Snowflake
The cloud data platform for analytics, data sharing, and the modern data stack. Separates compute from storage for elastic scaling.
ClearView
The modern data platform
Snowflake stores data once, in its own compressed columnar format, and points independently sized compute warehouses at it, so a heavy transformation job and the analysts running dashboards never contend for the same resources. Warehouses bill by the second while they run and suspend when idle, which makes cost a product of workload design as much as data volume.
The platform now reaches well beyond warehousing. Snowpark runs Python transformations where the data lives, Cortex brings model inference inside the same governance boundary, and secure data sharing gives another organisation live access to a dataset without a copy leaving the platform. Adaca is a Snowflake partner, and works across that surface: Cortex AI, Snowpark, and the consolidation of scattered data estates onto the platform.
Snowflake is an analytics platform, and it is the wrong choice for transactional workloads, which belong in an operational database such as PostgreSQL. It is also more platform than a small estate needs: where analytical data fits comfortably in PostgreSQL and the audience is a handful of internal dashboards, a warehouse adds cost without adding capability.
Snowflake was used by 4.1% of respondents to the 2025 Stack Overflow Developer Survey.
Snowflake deploys platform releases weekly, so new features arrive without customer-managed upgrades.
- Data Warehousing
- Analytics
- Data Sharing
- ELT Pipelines
Our Snowflake capabilities
Data warehouse design
Warehouse design in Snowflake is mostly cost design. Micro-partitioning removes the index maintenance of a traditional warehouse, so the work shifts to clustering keys chosen from real query predicates and warehouse sizes matched to each workload rather than one shared default. We size from query history and revisit as usage changes.
- Schema design
- Clustering
- Warehouse sizing
ELT pipeline development
Pipelines land raw data continuously through Snowpipe or a managed connector such as Fivetran, then transform inside the platform with dbt, where every model is versioned, tested and documented as code. Keeping the raw layer replayable means a wrong transformation is corrected and re-run rather than patched with backfilled data.
- Snowpipe
- dbt
- Fivetran
Analytics & data sharing
Secure shares give a counterparty live, governed access to selected data with no extract pipeline to build or maintain, and reader accounts extend the same access to organisations without their own Snowflake account. Snowpark carries the heavier work, running Python transformations and model scoring beside the data rather than shipping it to an external cluster.
- Data sharing
- Snowpark
- Marketplace
Why Adaca for Snowflake?
Snowflake platform expertise
Deep experience across compute, storage, and services layers.
Modern data stack
Integration with dbt, Fivetran, Airbyte, and other modern data tools.
Cost optimisation
Warehouse sizing, auto-scaling policies, and query optimisation to control costs.
Data governance
Role-based access, data masking, and audit logging for compliant data management.
Snowpark development
Data engineering and ML workloads in Snowflake using Snowpark and Python.
Migration experience
Migration from legacy data warehouses with proven methodologies.
How We Staff It
Snowflake work is data engineering work, and the people staffed to it carry the surrounding stack as standard: dbt for transformation, an orchestrator over the top, Python through Snowpark, and the SQL depth the platform assumes.
Engagements often begin with a defined outcome, a warehouse migration or a pipeline consolidation, then settle into steady platform ownership. Engineers work to your working day.
Common Questions
Do we need Snowflake, or will PostgreSQL do?
PostgreSQL does more analytics than it is usually given credit for. A read replica with good indexing serves reporting comfortably into the hundreds of gigabytes, and where that covers the requirement, it is the cheaper and simpler answer.
Snowflake earns its place when the shape of the work changes: terabyte scans a row store cannot answer in useful time, many concurrent analytical users, continuous semi-structured feeds, or data shared with other organisations under governance. Any one of those justifies it. The transactional database is a separate question, and it stays in PostgreSQL.
Should we consolidate several data platforms onto Snowflake?
Consolidation pays where the estate has accreted: a legacy warehouse, an export-driven reporting database, and a lake with no owner, each with its own pipelines and access model. Bringing them onto one platform collapses the pipeline count and puts a single role hierarchy and masking policy over data that previously had three.
Sequencing matters more than the destination. Warehouse workloads move first, because dbt models port most cleanly; the long tail of reports and extracts moves last, once the data it depends on already flows. We run consolidations in that order, keeping the old platform readable until nothing reads from it.
Does AI-assisted development work on Snowflake?
The SQL side works well. The language corpus is deep, Snowflake's information schema gives an agent the tables, types and constraints to write against, and dbt tests give generated transformations a fast correctness check.
What an agent does not see is the meter. A query can be correct and still scan a terabyte that a well-clustered table would prune, or hold an oversized warehouse resumed, and nothing in the code signals either. Generated work is therefore reviewed against query profiles and credit consumption as well as correctness, and that review stays with human engineers. The measured results of our AI-augmented delivery flow are on the Technology page.
Will you take over a Snowflake platform another team built?
Yes, and it is a common starting point. The first weeks read the estate as it stands: query history for the expensive patterns, warehouse configuration against actual load, and the role hierarchy and masking policies against who should see what. Improvements then land inside the running platform, without a rebuild.
Staffing follows our usual model: a shortlist usually reaches you within 48 hours of a brief, engagements start at one engineer, and terms are three, six, nine or twelve months, chosen up front.