Data Warehousing Services

ClickMasters builds cloud data warehouses for B2B companies across the USA, Europe, Canada, and Australia. Snowflake for organisations that want near-zero infrastructure management and pay-as-you-use compute. BigQuery for GCP-native teams and event-heavy workloads. Redshift for AWS-native architectures. Schema designs that your data team will still understand in 3 years. Query optimisation that keeps costs predictable. And migrations from legacy on-premises warehouses to the cloud.

Snowflake Architecture

BigQuery Implementation

Star Schema / Medallion Design

Query Optimisation & Cost Control

Warehouse Migration

Secure Data Sharing

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Years Experience

Projects Delivered

Client Satisfaction

0/7

Support Available

150+ clients worldwide

4.9/5 rating

Snowflake vs BigQuery vs Redshift Comparison

Pricing model: Snowflake per-second compute credit + storage, predictable scaling. BigQuery serverless pay per query (bytes scanned), zero idle cost. Redshift reserved instances or serverless RA3, predictable for steady workloads.
Compute/storage separation: Snowflake full separation scale compute independently of storage. BigQuery full separation no clusters to manage. Redshift RA3 managed storage separate from compute.
Concurrency: Snowflake excellent multiple virtual warehouses share same data. BigQuery excellent serverless auto-scales. Redshift good concurrency scaling adds clusters on demand.
SQL dialect: Snowflake ANSI SQL + extensions (FLATTEN, VARIANT JSON). BigQuery Standard SQL + extensions (ARRAY_AGG, STRUCT). Redshift PostgreSQL-compatible easiest migration from Postgres.
Semi-structured data: Snowflake VARIANT type query JSON natively without schema. BigQuery ARRAY/STRUCT types nested records, JSON functions. Redshift SUPER type PartiQL for semi-structured.
Cloud provider: Snowflake multi-cloud (AWS, GCP, Azure). BigQuery GCP only. Redshift AWS only.
Best for: Snowflake multi-cloud, near-zero DBA overhead, strong partner ecosystem. BigQuery GCP workloads, very large-scale event analytics, serverless preference. Redshift AWS-native teams, PostgreSQL familiarity, predictable workloads.

What we deliver

Data Warehousing Services We Deliver

05 capabilities

ClickMasters operates as a full-stack data warehousing partner — product strategy, UI/UX, engineering, cloud infrastructure, QA, and ongoing support in one delivery model.

Snowflake Implementation

End-to-end Snowflake setup: account configuration (cloud provider + region), virtual warehouse sizing and auto-suspend (right-size compute overspending is idle warehouse time), role-based access control (RBAC), network policy (IP whitelisting), Snowflake Data Sharing, cost monitoring (resource monitor budget limits, email alert at 80%).

BigQuery Implementation

BigQuery setup for GCP-native teams: project and dataset organisation (dev/staging/prod), partitioning strategy (partition pruning is primary cost control), clustering (reduce bytes scanned further), scheduled queries, BigQuery ML (train and serve ML with SQL), cost monitoring (Quotas + budget alerts).

Schema Design (Star/Medallion)

Dimensional modelling (star schema fact tables + dimension tables, standard for analytical performance) and medallion architecture (Bronze raw â†’ Silver cleaned â†’ Gold business-ready). Principles: appropriate grain, slowly changing dimensions (SCD Type 2), surrogate keys, naming conventions enforced via dbt contracts.

Query Optimisation & Cost Control

Query profiling (identify most expensive queries by bytes scanned/credits), partitioning and clustering (add partition filters 5-10x cost reduction), materialised views (pre-compute expensive aggregations), result caching (Snowflake caches identical queries for 24 hours), warehouse right-sizing (downsize idle warehouses, auto-suspend to 1-5 minutes).

Data Warehouse Migration

Migrate from legacy warehouses (Teradata, Netezza, Oracle, Azure SQL DW) to modern cloud: SQL translation (automated via Schema Conversion Tool, custom functions manual), data validation (compare row counts/aggregates between legacy and target), ETL/ELT migration (SSIS/Informatica â†’ dbt + Airflow), parallel run period (validate before decommission).

Why choose us

Why Companies Choose ClickMasters

05 advantages

We combine architecture discipline, transparent delivery, and long-term partnership — so your investment translates into measurable business results, not just shipped code.

3-Way Comparison Table

Snowflake vs BigQuery vs Redshift 8 rows covering pricing, compute/storage, concurrency, SQL, semi-structured, cloud, best for | Basic: One-size recommendation

Auto-Suspend Savings

"Auto-suspend saves 40-60% on Snowflake spend" specific, checkable claim | Basic: No cost optimisation guidance

Partition Pruning

Primary BigQuery cost control 5-10x cost reduction with proper partitioning | Basic: No partitioning (full scans, high costs)

SCD Type 2

Preserve historical state of changing dimensions essential for audit and trend analysis | Basic: Overwrite only (lost history)

Medallion Architecture

Bronze (raw) â†’ Silver (cleaned) â†’ Gold (business-ready) quality tiers | Basic: Single flat layer

500+

Companies served

4.9/5

Client rating

15+

Years in delivery

Our Process

Our Data Warehousing Process

Scroll to walk through each phase — lines connect as you move down.

Phase 1

Week 1-2

Warehouse Assessment

Technology selection (Snowflake vs BigQuery vs Redshift), schema review (current state), cost model estimation, migration plan. Deliverable: Warehouse Assessment + Selection Recommendation.

Phase 2

Week 2-5

Warehouse Implementation

Account/project setup, virtual warehouse configuration (Snowflake) or dataset structure (BigQuery), RBAC/IAM, network policies, auto-suspend, cost monitoring. Deliverable: Production Data Warehouse.

Phase 3

Week 2-4

Schema Design

Star schema or medallion architecture, fact/dimension table design, slowly changing dimensions (SCD Type 2), surrogate keys, naming conventions. Deliverable: Schema Documentation + dbt model contracts.

Phase 4

Week 3-5

Query Optimisation

Profile top 10 most expensive queries, add partitioning/clustering, create materialised views, configure caching, right-size warehouses. Deliverable: Query Cost Report + Optimised Warehouse.

Phase 5

Week 4-8

Migration (if legacy)

SQL translation (automated + manual), data validation (row counts, aggregates), ETL/ELT pipeline migration, parallel run, cutover. Deliverable: Migrated Warehouse + Validation Report.

Phase 1

Week 1-2

Warehouse Assessment

Technology selection (Snowflake vs BigQuery vs Redshift), schema review (current state), cost model estimation, migration plan. Deliverable: Warehouse Assessment + Selection Recommendation.

Phase 2

Week 2-5

Warehouse Implementation

Account/project setup, virtual warehouse configuration (Snowflake) or dataset structure (BigQuery), RBAC/IAM, network policies, auto-suspend, cost monitoring. Deliverable: Production Data Warehouse.

Phase 3

Week 2-4

Schema Design

Phase 4

Week 3-5

Query Optimisation

Profile top 10 most expensive queries, add partitioning/clustering, create materialised views, configure caching, right-size warehouses. Deliverable: Query Cost Report + Optimised Warehouse.

Phase 5

Week 4-8

Migration (if legacy)

SQL translation (automated + manual), data validation (row counts, aggregates), ETL/ELT pipeline migration, parallel run, cutover. Deliverable: Migrated Warehouse + Validation Report.

Technology Stack

Modern tools we use to build scalable, secure applications.

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Front-end Technologies

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Databases

PostgreSQL

MySQL

SQL Server

Oracle

MongoDB

Redis

Firebase

Elasticsearch

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Cloud & DevOps

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Industry-Specific Expertise

Deep expertise across various sectors with tailored solutions

SaaS Revenue Warehouse

E-commerce Analytics

Legacy Migration to Snowflake

Financial Consolidation

Pricing

Data Warehousing Development Pricing

Transparent pricing tailored to your business needs