Storage and Compute, Finally Separate
Traditional warehouses force you to buy a cluster big enough for your worst day, then pay for it every other day too. Snowflake breaks that link — storage sits in cheap cloud object storage, and compute spins up only when a query runs.
That means the finance team's month-end crunch can't slow down the analysts, each workload gets its own warehouse, and everything scales up or down in seconds. It runs on AWS, Azure and GCP, so it isn't a bet on one cloud.
Pay Per Query, Not Per Server
Compute auto-suspends when idle and resumes in seconds — no cluster sitting there burning money.
No More Query Queues
Give every team its own virtual warehouse — one team's heavy job can't starve another's dashboard.
Multi-Cloud by Design
The same platform on AWS, Azure or GCP — and across regions — so you're not locked to one vendor.
What Makes It Different
The capabilities that keep pulling teams off legacy warehouses.
Elastic Compute
Resize a warehouse mid-workload, or auto-suspend it when idle. Scaling is a setting, not a migration.
Zero-Copy Cloning
Clone a production database instantly for dev or testing — no storage duplicated, no waiting.
Time Travel
Query data as it looked days ago, and undrop a table someone deleted by mistake.
Secure Data Sharing
Share live data with partners and customers without copying or moving a single file.
Semi-Structured Data
Query JSON, Avro and Parquet with plain SQL — no separate pipeline to flatten it first.
Snowpark
Run Python, Java and Scala transformations directly where the data lives.
End-to-End Snowflake Services
From first migration to a bill you can actually explain.
Account & Warehouse Design
Databases, schemas, roles and virtual warehouses laid out for your teams and workloads.
Migration
Move off Teradata, Oracle, SQL Server or Redshift in phases - AI drafts the SQL conversions and reconciliation tests, developers review every change.
ELT Pipelines
Snowpipe, Streams and Tasks — or dbt and Fivetran — landing clean, tested data on schedule.
Cost Optimisation
Right-sized warehouses, auto-suspend and query tuning, so you stop paying for idle compute.
Security & Governance
RBAC, row-level security, dynamic masking and audit trails for regulated data.
BI Integration
Power BI, Tableau and Looker wired to a modelled layer your business actually trusts.
Sizing Warehouses and Governing Credits
Snowflake bills compute in credits, and each step up in warehouse size roughly doubles the credits consumed per hour. A larger size helps when individual queries are heavy; when the problem is many users at once, a multi-cluster warehouse that adds clusters under load is usually the better answer. We size from query history, not guesswork, and start one size smaller than feels comfortable.
Governance is what keeps the bill explainable: separate warehouses for loading, transformation, BI and data science so spend maps to a team; short auto-suspend timeouts; resource monitors that notify, then suspend, at agreed credit quotas; and a regular review of the account usage views to catch runaway queries, oversized warehouses and tables that are clustered but rarely filtered.
dbt, Snowpark and Data Sharing
For most transformation work we use dbt on Snowflake: SQL models under version control, tests on keys and accepted values, documentation generated from the code, and incremental models so large tables are not rebuilt every run. Changes go through pull requests and run against a zero-copy clone of production before they merge.
Snowpark covers the logic that is awkward in SQL - Python feature engineering, scoring a trained model, parsing messy files - while keeping the data inside Snowflake instead of exporting it to a separate cluster. Secure data sharing and the Snowflake Marketplace work in the other direction: you can publish live tables to partners or customers without copying files, and subscribe to third-party datasets that appear in your account ready to join, with no ingestion pipeline to maintain.
Locking It Down, Moving It Over
Snowflake security is built on role-based access control, and a clear role hierarchy matters more than any single setting. We separate access roles, which own privileges on databases and schemas, from functional roles that map to teams, then layer on masking policies for columns such as emails and national IDs, row access policies for regional or tenant data, network policies, and SSO or key-pair authentication for service accounts.
Moving off an on-premises warehouse such as Teradata, Oracle, Netezza or SQL Server starts with an inventory of tables, stored procedures, scripts and downstream reports. DDL and SQL dialects are converted, history is bulk-loaded from cloud storage stages, and incremental feeds are switched to Snowpipe or scheduled tasks. Both systems then run in parallel while row counts, aggregates and report outputs are reconciled, and each subject area cuts over only once they match.
Pixel-perfect software, delivered at AI speed
PixoBots stands for Pixel & Bots. Our Bots are dedicated developers who work with AI tools: AI takes the repetitive work, a developer reviews every line, and the result is pixel-perfect.
Pixel
Polished UI and clean, tested code - detail is part of the job, not an afterthought.
Bots
Dedicated developers who join your team and use AI for boilerplate, tests and documentation.
Savings
AI-assisted delivery can save more than 50% of development cost compared with traditional development.
Ready to Move to the Data Cloud?
Let our engineers design a Snowflake account that scales with you — and a bill that doesn't surprise you.