Data Warehouse Design & Migration
Snowflake's cloud-native architecture separates compute from storage, enabling virtually unlimited scalability and true elasticity. Nextbrick's Snowflake consultants help organizations design warehouse architectures that take full advantage of this model—implementing multi-cluster virtual warehouses, resource monitors, and workload-specific compute pools that optimize both performance and cost. Whether you are migrating from Teradata, Oracle, Redshift, or BigQuery, our proven migration methodology covers schema conversion, data transfer, query translation, and performance validation to ensure a seamless transition.
We establish governance frameworks using Snowflake's role-based access control, row-level security, and dynamic data masking so that sensitive data is protected from day one. Our architecture blueprints include database and schema naming conventions, tagging strategies, and data classification policies that keep your Snowflake environment organized and auditable as it scales.
Data Engineering & ELT Pipelines
Nextbrick builds modern ELT pipelines that land raw data into Snowflake and transform it using SQL-based frameworks like dbt, Matillion, or Snowflake's native Snowpark. We design incremental loading patterns with Snowpipe and streams/tasks that keep your analytics layer fresh without the overhead of full table refreshes. Our engineers implement merge logic, slowly changing dimensions, and data vault modeling to support both operational reporting and historical analysis.
For organizations adopting the medallion architecture pattern, we structure bronze, silver, and gold layers within Snowflake that provide clear data lineage and quality gates. We integrate data quality testing with tools like dbt tests, Great Expectations, and Monte Carlo to catch anomalies before they reach dashboards and reports.
Snowpark & Application Development
Snowpark extends Snowflake's capabilities beyond SQL, allowing engineers and data scientists to write Python, Java, or Scala code that executes directly within Snowflake's compute engine. Nextbrick helps teams build Snowpark-based feature engineering pipelines, ML training workflows, and UDFs that eliminate the need to move data out of Snowflake for processing. We develop Snowflake Native Apps that package analytics and data products for distribution through the Snowflake Marketplace.
Our consultants also leverage Streamlit in Snowflake to build interactive data applications that sit on top of governed datasets, giving business users self-service access to insights without the complexity of traditional BI tools.
Data Sharing & Collaboration
One of Snowflake's most powerful differentiators is secure data sharing. Nextbrick helps enterprises set up direct shares, listings on the Snowflake Marketplace, and data clean rooms that enable collaboration with partners, customers, and vendors without copying or moving data. We design sharing architectures that maintain governance—applying row-level policies, column masking, and usage tracking so you retain full control over shared assets.
For multi-cloud organizations, we configure Snowflake's cross-cloud replication and data sharing capabilities to provide a unified data experience regardless of whether workloads run on AWS, Azure, or GCP.
Cost Management & Performance Optimization
Snowflake's consumption-based pricing model rewards efficient design. Nextbrick conducts detailed spend analyses using Snowflake's Account Usage views, identifying warehouse over-provisioning, query anti-patterns, and unnecessary data scanning. We implement auto-suspend and auto-resume policies, query acceleration services, and search optimization to drive down credit consumption while maintaining or improving query performance.
Our ongoing optimization engagements include monthly cost reviews, workload profiling, and warehouse rightsizing recommendations that keep your Snowflake investment aligned with business value.