Snowflake
Snowflake architecture, migration, data engineering and ongoing support for an enterprise data platform.
Discuss your platform- Source systemsERPApplicationsFiles and events
- On the platformIngestionbatch and continuousTransformationdynamic tables, SnowparkGoverned tablesstorage
- ConsumptionBI and analyticsCortex AIApplications
- Virtual warehousescompute, sized per workload and separate from storage
- Horizon Cataloglineage, discovery and quality monitoring
A governed data platform your analytics and AI can rely on.
Snowflake separates storage from compute, which changes how an enterprise data estate is designed, governed and paid for. Sreehith offers advisory, implementation, migration and support across the platform, connecting it to the ERP and cloud systems where business data originates. Product capabilities described below reflect Snowflake’s published documentation.
Explore our expertiseWorking across Snowflake.
Specialist capabilities across the applications, processes and requirements of your platform.
Advisory and architecture
Establish what the platform needs to do before building it. Assess the current estate, define a target architecture across databases, schemas and virtual warehouses, and set out a roadmap with a clear sequence rather than a single large migration.
- Platform assessment and current-state review
- Target solution architecture
- Data platform strategy and operating model
- Phased implementation roadmap
Implementation
Configure the platform so that structure, access and compute are deliberate from the outset. Account and warehouse setup, data modelling, and the integration points that connect Snowflake to source systems.
- Account, database and virtual warehouse configuration
- Data warehouse and data mart implementation
- Dimensional and analytics-ready data modelling
- Source system and application integration
- Workload deployment and environment promotion
Migration and modernisation
Move existing warehouses and transformation logic onto the platform without losing the business rules embedded in them. Legacy ETL is often the harder half of a migration, so it is assessed and reworked explicitly rather than lifted.
- Migration assessment and workload inventory
- Data warehouse migration
- ETL to ELT modernisation
- Reconciliation, migration testing and validation
- Cutover planning and parallel running
Data engineering
Build the pipelines that keep the platform current and trustworthy. Snowflake’s dynamic tables let a pipeline be declared as a query, with the platform handling refresh timing, dependency ordering and incremental processing, which removes a large amount of orchestration code.
- Batch and continuous ingestion pipelines
- Transformation with dynamic tables and Snowpark
- Data quality rules and monitoring
- Orchestration, scheduling and automation
- Apache Iceberg and open table format interoperability
Performance and cost optimisation
On a consumption platform, performance and cost are the same conversation. Right-size warehouses against real workload patterns, remove the query and modelling problems that inflate compute, and give finance a credible view of spend.
- Query performance analysis and tuning
- Virtual warehouse sizing and auto-suspend policy
- Workload isolation and concurrency management
- Resource monitors and consumption alerting
- Cost visibility and chargeback reporting
Security and governance
Govern access and lineage as part of the build, not afterwards. Snowflake Horizon Catalog provides discovery, lineage and data quality monitoring across Snowflake-managed and externally managed tables, which gives governance a single place to live.
- Role-based access control and role hierarchy design
- Masking, row access policies and data protection
- Access governance and periodic review
- Lineage, discovery and quality monitoring with Horizon Catalog
- Evidence to support compliance and audit requirements
Snowflake AI capabilities
Snowflake Cortex AI brings model-backed functions to data already inside the platform, so governed data does not have to be copied elsewhere to be useful. Cortex Analyst answers business questions over structured data using a semantic view; Cortex Search provides semantic retrieval over text; Cortex Agents combine the two and reason over the result.
- Cortex AI readiness and use-case selection
- Cortex Analyst and semantic view modelling
- Cortex Search for enterprise retrieval
- Cortex Agents over structured and unstructured data
- Data preparation for machine learning workloads
Platform support
Keep the platform dependable once it is live, with monitoring, incident response and a regular review cycle that looks at cost and performance together rather than waiting for a problem.
- Platform and pipeline monitoring
- Incident investigation and resolution
- Scheduled performance and cost reviews
- Access and configuration maintenance
- Release support and continuous improvement
Typical challenges we address.
If any of these apply to your organisation, our specialists can advise on the right approach.
Discuss your priorities- A legacy warehouse that cannot carry current workloads
- Transformation logic with no clear owner
- Consumption cost that is hard to predict or attribute
- Governance and lineage added after the fact
- Data too fragmented for analytics or AI to be trusted
The platforms and applications behind the work.
Make more of your enterprise technology.
Tell us which warehouse or data estate you are modernising, the workloads it must carry, and the governance and cost constraints that apply.