Snowflake Consulting & Development Services
Snowflake Consulting and Production-Grade Lakehouse Solutions
Handling massive data is tough for mature companies and regulated industries. STX Next, an official Snowflake partner, addresses these challenges by building cloud-native lakehouse platforms that unify data management, simplify integration, and make real-time analytics available without the operational overhead of separate warehouse and lake infrastructure.
Our approach to the Snowflake Lakehouse
Snowflake is a fully managed platform that combines the performance of a data warehouse with the flexibility of a data lake. By separating storage from compute, it allows ingestion, transformation, and ML workloads to run in isolation, preventing resource competition.
- Multimodal Data: Handles structured, semi-structured, and unstructured data in one system.
- Open Standards: Supports Apache Iceberg to minimize vendor lock-in while maintaining a managed experience.
- AI-Native: Built-in LLM functions via Cortex and Python-based ML through Snowpark.
- Cloud Agnostic: Full functionality across AWS, Azure, and GCP.
Snowflake Lakehouse Architecture
A production-grade Snowflake environment relies on a multi-layered stack designed for performance and governance. We typically structure our engagements around these core components:
Why Choose This Stack?
- Lower Operational Burden: Snowflake manages scaling, metadata, and security, allowing your team to focus on data value rather than cluster tuning.
- True Workload Isolation: Separate compute resources ensure that a heavy ML job never slows down a board-level BI dashboard.
- Cost Control: Auto-suspend settings and multi-cluster scaling mean you only pay for active compute. We provide monthly monitoring to prevent budget overruns.
- Secure Sharing: Share governed datasets with partners or subsidiaries instantly without copying data or managing APIs.
Snowflake limitations (and how we mitigate them)
While Snowflake is a market leader, each technology has its limitations. We address specific platform constraints through strategic architectural choices.
- Storage Cost Optimization: Snowflake-managed storage carries a markup over raw object storage. For high-volume, less frequently accessed data, we use Apache Iceberg tables to maintain open-format flexibility and lower costs.
- External Catalog Management: Open Iceberg tables reduce vendor lock-in but increase the complexity of metadata management. We configure specialized catalogs (like Polaris) to ensure data remains discoverable across different engines.
- Pipeline Orchestration: Snowflake’s native task scheduling is basic. To ensure reliable, complex workflows, we integrate dbt or Apache Airflow, providing better visibility and error handling for production pipelines.
Replatforming: from Snowflake, Databricks or AWS into Fabric
Running Snowflake, Databricks, Redshift, or Glue? We help you evaluate, plan, and execute a strategic move to Fabric; or architect a hybrid that makes financial sense.
Not every replatforming is a full rip-and-replace, and we don't pretend it is. Some workloads migrate cleanly to Fabric. Others are better served by Fabric's Mirroring and Shortcut capabilities that let you query external data in place. And some may stay where they are, for now.
Our cross-platform replatforming accelerator starts with an honest assessment: we map your existing workloads (compute patterns, data volumes, query profiles, licensing costs), identify what moves, what mirrors, and what stays, then build a phased transition plan that protects your operations while consolidating where it makes financial and architectural sense.
What the replatforming accelerator covers:
- Workload discovery and cost analysis across Snowflake, Databricks, Redshift, Glue, S3, and/or other sources
- Data architecture mapping: which datasets migrate, which get mirrored via OneLake Shortcuts, which stay external
- SQL dialect translation tooling (Snowflake SQL / SparkSQL / Redshift SQL → Fabric T-SQL / Spark)
- Pipeline re-engineering: Airflow, dbt, Glue, or Databricks workflows → Fabric Data Factory + Notebooks
- OneLake Shortcut configuration for AWS S3 and GCS sources that remain external
- BI layer migration: Tableau/Looker → Power BI conversion assessment and semantic model design
- TCO comparison model: current multi-vendor costs vs. projected Fabric capacity spend
- Terraform-managed target environment with governance and CI/CD from day one
How STX Next adds value as an official Snowflake partner
We help businesses implement Snowflake as a production-grade data lakehouse, shaped around actual data landscapes, compliance requirements, and team structures. Whether the challenge is consolidating data from 100+ sources, replacing overnight batch jobs with real-time pipelines, or standing up a governed self-service analytics layer, we bring the engineering depth to get it done and the domain knowledge to make it useful.
STX Next assist in making data actionable, by:
- Automating pipelines
- Embedding data validation
- Optimizing analytics
- Gaining real-time insights all in a cloud-native way
Why choose us?
Scalable Cloud-Native Solutions
Seamlessly integrate Snowflake with AWS, Azure, or GCP. As your data needs grow, Snowflake scales automatically, ensuring high performance without disruptions while we optimize your overall cloud speed.
AI-Driven Insights
We tailor Snowflake's AI and visualization features to get real-time data insights, helping your teams make quicker and more informed decisions.
Team Efficiency Enhancement
We drive your AI transformation with centralized data and ML tools, allowing your teams to easily access reliable and augmented information, leading to smarter decisions and increased productivity.
Our Snowflake Lakehouse services
Lakehouse Architecture & Platform Design
Design starts with storage configuration (Snowflake-managed or external Iceberg Tables), virtual warehouse layout for workload isolation, and role hierarchy. Where open format portability matters, we configure Iceberg Tables to avoid lock-in without adding unnecessary operational complexity.
Data Migration & Warehouse Modernization
Migration of legacy warehouses, on-premise databases, and fragmented stores includes schema mapping, historical data loading, and row-level validation. Snowflake's zero-copy cloning provides isolated dev, test, and production environments without duplicating storage costs. Most initial migrations complete within 4-6 weeks with automated incremental loads.
Real-Time & Batch Ingestion Pipelines
Pipeline construction matched to specific latency requirements: Snowpipe Streaming for near-real-time data, COPY INTO for batch files, and Kafka connectors for high-throughput streams. Every pipeline includes schema evolution handling, latency monitoring, and data quality checks before data reaches the Silver layer.
dbt Transformation & Data Quality
Development of dbt models covers Bronze-to-Gold Medallion transformation logic and dimensional modeling for BI. Every model ships with schema tests, source freshness checks, and CI/CD deployment via GitHub Actions or dbt Cloud. This allows teams to version models and add data quality logic without risking production pipelines.
Governance, Compliance & Security Configuration
Configuration of the full governance stack includes role hierarchies, dynamic column masking for PII, row-level security, and object tagging. For financial services and healthcare, this includes GDPR and HIPAA-compatible setups and an audit trail documentation package ready for external auditors.
Snowpark ML & Cortex AI Integration
Extension of the lakehouse into ML and AI occurs without a separate model serving layer. Custom ML models are built and registered using Snowpark Python to run directly on Snowflake data. For text classification and semantic search, we integrate Cortex LLM functions to enable real-time scoring without data movement or external API latency.
Expertise Built On 100+ Data Engineering Projects
Partnering with us, our clients have cut incident response times from days to minutes, consolidated thousands of redundant dashboards into focused reporting, and built systems that could never have run on their previous infrastructure.
AI-Powered Threat Management
A global cybersecurity leader centralized 10TB of data across over 50 sources using Snowflake integrated by STX Next, enabling faster threat detection, streamlined data sharing, real-time insights, and a more resilient security strategy.
Streamlining Insurance Data
STX Next assisted a UK insurer in migrating millions of records to Snowflake, modernizing their data infrastructure. With automated pipelines and transformed analytics, latency was reduced, enabling zero-latency data insights.
Enhancing ePharmacy Data Efficiency
For a US-based ePharmacy, STX Next migrated their data platform to Snowflake, creating a unified data model with dbt integration for consistent insights. This resulted in scalable, efficient data management and enhanced business analysis.
Which businesses will benefit most from Snowflake?
Large Enterprises
Organizations needing to consolidate data across multiple regions and business units into a single governed layer.
Regulated Industries
Healthcare and Financial Services requiring strict access control, masking, and compliance-ready audit trails.
Data-Driven Teams
Scientists and engineers who need clean, ML-ready data and support for both SQL and Python in a single environment.
Technical Leaders
CTOs and CIOs looking to consolidate separate warehouse, ML, and governance tools onto one platform with predictable costs.
How we work
Discovery & Assessment
Mapping data sources, ingestion patterns, and transformation logic. You receive a written assessment of current-state architecture and a prioritized implementation scope.
Architecture & Cost Modeling
Design of the target lakehouse, role hierarchy, and cloud deployment. You receive a cost model based on actual data volumes and a phased project plan.
Proof of Concept
Building a working environment with a representative slice of data to validate query performance, ingestion latency, and cost projections before full commitment.
Full Implementation
Execution in two-week Scrum sprints. This covers full migration, ingestion pipelines, dbt models with CI/CD, governance setup, and BI connector integration.
Handoff & Optimization
Delivery of runbooks, model documentation, and onboarding sessions. Optional retained support includes monthly cost optimization and architecture guidance.
Copilot Studio Development built for your Industry
At STX Next, we support your business with solutions built on Copilot Studio & Microsoft Foundry for your specific use cases, easing complex challenges across diverse industries and boosting efficiency. Explore some sectors from our portfolio:
Finance
The world of finance demands precision and foresight. Our AI solutions help fintech companies stay ahead and secure.
Proactive risk management
Employ machine learning to identify and mitigate potential risks before they impact your bottom line.
Customer insights at your fingertips
Use AI-driven analytics to accurately score customers and tailor financial products to their needs.
Efficient compliance
Automate classification tasks like email sorting to save time and ensure regulatory adherence.
See our finance solutions
Education
Redefining learning with AI needs personalization and engagement. We help educational institutions create immersive experiences.
Crafting engaging content
Use generative AI to develop interactive learning materials that capture student interest and adapt to diverse learning styles.
Personalized learning paths
Implement data science techniques for tailored education plans, ensuring every student progresses at their own pace
Streamlining administrative tasks
Automate grading and other time-consuming tasks, letting educators focus more on teaching and less on paperwork.
See our education solutions
Marketing
Redefining learning with AI needs personalization and engagement. We help educational institutions create immersive experiences.
Smart content creation
Generate engaging ad content using generative AI, ensuring it resonates with your target audience.
Optimizing customer journey
Leverage predictive analytics to enhance user acquisition, retention, and conversion strategies.
Campaign performance insights
Use data analytics to understand campaign outcomes better and tailor future marketing efforts.
See our marketing solutions
Healthcare
Healthcare must be precise, proactive, and patient-centric. Our AI solutions bring about smarter healthcare systems.
Improved diagnostics
Enhance medical diagnoses with AI technology, offering real-time insights and support for clinicians.
Tailored treatments
Utilize analytics to recommend personalized medications and treatment routes, ensuring optimal patient care.
Advancing research
Support drug discovery and research efforts through powerful AI-driven analytics and data interpretation.
eCommerce
Retail thrives on customer experience and operational efficiency. We help eCommerce businesses optimize every step of the sales journey.
Personalized shopping experiences
Enhance customer engagement with AI-powered recommendation systems tailored to their preferences.
Streamlined inventory management
Utilize data analytics to maintain accurate stock levels and anticipate demand changes.
Customer support redefined
Implement chatbots to offer immediate assistance and improve the overall shopping experience.
See our ecommerce solutions
Logistics
Efficiency is everything in logistics. Our AI solutions streamline operations and reduce impacts.
Resource optimization
Use intelligent AI tools for better supply chain and resource management, ensuring deliveries stay on schedule.
Risk management
Deploy AI systems to anticipate and mitigate logistical risks before they throw a wrench in your plans.
Eco-friendly operations
Incorporate AI analytics to reduce environmental footprints and foster sustainability in logistics operations.
See our logistics solutions
Let's talk
Schedule a chat with Head of Data Engineering and one of our senior engineers to discuss your Snowflake needs.
FAQ
What does Snowflake implementation look like with STX Next?
STX Next handles the full Snowflake data engineering process, from initial workspace setup and cloud integration (AWS, Azure, or GCP) to building custom ingestion pipelines, embedding data validation, and optimizing analytics. The process is tailored to each client's existing infrastructure and data volumes, with engagements running from a 1-2 week discovery through to full implementation over 6-16 weeks.
Is STX Next a certified Snowflake partner?
Yes. STX Next is a certified Snowflake Select Services Partner, with hands-on delivery experience across financial services, cybersecurity, healthcare, and e-commerce sectors.
Does STX Next support Snowflake Lakehouse architecture?
Yes. STX Next builds and migrates clients to a Snowflake Lakehouse setup, combining structured and semi-structured data in a single unified platform. This removes the need for separate data warehouses and data lakes, reducing both cost and complexity.
Can STX Next help with Snowflake dbt implementation?
Yes. dbt is central to how we build transformation layers on Snowflake. We develop dbt models covering Bronze-to-Gold Medallion logic, dimensional modeling for BI, and schema tests with CI/CD deployment via GitHub Actions or dbt Cloud. The Snowflake dbt combination gives teams a SQL-native, version-controlled transformation layer that analysts and engineers can maintain without risk to production pipelines.
Which industries benefit most from Snowflake consulting services?
STX Next's Snowflake consulting is particularly suited to regulated and data-heavy industries. For Snowflake financial services engagements, we configure role hierarchies, dynamic data masking, and audit trail documentation ready for external review. For Snowflake healthcare clients, we implement HIPAA-compatible governance setups including row-level security and PII masking policies. We also have delivery experience across cybersecurity and e-commerce.
How does Snowflake cost optimization work?
Snowflake cost optimization starts with right-sizing virtual warehouses and setting auto-suspend policies so compute is never running idle. STX Next goes further by consolidating redundant pipelines, automating incremental loads to reduce full-table scans, and using Apache Iceberg tables for high-volume, infrequently accessed data where Snowflake-managed storage carries a cost premium. We provide monthly monitoring as part of our retained support to prevent budget overruns before they appear on your invoice.
What is included in Snowflake managed services from STX Next?
Our Snowflake managed services cover ongoing cost monitoring and optimization, pipeline reliability checks, architecture guidance as your data volumes grow, and incident response for production issues. It is structured as a retained monthly engagement following the initial implementation, with a defined scope and response SLA agreed upfront.
What does Snowflake data governance configuration include?
Snowflake data governance covers the full access control stack: role hierarchies, dynamic column masking for PII fields, row-level security policies, and object tagging for lineage and classification. For regulated clients, we also configure audit trail exports and produce documentation packages ready for external auditors, covering GDPR and HIPAA requirements.
Can STX Next provide Snowflake consultants or developers for our team?
Yes. In addition to full implementation engagements, we offer team augmentation with certified Snowflake consultants who embed directly with your team. Whether you need to hire Snowflake developers for a specific build phase or want ongoing architecture support from a senior consultant, we can structure the engagement around your timeline and internal capacity.
