Data & Analytics
When teams work from different data, they make conflicting decisions. A well-designed data warehouse unifies your data, ensures consistency, and gives every team access to the same trusted, up-to-date information.
The Problem
Star and snowflake schema design with fact and dimension tables optimized for analytical query performance - Without this, you risk wasting time, money, and competitive opportunities.
Modular, tested, and documented SQL transformations using dbt with staging, intermediate, and mart layers - Without this, you risk wasting time, money, and competitive opportunities.
Column-level documentation, lineage tracking, access controls, and data classification for compliance - Without this, you risk wasting time, money, and competitive opportunities.
How We Do It
Inventory all data sources, understand business processes, and document analytical requirements and KPI definitions
Select the warehouse platform, design the schema, and plan the data modeling approach for your use cases
Build dimensional models with dbt, implement staging and transformation layers, and write data quality tests
Connect data sources, implement ELT pipelines, and configure automated refresh schedules
Document the data catalog, set up access controls, train your team, and establish maintenance procedures
The Proof
CodeLeap transformed our vision into a complete product in just 3 months. The quality and commitment were exceptional - we could not have achieved this on our own in an entire year.
Sarah Chen
Chief Technology Officer, TechVista Inc.
Reduction in decision-making time with real-time dashboards
What You Get
Timeline: 5-12 weeks depending on source count
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