedding enterprise-grade practices.
Ingestion templates
For batch, API, and streaming data sources with error handling and monitoring, built using Airflow, AWS Glue, or Snowflake OpenFlow.
Enable AI with your data
Cortex Agents and Snowflake Intelligence applied to your data using semantic models defined by the business.
Snowpark starter kits
Python-based ML and data engineering frameworks with optimized performance patterns for Snowflake compute.
Cost guardrails
To keep usage optimized and transparent with automated alerts and warehouse scaling rules.
CI/CD deployment frameworks
For repeatable, secure platform rollouts with GitOps workflows and automated testing pipelines.
Data product blueprints
Accelerates domain-aligned architecture and business adoption with built-in governance and access controls, built using dbt.
Enterprise-grade security
Enterprise security controls and governance frameworks built into every Snowflake implementation. Role-based access, data encryption, and audit trails configured from day one.
SOC 2 TYPE I
Compliance
SOC 2 TYPE II
Compliance
What our clients say
Learnings for data leaders
The best Snowflake consulting partners in 2026
Compare the best Snowflake consulting partners in 2026. Expert ranking based on AI capability, cost optimization, and delivery maturity. Find the right Snowflake consultant for your business.
Selecting the right Snowflake consulting partner determines whether your data platform becomes a strategic advantage or an operational burden. The consulting partner you choose will shape your platform maturity, AI capability, and cost efficiency for years.
This ranking evaluates Snowflake consulting partners based on delivery capability, AI readiness, and enterprise credibility. Each firm has been assessed on its ability to deliver governed, cost-efficient Snowflake environments that support advanced analytics and machine learning workloads at scale.
Who is a Snowflake consultant?
Before we rank anyone, let's define what these people do.
A Snowflake consultant is a specialist (often a cross-functional team) that designs, implements, and operates on the Snowflake AI Data Cloud. Their work covers:
- Implementation. Clear layers for staging, integration, and presentation. RBAC that matches your teams. Orchestration across multiple warehouses.
- Integration. Openflow pipelines for batch, streaming, and unstructured data. Change data capture at the source. Solid dbt practices.
- Optimisation and FinOps. Right-sized warehouses with the Saving Calculator. Cache aware scheduling. Autosuspend and autoresume settings that fit your workloads.
- AI enablement and Data Governance. Governed Cortex use cases. Clear evaluation methods. Cost guardrails for safe scale.
- Enablement. Documentation, runbooks, and training that lower long-term consulting spend.
A credible Snowflake consultant works a simple loop: baseline → diagnose → design → prove the delta. They pull your ACCOUNT_USAGE and Query History, map spend to pipelines and users, fix anti-patterns, and prove before/after cost and performance with your telemetry.
Why Snowflake consulting partners matter in 2026
Internal teams lack the implementation experience that Snowflake consultants bring from dozens of production deployments. Poor implementation decisions made early compound over time, creating technical debt that becomes expensive to remediate. The platform's architecture demands deep knowledge of cloud data warehousing, query optimization, and cost management that most organizations do not maintain internally.
The stakes have increased significantly with AI workload requirements. Modern Snowflake services must support machine learning pipelines, large language model integrations, and retrieval-augmented generation patterns. Governance and security requirements now demand comprehensive data lineage, access controls, and audit capabilities from day one. Cost optimization expertise can reduce monthly Snowflake spending by 30 to 50 percent through proper warehouse sizing and query tuning.
A qualified Snowflake consulting partner implements these controls during initial architecture design rather than retrofitting them later. The cost differential between proactive governance and reactive compliance can reach millions in enterprise environments. Legacy migration experience prevents data loss and performance degradation during cloud transitions.
What defines a top Snowflake consultant in 2026
Elite Snowflake consulting expertise requires specific technical competencies and operational maturity. The best Snowflake consultants demonstrate architecture mastery across warehouses, data sharing, and Snowpark implementations. Cost optimization capability that reduces monthly spend by 30 to 50 percent through query tuning and warehouse right-sizing separates competent from exceptional firms. AI workload implementation covering Cortex AI, ML model deployment, and vector search integration has become mandatory in 2026.
Critical capabilities that define elite Snowflake consultants:
- Governance expertise with role-based access controls, data masking, and automated lineage documentation
- Security controls implemented as standard practice that satisfy industry regulators
- Documented runbooks and escalation paths with production incident resolution within defined SLAs
- Regular optimization reviews identifying cost reduction opportunities before clients request them
The best firms demonstrate strategic judgment about when not to use certain features. They push back on unnecessary complexity and recommend simpler patterns that deliver equivalent business value. This judgment comes from extensive implementation experience across multiple client environments and industry verticals.
Snowflake consulting partner comparison table
The best Snowflake consulting partners in 2026
1. Snowstack
Snowstack operates as a Snowflake-first consulting firm delivering Platform Team as a Service. The firm compresses typical 12-month projects into 90-day engagements from discovery to production using their proprietary framework and how we deliver. Client outcomes include 30 to 50 percent cost reduction and 80 percent faster reporting cycles across pharma, financial services, and FMCG implementations.
Key differentiators:
- FinOps cost optimization and AI governance specialization with senior architects on every engagement
- Multi-petabyte data volume handling with systematic knowledge transfer to internal teams
- Rapid enterprise implementations handling discovery through production in compressed timeframes
2. Slalom
Slalom earned recognition as Snowflake's Global Data Cloud Services AI Partner of the Year 2025. The firm excels at integrating machine learning workflows into Snowflake environments and building real-time analytics dashboards with collaborative consulting methodology.
3. phData
phData maintains exclusive focus on data engineering and analytics with multiple Snowflake Partner of the Year awards including 2025 Americas recognition. The firm offers comprehensive Snowflake consulting spanning strategy, implementation, and managed services with hundreds of certified engineers.
4. Cognizant
Cognizant operates as Snowflake's Global Data Cloud Services Implementation Partner of the Year 2025. The firm brings Fortune 500 scale with proprietary Data Estate Migration toolkit for legacy warehouse transitions and global delivery capability.
5. Accenture
Accenture maintains Elite Snowflake partner status with full-service capabilities from strategy through managed operations. The firm has developed industry accelerators that reduce implementation timelines with particular strength in marketing analytics and advertising use cases.
6. Deloitte
Deloitte combines Big Four strategic advisory with technical Snowflake implementation capability. The firm's Insight Driven Organization framework aligns platform projects with business measurement systems and objectives, particularly in finance, retail, and public sector.
7. Krish Technolabs
Krish Technolabs operates as a certified Snowflake partner with expertise in AI-driven analytics and multi-cloud deployments. The firm delivers comprehensive Snowflake services with focus on predictive modeling and LLM-powered insights for enterprise datasets.
8. Wipro
Wipro operates as an Elite Snowflake partner with a dedicated Center of Excellence supporting over 100 client implementations. The firm brings strong execution capability with global delivery scale for complex enterprise deployments in banking, consumer goods, and manufacturing.
The questions you must ask before signing a Snowflake consultant
Technical Competence:
- "Walk me through your Snowflake implementation methodology"
- "Can you show me sanitized architecture diagrams from similar projects?"
- "What's your approach to FinOps and cost optimization?"
Delivery Model:
- "Who will actually be on my project team day-to-day?"
- "What's your knowledge transfer approach?"
Pricing & Scope:
- "What's included vs. out of scope?"
- "What happens if the project runs over budget?"
Why choose Snowstack for high-impact Snowflake consulting in 2026
Choosing the right Snowflake partner is not easy. When you’re comparing Snowflake partners, it helps to talk to someone who isn’t trying to sell you a 12-month transformation on day one. If you’d like a second opinion on your shortlist, your current proposals, or whether you should even bring in a GSI vs a specialist, let’s chat. At Snowstack, we combine deep Snowflake expertise with proven delivery methods, transparent team structures, and a focus on long-term governance and optimization. Our Snowflake experts deliver production-ready environments in 90 days while larger consultancies require 12 to 18 months for equivalent capability.
Second is cost optimization delivered as core methodology rather than optional add-on. Every Snowstack engagement includes FinOps analysis that identifies 30 to 50 percent spending reduction through warehouse right-sizing, query optimization, and automated scaling policies. Most Snowflake consultants treat cost management as afterthought, creating expensive platforms that require subsequent optimization projects.
Third is AI readiness embedded in architecture from day one. Snowstack implementations support Cortex AI integration, vector search capabilities, and machine learning pipeline deployment without requiring platform redesign. Firms focused on legacy data warehouse patterns deliver environments that need expensive rework when organizations advance AI initiatives.
The Platform Team as a Service model provides ongoing senior architect access rather than transitioning to junior support resources post-implementation. This continuity ensures optimization opportunities get identified and implemented proactively. Industries with strict governance requirements including pharma and financial services benefit from Snowstack's compliance framework expertise built into initial architecture rather than retrofitted later.
Contact us to discuss your specific requirements!
Understanding Snowflake: 7 core capabilities that set it apart from legacy databases in 2025
Most enterprise databases were built for monthly reports, not AI products that need fresh, reliable data every hour. This guide breaks down 7 core Snowflake capabilities, explains how they solve the typical Oracle, Teradata, SQL Server and on premises PostgreSQL or MySQL limitations, and shows what they mean for your teams in real projects.
Let's be honest. Your current database was most likely built for monthly reports, not AI products that demand regular updates and reports all the time. This is the reason why, in 2025, really innovative and data-driven businesses continue their migration away from legacy databases like Oracle, Teradata, SQL Server, and on-premises MySQL/PostgreSQL toward modern cloud-native architectures. Snowflake has become the industry leader, powering analytics and AI workloads across finance, retail, technology, and enterprise sectors.
This guide breaks down 7 core Snowflake capabilities and shows how the right Snowflake consulting can turn them into best results for your teams.
What is the legacy database challenge?
Before diving into Snowflake's capabilities, it's crucial to understand the limitations organisations face with traditional databases. Therefore, let’s consider the scenario of a global FMCG company operating in multiple regions, where we helped transform the data infrastructure from legacy on-prem systems to
With our expert Snowflake migration services, the company moved to Snowflake + dbt + Fivetran + Tableau as a modern data stack.
Talk to our Snowflake consultant →
The 7 core Snowflake capabilities in 2025
1. Multi-cluster shared data architecture
The fundamental differentiator: Snowflake's three-layer architecture completely separates storage from compute resources.
Key benefits:
- Unlimited concurrency
- Auto-scaling virtual warehouses
- Near-zero locking and contention
- Pay-as-you-use compute
This means analysts, data scientists, and applications can work in parallel on the same datasets without contention.
Business impact:
You no longer have to buy extra storage just to get more compute. You scale up when you need power, scale down when you don’t, and you can see what that means for your bill in minutes with our FinOps savings calculator
2. Cross-cloud & multi-region replication
This Snowflake capability is critical for regulated industries (financial services, healthcare, insurance) and companies with international operations requiring data sovereignty compliance.
Snowflake delivers:
- Multi-cloud availability on AWS, Azure, and Google Cloud Platform
- Easy cross-region replication and failover
- Global application distribution
- Built-in disaster recovery without complex configuration
Plan residency, failover, and recovery during **platform architecture,** then implement Snowflake like a pro.
Business impact:
A global FMCG company can maintain synchronised data across North American, European, and Asian markets while meeting local data residency requirements. This is difficult to achieve with legacy on-premises databases.
3. Zero-copy cloning & time travel
Snowflake's innovative approach to data management enables instant environment creation with zero additional storage costs.
Game-changing features:
- Clone terabyte-scale databases in seconds without duplicating data
- Time Travel for historical queries and point-in-time recovery
- Safe dev/test environment provisioning without impacting production
Development teams can spin up complete production-like environments instantly for testing, while legacy databases require duplicated environments that consume massive storage and take hours or days to provision.
Business impact:
Data engineers can test complex transformations on production-scale data without risk, dramatically accelerating development cycles and improving data reliability.
4. Built-in governance & RBAC security
In 2025, data governance and security are business-critical requirements for compliance and risk management.
Snowflake's security framework includes:
- Fine-grained access control with row-level and column-level masking
- Data lineage and classification for understanding data provenance
- Policy-based access control with external tokenisation partner support
- Automatic encryption at rest and in transit
- Dynamic data masking to protect sensitive information
- Audit logging and monitoring for compliance reporting
These are essential for organisations operating under SOC 2, HIPAA, GDPR, PCI DSS.
5. Native AI & Python ecosystem
Snowflake has built-in support for Python and machine learning, so your team can build and run models where the data already lives instead of exporting them elsewhere. With solid AI and data governance in place, it becomes easier to try new ideas safely and move them into production. The key building blocks are:
Business impact:
This means that teams can train, deploy & serve ML models securely inside Snowflake. Data scientists spend less time on data engineering and infrastructure management and more time building models that drive business value.
6. Marketplace & data sharing economy
The Snowflake Marketplace reshapes how enterprises access 3rd-party data (functioning as the "App Store for data"). We are looking at:
- Thousands of data providers covering financial data, geospatial information, retail insights, weather patterns, ESG metrics, and logistics intelligence
- Live data feeds without pipelines (No ETL required)
- Private data exchange across subsidiaries, partners, and customers
Business impact:
You can now achieve faster analytics, better forecasting, and smarter decisions by instantly accessing external data sources that would traditionally require weeks of negotiation, integration work, and ongoing pipeline maintenance.
7. Extensibility: unistore & native apps
Snowflake is no longer just a data warehouse. In 2025, it can also handle simple day-to-day transactions and apps that run directly on your data.
Next-generation capabilities:
- Unistore for OLTP-lite workloads, enabling hybrid transactional/analytical processing
- Snowflake Native Apps for custom application development
- Streamlit integration for building interactive data applications
- Real-time data pipelines via Kafka connectors and Snowpipe Streaming
Business impact:
Snowflake serves hybrid workloads that legacy databases struggle to handle without significant operational complexity. Organisations consolidate their data infrastructure rather than maintaining separate systems for transactional and analytical workloads.
Real-world example: Snowflake consulting & migration results
Here’s what the shift looks like in practice. In a recent Snowflake project with a global FMCG company, we rebuilt the analytics backbone by establishing a governed core data model, automating ingestion and orchestration with native services and partner connectors, and reconnecting BI directly to a single, auditable source of truth. As seen in the table below, the result was a step-change in reliability and speed.
Documented results from migration to Snowflake:
Beyond the database
Snowflake’s strengths include a unique design, flexible scaling, strong access and security controls, built-in AI features, and safe sharing across regions, which make it more than a database. It is a modern cloud data platform that powers predictive analytics, self-service reporting so product teams can trust the data and use it with ease. In business, the faster you get answers, the stronger your advantage, and Snowflake is setting the standard for company data platforms.
If you are choosing a data platform in 2025, plan for what you will need next year as well as today. Snowflake’s design is built for an AI-ready cloud-based future. We help you make that future real by setting up Snowflake, connecting your data, putting clear access rules in place, and keeping costs under control with a simple 90-day plan that we build with your team.
Ready to turn Snowflake into results?
Book a 30 minute call with our Snowflake consultant →
Can Snowflake store unstructured data? How Snowflake handles documents, images, and other data in 2025
Snowflake isn’t just rows and columns anymore. In 2025 you can land PDFs, images, logs, and app data next to your tables, then query, enrich, and search them with SQL, Snowpark, and Cortex AI.
What if your PDFs, transcripts, and logs could live in the same place as your BI dashboards? For years, Snowflake was known primarily as a cloud native data warehouse built for structured analytics. It was the go-to solution for SQL analysts, BI teams, and data engineers working with neat rows and columns. Meanwhile, many teams dealing with documents, images, logs, and raw application data assumed they needed separate storage such as Amazon S3, Google Cloud Storage, Azure Blob, or NoSQL databases.
In 2025, that separation no longer has to exist. Snowflake is now a multimodal data platform that can store, process and query unstructured data.
So yes, Snowflake can store unstructured data, but more importantly, it can use it. This capability offers significant architectural advantages for modern data teams. In this blog post, we’ll break down exactly how and why it matters.
What is unstructured data?
Unstructured data refers to any information that doesn't fit neatly into traditional rows and columns. This includes:
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- Documents: PDF, DOCX, TXT files
- Images: PNG, JPG, TIFF formats
- Audio and video files: Media content and recordings
- Logs and event data: Application and system logs
- Communication data: Email threads and chat transcripts
- Markup and structured text: HTML, XML, JSON blobs
- Binary files: Application-specific file formats
As organisations increasingly generate massive volumes of this data, the need for unified platforms that can both store and analyse unstructured content has become critical.
How Snowflake stores unstructured data?
Snowflake stages for unstructured data
Snowflake manages unstructured data through stages. This means through storage locations that reference files either within Snowflake's managed infrastructure or in external cloud storage:
- Internal Stages: Files are stored within Snowflake's managed storage, offering quick setup and seamless integration
- External Stages: Files remain in external cloud locations (Amazon S3, Azure Blob Storage, Google Cloud Storage), with Snowflake accessing them via metadata references
You can also combine both approaches for optimal performance and scalability based on your specific requirements.
The FILE data type in Snowflake for unstructured files and metadata
Snowflake provides a dedicated FILE data type for unstructured data. A FILE value represents a reference to a file stored in an internal or external stage, without storing the actual file content in the table itself. This approach allows:
- Efficient storage and cost management
- Fast metadata querying
- Seamless integration with processing pipelines
Accessing unstructured files in Snowflake
Snowflake provides familiar commands for file management:
- PUT: Upload files to stages
- GET: Download files from stages
- LIST: View files stored in stages
These operations mirror cloud storage interactions while maintaining Snowflake's security and governance standards.
Processing and querying unstructured data in Snowflake
Storage is just the beginning. Snowflake's real power lies in its ability to process and extract insights from unstructured data.
Snowflake Cortex AI and Document AI for PDFs, images and hybrid search
Cortex AI enables advanced analytics on unstructured data directly within Snowflake:
- Document analysis: Extract text, summarise content, and perform batch LLM inference on PDFs and documents
- Image processing: Run classification and analysis on stored images
- Multimodal SQL functions: Query and transform documents, images, and audio using SQL-powered pipelines
- Schema-aware extraction: Automatically extract structured tables from unstructured documents like invoices and reports
Snowpark for custom processing
With Snowpark, you can:
- Extract text from PDFs using Python
- Perform image classification with embedded ML models
- Parse JSON or log files into VARIANT columns
- Run OCR, NLP, and generate embeddings via external functions
- Build semantic search capabilities over document collections
VARIANT data type for semi-structured data
The VARIANT data type handles semi-structured data formats like JSON, XML, Parquet, and Avro:
- Store complex, nested data structures
- Query JSON fields directly using SQL
- Maintain schema flexibility while preserving query performance
Why unified data architecture matters?
In most companies, data still lives in many places and tools. Dashboards sit on a legacy SQL warehouse, logs go to a separate observability stack, and documents and images disappear into unmanaged cloud buckets or shared drives.
Instead of stitching together a dozen point solutions, you can use Snowflake as the backbone of your data architecture and keep external systems only where they add unique value. The table below shows how data stack functions shift when you standardise on Snowflake in 2025:
Real-world use cases of handling unstructured data in Snowflake
Here is how this looks in practice. Below is our recent project, plus common patterns we see when teams bring documents, images, logs, and app data into Snowflake and put them to work.
Global finance, AI-ready in 90 days
A multinational finance firm spending more than 800K per month on cloud was battling rising costs and fragmented data. They needed a governed place for documents, logs, and tables. We used OpenFlow to ingest both structured and unstructured data into Snowflake, tracked lineage and policies in Horizon Catalog, set consistent business logic with semantic views, and enabled natural language querying through Cortex AI SQL. The result was about an 80% reduction in ingestion latency, real-time cost visibility with FinOps, and a platform ready for analytics, ML, and AI at scale.
Read how a global finance managed unstructured data in Snowflake →
Limitations and considerations of Snowflake
Snowflake’s unstructured data capabilities are strong, but it won’t fully replace your data lake or media platform. For B2B teams planning at scale, keep these practical constraints in mind:
- Not a pure object storage replacement: Snowflake complements rather than replaces S3/GCS for massive-scale raw object storage
- File retrieval performance: Binary object retrieval speed varies by file size and stage type
- Compute costs: AI and ML workloads require careful resource management
- Specialised use cases: For intensive video/audio editing, use specialised systems.
Best practices for managing unstructured data in Snowflake in 2025
1. Keep big binaries in external object storage, keep brains in Snowflake
Register S3, Blob, or GCS as external stages and reference files via the FILE type; keep only hot assets in internal stages for speed.
2. Standardize file layout and formats from day one
Use predictable paths (org/source/system/YYYY/MM/DD/id) and checksums; prefer compressed columnar formats like Parquet, with extracted text or page JSON beside PDFs and images.
3. Store metadata and embeddings in Snowflake, not in files
Put raw files in stages, but keep metadata, chunks, and embeddings in Snowflake tables linked by stable URIs for fast search and governance. Use directory tables to catalog staged files.
4. Orchestrate ingest → extract → enrich → index → serve with Snowpark
Run OCR, NLP, and parsers as Snowpark tasks and UDFs; batch, log runs, and make jobs idempotent so reruns are safe. Implementation flow in processing files with Snowpark.
5. Treat AI as a costed product
Separate warehouses for ELT and AI, strict auto-suspend, resource monitors, caching, and reuse of embeddings and summaries. Get a baseline with the FinOps savings calculator.
6. Govern at the row, column, and file edge
Classify on arrival, enforce row and column policies with masking, and keep least-privilege stage access and full lineage. For role design patterns, see Snowflake role hierarchy best practices.
Need a hand?
Our snowflake experts at Snowstack can audit your current setup, design a lean reference architecture, and prove value with a focused pilot. Read how we deliver in How we work or talk to a Snowflake expert.
Talk with a Snowflake consultant→
Final thoughts
Snowflake doesn’t just store unstructured data; it makes it usable for search, analytics, and AI. With stages, the FILE data type, VARIANT, Snowpark, and Cortex, you can land documents, images, and logs alongside your tables, extract text and entities, generate embeddings, and govern everything under a single security and policy model. The winning pattern is simple: keep raw binaries in low-cost object storage, centralise metadata and embeddings in Snowflake, and start with one focused, high-value use case you can scale.
Ready to try this in your stack?
Book a 30-minute call with our Snowflake consultant →
