Data Engineering & Platforms

Platform Capability

Snowflake

The cloud data platform at the centre of modern analytics and AI.

Snowflake AI Data Cloud Select Services Partner badge

AgentFaktory is a Snowflake Select Services Partner.

Recognised services capability for Snowflake AI Data Cloud delivery, Cortex AI, governance, and production data engineering.

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What it is

Snowflake separates compute from storage, making it the go-to choice for enterprises that need to scale analytics independently of data volume — and increasingly, the foundation on which production AI is built with Snowflake Cortex.

When we recommend it

  • Your analytics and BI workloads are unpredictable and you need elastic scaling without managing clusters.
  • You want to run AI and ML workloads on the same platform as your analytics, without moving data.
  • You are migrating off a legacy on-premise warehouse and need a clean, governed landing zone.
  • Multiple teams or business units need access to shared data with fine-grained access controls.
  • You need to share data securely with partners or subsidiaries without copying it.

What we deliver

Our Snowflake capabilities

Snowflake Architecture & Design

Multi-cluster warehouse sizing, virtual warehouse strategy, cost governance, and data architecture design from scratch or migration.

Data Pipeline Engineering

ELT pipelines into Snowflake using dbt, Fivetran, Airbyte, or custom Snowpark Python — with full lineage, testing, and incremental load patterns.

Snowflake Cortex AI

LLM-powered search, classification, translation, and summarisation directly inside Snowflake using Cortex functions — no data leaves your environment.

Snowpark for ML

Feature engineering, model training, and inference pipelines running natively in Snowflake using Snowpark Python and the Snowflake ML API.

Migration from Legacy Warehouses

Schema translation, workload migration, and cutover planning from Oracle, Teradata, SQL Server, or Redshift — with zero regressions validated.

Governance & Data Sharing

Row-level security, dynamic data masking, object tagging, and Snowflake Data Sharing for controlled cross-organisation data exchange.

Implementation playbook

From warehouse landing zone to governed AI Data Cloud

We do not treat Snowflake as a database replacement. A proper Snowflake build covers account architecture, ingestion, modelling, workload isolation, governance, AI readiness, and operating controls from the start.

Discuss this architecture
01

Foundation decisions

  • 01

    Account, region, organisation, role hierarchy, warehouse strategy, and environment separation.

  • 02

    Ingestion patterns for batch, CDC, files, APIs, streams, and third-party ELT connectors.

  • 03

    Raw, cleansed, curated, and semantic layers with dbt or native Snowflake transformation patterns.

  • 04

    Access governance using roles, masking policies, row access policies, tags, object ownership, and audit views.

  • 05

    Cortex AI, Snowpark, vector search, and external access patterns where AI workloads need governed data.

02

Common Snowflake programmes

  • New Snowflake landing zone and warehouse buildout.

  • Legacy warehouse migration from Oracle, SQL Server, Teradata, Redshift, or on-premise appliances.

  • Snowflake cost and performance rescue for estates with warehouse sprawl or slow BI.

  • Cortex AI enablement for document intelligence, semantic search, classification, summarisation, and AI analyst use cases.

03

Handover assets

  • Target architecture and deployment plan.

  • Production-ready schemas, warehouses, roles, and governance policies.

  • Validated ingestion and transformation pipelines with reconciliation checks.

  • Cost, performance, lineage, and data quality monitoring dashboards.

  • Runbooks for release, incident handling, access review, and warehouse operations.

Working with Snowflake?

Tell us where you are and what you are trying to build. We will be straight with you about whether Snowflake is the right tool for it.

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