Run analytics, AI and apps on one platform built to scale.

Snowflake is a fully managed cloud data platform, often called the AI Data Cloud. It brings data warehousing, data engineering, analytics, AI, and data sharing into one environment that runs on AWS, Azure, or Google Cloud. Organisations use it to unify data from every source, scale compute on demand, and run everything from BI reporting to production AI on a single governed platform.

A traditional warehouse ties storage and compute together, so you pay for peak capacity all the time and rebuild when you outgrow it. Snowflake separates storage from compute, so you scale each independently and only pay for what you use. It also handles structured and semi-structured data, supports multiple clouds, and adds AI and data sharing without extra infrastructure to manage.

All five are strong cloud data platforms. Snowflake stands out for near-zero maintenance, per-second consumption pricing, and cross-cloud data sharing. BigQuery suits Google Cloud-native teams, Redshift fits deep AWS estates, Databricks leads for code-first data science and lakehouse workloads, and Microsoft Fabric fits Microsoft and Power BI estates. Catalyst BI recommends the platform that fits your team, estate, and workloads, not a single default.

Snowflake charges separately for storage and for compute, billed per second while a virtual warehouse runs. Costs rise when warehouses are oversized or left running, not because the platform is expensive. Our Snowflake Health Check reviews your environment and puts the right cost controls in place, so finance sees predictable spend tied to actual usage."

Yes. Snowflake lets teams build, deploy, and scale machine learning and large language models on governed data without moving it out of the platform. Because models run on the same trusted data as your reporting, you avoid the copies and silos that stall most AI projects. This makes Snowflake a practical foundation for forecasting, churn prediction, and GenAI use cases.

Snowflake is the storage and compute foundation, and it works alongside the rest of the modern stack. Fivetran and Qlik move data in, Coalesce and dbt transform it, DataOps.live automates CI/CD, and Qlik or Power BI visualise it. The tools cover ingestion, storage, transformation, orchestration, and analytics with no overlap.

A focused migration of a legacy warehouse typically takes three to six months, depending on the number of source systems and the complexity of existing reporting. Catalyst BI delivered a full cloud data warehouse for INEOS Automotive in six months. We phase migrations so priority workloads move first and value lands early, rather than waiting for a single big-bang cutover.

No. Snowflake connects to Qlik, Power BI, Tableau, and most modern BI tools, so your teams keep the reporting they already know. Most organisations modernise the data foundation first, then improve or consolidate reporting over time. Catalyst BI prioritises the changes that remove the most cost and risk first.

Most engagements start with a 30-day Proof of Value on your own data and workload, so you see real consumption-based cost numbers and board-ready evidence before you commit. From there it runs in three phases: a scoped migration of one high-value workload, a phased rollout to priority domains, and ongoing cost and performance optimisation through our managed services team.