Data Warehouse Services
Build the trusted answer layer for BI, copilots, and AI agents
Data warehouses are entering a new cycle of relevance. AI agents can call tools, retrieve records, and trigger workflows, but they still need governed business definitions for revenue, margin, customers, billing, inventory, and performance. We build warehouse foundations that make trusted answers reusable across dashboards, semantic layers, MCP servers, and agent workflows.
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Our Data Warehouse Services
AI-Ready Data Warehouse Architecture
We design warehouse layers for the next era of analytics: BI, executive reporting, semantic layers, and AI agents. Raw application data is separated from cleaned, reconciled, and business-ready datasets so agents call trusted answers instead of improvising metrics from operational records.
Modern Warehouse Implementation
We build reliable warehouse environments on cloud platforms. Whether starting fresh or modernizing an existing stack, we design for performance, auditability, cost control, and the repeatability required when humans and agents depend on the same numbers.
Versatility Across Cloud Platforms
We work with Google BigQuery, Amazon Redshift, Snowflake, and Azure Synapse Analytics, choosing the platform that best fits your ecosystem and requirements.
Pipelines for Trusted Metrics
We design ETL and ELT pipelines that clean, reconcile, transform, and test data before it reaches reporting, semantic layers, MCP servers, or AI-agent tools. The goal is consistent answers, not just data movement.
Data Governance and Metric Ownership
We help define metric ownership, data freshness, validation rules, lineage, access controls, and business definitions so reports and AI agents use the same trusted logic.
Agent-Ready Data Integration
We integrate data from across your organization, including ERP, CRM, billing, logistics, manufacturing, web analytics, and third-party APIs, into a unified warehouse that can serve BI users and agent workflows without creating conflicting versions of truth.
Technical whitepaper
A CIO guide to trusted analytics interfaces for AI agents
This companion whitepaper is written for CIOs, CTOs, and data leaders evaluating AI-agent workflows that touch revenue, billing, operations, manufacturing, and supply chain decisions. It explains why connecting an assistant to business applications is not enough, and how to expose governed analytics answers.
Designing MCP Servers for Business Analytics
A practical whitepaper for leaders designing agent-ready analytics interfaces, including concrete examples for:
- Retail & E-commerce Analytics
- Manufacturing Data Analytics
- Logistics & Supply Chain Analytics
PDF format, free to download.
TESTIMONIALS
Witanalytica has been an awesome team to work with. They have such a talented team with a broad range of expertise in software development, BI and data analysis - which have all been instrumental in helping us achieve our technical goals. We truly value their partnership and look forward to continuing to work together.
Gregg Bansavage
CIO, RBW Logistics
Witanalytica has been an excellent partner in managing and optimizing our Tableau environment. Their team’s technical expertise and proactive support have streamlined our reporting processes, improved dashboard performance, and provided valuable insights to our business. Their responsiveness and deep understanding of data analytics make them a trusted extension of our own team.
Mark Lack
Director of Data Analytics and AI, The Ubique Group
Witanalytica helped us transition from Excel to a dynamic dashboard, allowing us to view all the relevant data and the KPIs that we track as a business. Instead of having our developers code an interface for weeks, we can now instantly accomplish this process through an interface, eliminating the need for manual coding.
Radu Albastroiu
Startup Founder, masinilacheie.ro
Witanalytica’s expertise in big data engineering and visualization complements our digital media audit and customer analytics services. Collaborating with them allows us to deliver end-to-end analytics solutions and services, without the risks and investments associated with building these capabilities in-house.
Silviu Toma
Senior Partner, Microanalytics
Working with Witanalytica has transformed our approach to reporting. Their expertise in PowerBI enabled us to go beyond the limited capabilities of Excel, allowing us to provide our clients with dynamic and visually captivating PowerBI dashboards. This capability has facilitated rapid testing, iteration, and the collection of customer feedback to improve our platform.
Alin Rosca
Startup Founder, RepsMate
Working with Witanalytica has been a consistently positive experience. They are responsive, professional, and approach every revision with patience and precision. What sets them apart is a strong understanding of supply chain management, inventory planning, and sales operations, which makes collaboration efficient and ensures deliverables align with real business needs. They have also worked effectively across multiple departments in our organization and manage a 6-7 hour time zone difference seamlessly. I would confidently recommend them to any organization seeking a skilled and dependable analytics partner.
Rubin Chen
Supply Chain VP, The Ubique Group
Your Goals, Our Expertise
We start from your strategic objectives and work our way back to the right mix of solutions and technologies, not the other way round.
Book a Consulting CallOur Strategic Approach to Data Warehouse Implementation
We begin by identifying the business questions that must be trusted before a dashboard, copilot, or agent can use them: revenue, margin, customer status, billing readiness, supply chain risk, and operational performance. The warehouse is designed around answers, not just source tables.
We identify data sources across departments and obtain access to underlying systems, ensuring all relevant data is included in the warehouse.
We extract data from sources, transform it to fit the warehouse schema, and load it with proper validation, handling structured and unstructured data without hiding business rules inside fragile scripts.
We test for retroactive data changes to ensure integrity, performing upserts to maintain accurate, historical datasets.
We create reusable data marts, views, semantic models, metric definitions, and materialized tables for cross-departmental reporting and AI-agent consumption.
We expose warehouse data through BI tools, semantic layers, cube APIs, controlled APIs, and agent-facing interfaces so consumers can access trusted answers without bypassing governance.
Continuous monitoring, query optimization, cost management, and data governance compliance to maintain a high-performance data warehouse.
We begin by identifying the business questions that must be trusted before a dashboard, copilot, or agent can use them: revenue, margin, customer status, billing readiness, supply chain risk, and operational performance. The warehouse is designed around answers, not just source tables.
When Do You Need Our Data Warehouse Services?
- Your reports are slow, incomplete, and different departments report conflicting results.
- You need to consolidate data from multiple systems into a single source of truth.
- Excel and Google Sheets can no longer handle your data volumes.
- You want real-time or near-real-time analytics across departments.
- Data governance and compliance requirements demand a structured approach.
- Your organization needs cross-departmental alignment on key metrics.
- You are connecting AI agents to business systems and need trusted definitions before they recommend or act.
- You are exposing MCP tools and need the agent to call governed answers rather than raw source records.
Why Hire Witanalytica for Data Warehouse Implementation?
Strategically Aligned
We prioritize your strategic objectives to build data warehouses that deliver measurable business outcomes across BI, operations, and AI-agent initiatives.
Single Source of Truth
We eliminate data silos and conflicting reports by centralizing data into a warehouse that serves as the trusted foundation for analytics, dashboards, and AI-agent workflows.
Cost-Efficient Architecture
We leverage serverless and cloud-native solutions to minimize operational costs while maximizing performance and scalability.
Clean, Governed Data for AI
Comprehensive data preparation with governance frameworks, ensuring accuracy, consistency, and compliance before data is exposed to dashboards, copilots, or autonomous agents.
Cross-Department Alignment
We standardize data dictionaries across departments, ensuring everyone works from the same accurate, up-to-date data.
Reusable Data Products
We create data marts, metric layers, and standardized assets designed for reuse across BI, operations, finance, and AI workflows, promoting consistency across the organization.
Our Data Warehouse Pricing Models
Transparent pricing built for long-term partnerships, not one-off transactions.
On-Demand Expertise
All tasks are tracked, and the corresponding invoice of the delivered services is billed monthly.
| Activity | Hourly Rate |
|---|---|
| AI Agents Development and Implementation | $100 |
| Data Engineering & Database Administration | $110 |
| Business Intelligence Reporting | $90 |
| Data Science | $120 |
Reserved Capacity Agreement
- Pre-purchase a package of monthly working hours that guarantees reserved capacity and priority availability, regardless of our workload.
- Because this capacity is exclusively allocated to you, unused hours do not carry over to the following month.
| Hours Package | Price |
|---|---|
| Every 50 hours | $4,500 10% savings |
Alternatively, we also offer project-based pricing
For well-defined engagements, we scope the full project upfront and agree on a fixed fee, so you know exactly what to expect.
Data Warehouse Platforms
Amazon Redshift
We build data warehouse solutions on Amazon Web Services using Amazon Redshift for fast query execution and scalability to handle massive datasets and complex analytics. These solutions integrate seamlessly with other AWS services for data collection, storage, and analysis.
Azure Synapse Analytics
Our team has hands-on experience with Azure Synapse Analytics, which unifies big data and data warehousing into a single, powerful service. It integrates seamlessly with other Microsoft services, enhancing data workflows and business intelligence capabilities.
Google BigQuery
We design and deploy data warehouse solutions on Google Cloud using BigQuery, a fully-managed, serverless warehouse that enables super-fast SQL queries. It integrates effortlessly with Google's analytics and machine learning tools.
Snowflake Data Cloud
Snowflake's Data Cloud revolutionizes data warehousing by separating storage from computing. It enables businesses to dynamically scale compute resources on-the-fly, without impacting storage. Snowflake supports multiple data types, from structured data to semi-structured data like JSON.
Open Source
Popular open source data warehouses often utilized in PaaS environments include Apache Hive and Apache Hadoop. These solutions offer flexibility and cost-effectiveness for handling large datasets and complex queries. Apache Hive excels in data summarization, querying, and analysis of big data.
Case Studies for Data Warehouse Services
Explore real life case studies and see how we delivered measurable outcomes in similar situations.
Showing 8 case studies
Thanksgiving & Black Friday Sales Analytics: Real-Time Campaign Monitoring
A real-time Tableau dashboard on Salesforce and SQL Server data helped a retailer monitor hourly Black Friday sales and plan next year's strategy.
Read case study →
3PL Digital Transformation: Data Analytics & Automated Invoicing
Discover how a U.S. 3PL company eliminated revenue leakage, automated invoicing, and gained real-time margin visibility.
Read case study →
Affiliate Marketing Dashboards: Unified Performance Tracking
Learn how affiliate teams replaced spreadsheets with unified dashboards to track performance across networks, identify profit leaks, and optimize ROAS.
Read case study →
HitPath to Everflow BI Migration for Affiliate Tracking
Unified HitPath and Everflow data into one BI system with migration monitoring dashboards, ensuring reporting continuity throughout the platform transition.
Read case study →
Multi-Channel Retail Profitability: Amazon vs Wholesale Analytics
A US retailer used Tableau to compare Amazon and wholesale profitability, uncovering margin differences that reshaped their distribution and pricing strategy.
Read case study →
Amazon Ads Reporting with Power BI for Food & Beverage
How a snack manufacturer replaced manual Amazon Ads tracking with automated Power BI dashboards to optimize spend, measure ROAS, and guide marketing decisions.
Read case study →
Healthcare Data Warehouse: Unifying GA4, App Analytics and CRM in BigQuery
How we built a BigQuery data warehouse for a European healthcare clinic, unifying GA4, mobile app analytics, CRM and 10+ paid media platforms to expose onboarding drop-off and enable churn modeling.
Read case study →
Car Rental Marketplace BI: Startup Analytics with Power BI
We built the analytics stack for a car rental marketplace using MongoDB, Python, MariaDB, and Power BI to centralize operations and enable data-driven growth.
Read case study →Related Articles
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Why AI Agents Still Get Business Questions Wrong
MCP and APIs can connect Claude to business apps, but they do not guarantee trusted answers. Learn why AI agents still need governed business definitions.
Snowflake in Practice: What We Learned After Using It at Two Clients
An honest review of Snowflake from two data engineers who used it in production. What works well, what is frustrating, and when it makes sense over alternatives.
GA4 vs Power BI vs Databases: OLTP, OLAP, and Schemas Explained
GA4, Power BI, and BigQuery handle data differently. Understand schemas, OLTP vs OLAP trade-offs, and when to use each type of data product in your stack.
Google Analytics to BigQuery: Export Scenarios and Use Cases
GA4 data in BigQuery unlocks analytics beyond standard reports. See real scenarios: cohort analysis, ML recommendations, and cross-platform attribution.
Data Warehouse FAQs
A data warehouse is a centralized repository that consolidates data from multiple sources into a single, structured environment optimized for analytics and reporting. It serves as your organization's single source of truth.
Your reports are slow, incomplete, or pull conflicting numbers across departments.
You need to consolidate data from multiple systems (ERP, CRM, APIs) into one place.
Your Excel and Google Sheets can no longer handle your data volume.
Different departments report conflicting results from the same data.
You want AI agents or copilots to answer business questions using trusted metrics instead of raw application data.
You are building MCP servers and need governed business answers behind agent-facing methods.
BigQuery is great for Google ecosystem users. Redshift excels in the AWS ecosystem. Snowflake offers maximum flexibility with separate compute and storage. Azure Synapse integrates well with Microsoft tools. We help you choose what fits best.
A single data mart can be built in a few weeks. Comprehensive data warehouses covering multiple departments may span several months. We prioritize the most impactful areas first.
A data warehouse stores structured, processed data optimized for fast queries and reporting. A data lake stores raw, unstructured data in its native format. We often set up both as complementary layers.
Outsourcing provides diverse expertise, cross-industry experience, and scalability. It's ideal if you need specialized data warehousing skills without full-time commitments.
Rigorous ETL validation, data governance frameworks, mandatory fields, automated data cleaning, cross-system standardization, and regular refresh schedules and audits.
AI agents need governed business definitions, not just access to operational systems. A warehouse gives agents a trusted layer for metrics, historical comparisons, entity resolution, exclusions, and auditability so they do not calculate strategic answers from raw APIs during a conversation. In practice, the warehouse becomes the answer layer behind BI, semantic tools, MCP methods, and agent workflows.
You retain complete ownership of architecture and data. We work within your infrastructure using SSH keys, VPNs, IP whitelisting, OAuth APIs, and encryption. We align with GDPR and CCPA.
We offer two engagement models with transparent pricing.
On-Demand Expertise
All work is tracked and billed monthly at hourly rates:
- AI Agents Development and Implementation - $100/hr
- Data Engineering & Database Administration - $110/hr
- Business Intelligence Reporting - $90/hr
- Data Science - $120/hr
Reserved Capacity Agreement
- Pre-purchase a 50-hour monthly package at $4,500 (10% savings)
- Guaranteed priority availability regardless of our workload
We also offer project-based pricing for well-defined engagements.
Contact us to discuss the best fit for your needs.
We actively monitor warehouse performance, query costs, and data freshness. Maintenance takes precedence, with flexible support options to ensure your data warehouse continues delivering value.