AWS Data Warehouse
Development Services
We design and build cloud data warehouses on AWS that give your teams fast, reliable reporting without the manual spreadsheet work or the wait for IT to run a query.
From schema design through Redshift implementation and performance tuning, we build warehouses sized for how your business actually reports, not a generic template that gets slow the moment real data volume hits it.
What Is AWS Data Warehouse Development?
AWS data warehouse development is the design and build of a structured, query-optimized database - typically on Amazon Redshift - that stores cleaned, modeled data specifically for fast, repeatable reporting and analytics. Unlike a data lake, which holds raw data in its original form, a warehouse holds data that's already been shaped into fact and dimension tables so BI tools and analysts can query it directly, without knowing the underlying source systems at all.
This service covers building or re-architecting a warehouse from the ground up: schema design, platform setup, data loading, and performance tuning. If you already have a warehouse and need to move it onto AWS or bring it up to modern standards, that's a related but different project. See our data warehouse modernization services for that scope.
Our AWS data warehouse development services help you:
- Replace slow, manual reporting with a warehouse built for fast queries
- Model data into star or snowflake schemas that match how your business actually reports
- Load data reliably from source systems, pipelines, or your data lake
- Tune query performance so dashboards load in seconds, not minutes
- Control warehouse compute cost as data volume and usage grow
- Connect directly to the BI tools your analysts already use
We build primarily on Amazon Redshift because it's the AWS-native warehouse engine, and we bring the same team in for schema design, loading, and tuning, so nothing gets lost between separate vendors.
AWS Data Warehouse Development Capabilities
Here's what our team delivers, whether you're standing up your first warehouse or replacing one that's outgrown its original design.
1. Data Warehouse Architecture & Schema Design
The foundation of a fast warehouse is the schema underneath it. We design the structure before we build anything, so performance problems don't show up six months in.
Capabilities:
- •Star and snowflake schema design
- •Fact and dimension table modeling
- •Slowly changing dimension (SCD) handling
- •Schema design reviews for existing warehouses that have grown ad hoc
2. Redshift Platform Implementation
We set up the warehouse platform itself, sized for your data volume and query concurrency rather than over-provisioned by default.
Capabilities:
- •Redshift cluster or Redshift Serverless setup and configuration
- •Node type and sizing decisions based on workload
- •Workload management (WLM) and queue configuration
- •Backup, snapshot, and disaster recovery setup
3. Data Modeling & Dimensional Design
We turn raw or semi-structured source data into a model analysts can actually query without writing complex joins every time.
Capabilities:
- •Dimensional modeling for reporting and BI use cases
- •Semantic layer design for consistent business definitions
- •Historical tracking and versioned dimension design
- •Data type, key, and indexing strategy for query speed
4. ELT & Data Loading Pipelines
Getting data into the warehouse reliably matters as much as the schema itself. We build the loading layer, or connect to pipelines you already have.
Capabilities:
- •Scheduled and incremental loading from source systems
- •Loading from your data lake or upstream pipelines into the warehouse
- •Data validation checks during load
- •Load failure handling and alerting
5. Query Performance & Cost Optimization
A warehouse that's slow or expensive at scale usually has the same root causes: poor distribution keys, missing sort keys, or queries fighting each other for compute. We fix these at build time.
Capabilities:
- •Distribution and sort key strategy for common query patterns
- •Query plan analysis and slow-query remediation
- •Concurrency scaling and workload isolation
- •Storage and compute cost review
6. BI & Reporting Enablement
Once the warehouse is built, we connect it to the tools your team reports from, so the transition from "IT project" to "usable by analysts" is immediate.
Capabilities:
- •Direct connectivity setup for BI and dashboard tools
- •Query result caching for frequently run reports
- •Role-based access setup for reporting users
- •Documentation of schema and business definitions for analysts
How We Build AWS Data Warehouses
Discovery & Reporting Requirements
We look at your current reporting pain points, source systems, data volume, and the queries your teams actually need to run, before deciding on a schema.
Schema & Architecture Design
We design the star or snowflake schema, sizing decisions, and platform configuration around those requirements, not a generic reference architecture.
Platform Build & Data Loading
We provision Redshift, build the loading layer, and load historical and incremental data into the modeled schema.
Performance Tuning & Validation
We test query performance under real workload patterns, tune distribution and sort keys, and validate data accuracy against source systems.
Deployment, Handover & Support
We connect BI tools, document the schema for your analysts, and either hand over or stay on for ongoing tuning and support.
Built for How You Actually Report
AWS Data Warehouse Technologies We Use
Platform & Compute
- Amazon Redshift
- Redshift Serverless
- Amazon Athena
Data Modeling & Loading
- AWS Glue
- dbt
- SQL / PL-SQL
BI & Reporting Connectivity
- Amazon QuickSight
- Power BI
- Tableau
Complementary Platforms
- Snowflake
- Databricks
Redshift is our default because it's the AWS-native engine and integrates directly with the rest of your AWS stack. Some teams are better served by Snowflake or Databricks, usually because of an existing multi-cloud footprint or a specific workload pattern. We support both and can help you weigh the decision. For a side-by-side breakdown, see our Redshift vs. Snowflake vs. Databricks comparison.
Industries We Build Data Warehouses For
Healthcare & HealthTech
Warehouses that turn clinical, claims, and operational data into reports your compliance and operations teams can trust.
Fintech & Banking
Warehouse architectures built for regulated reporting, audit trails, and the query volume that risk and finance teams generate daily.
SaaS & Technology
Warehouses that support product, revenue, and customer reporting without the query slowdowns that come from growing usage data.
E-commerce & Retail
Warehouses sized for high-volume transactional and behavioral data, with reporting that stays fast during peak periods.
Enterprise Data Platforms
Consolidated warehouse architectures that bring reporting from multiple business units and legacy systems into one queryable structure.
Why Choose Eagle in Cloud for AWS Data Warehouse Development?
Schema Design First
We design the data model before we build anything, which is where most warehouse performance problems actually start.
Redshift Depth, Not Just Setup
Our team tunes distribution keys, sort keys, and workload management, not just provisions a cluster and hands it over.
One Team, Full Pipeline to Report
The same engineers handle schema, loading, and tuning, so nothing gets lost in handoffs between separate vendors or teams.
Cost-Aware From the Start
We size compute to your actual query patterns instead of defaulting to the largest cluster to avoid a follow-up conversation later.
Proven Across Regulated & High-Volume Industries
We’ve built warehouses for healthcare, fintech, and e-commerce teams with real audit, compliance, and scale requirements.
Frequently Asked Questions
Still have questions? We are here to help you.
Ask a Warehouse ArchitectReady to Build a Data Warehouse Your
Team Can Actually Report From?
Let's replace the slow queries and manual spreadsheet exports with a warehouse designed around how your business reports.
Let's Build Your AWS Data Warehouse