Our data warehouse consultants dive deep to grasp your data landscape and craft a bespoke warehousing strategy. From requirements engineering to tool selection, data modeling, ETL setup, and scalability planning, we ensure the architecture supports both classic data analytics and emerging AI initiatives—including LLM integration and agentic AI.
Leveraging best practices, we build custom DW platforms end to end. We help with data inventory, data pipeline automation, integration, data quality management, testing, deployment, and optimization. Even the toughest DW projects are made possible with our data warehousing consultants on board.
As part of data warehouse implementation, we design accurate, timely, and traceable ingestion and transformation pipelines. We automate ETL/ELT workflows, orchestrate data movement across systems, and set up monitoring and lineage—ensuring the right data reaches the warehouse ready for analytics and AI.
We help you adopt cutting-edge data storage solutions, implement new data management practices, and improve data processing. Our consultants will assess your DWH and suggest upgrades to meet current and future challenges, preparing it for AI workloads, vector-enabled queries, and streaming analytics.
Our data warehouse consultants will assist you in planning, executing, and scaling cloud migration projects powered by AWS, Azure, or Google Cloud. ITRex recommends cloud-native architectures that balance cost and performance and support real-time dashboards and AI inference.
Data warehouse consulting keeps your DW a reliable analytics and reporting source. We reduce data latency, optimize performance, and cut storage costs. An ongoing optimization program? We will build it for your data team, ensuring low-latency performance for modern workloads and preparing the warehouse for downstream analytics or AI systems.
We ideate future-proof data architectures tailored to your data flows, business goals, and analytics needs—from warehouses and lakes to lakehouses
We provide data integration services to deliver maximum interoperability across your systems and business units, no matter the complexity
We further leverage our data engineering expertise to tidy up, correct, standardize, and enrich your data employing advanced techniques
We implement robust data management strategies so your teams can store, retrieve, and access trusted data on demand
We help you ensure data compliance, privacy, security, and effective usage through comprehensive data governance frameworks
We integrate your DW with leading BI tools and AI systems, enabling both human and machine decision-making at scale
By consolidating fragmented data and applying governance, your warehouse ensures clean, context-rich input for AI
Your warehouse can work alongside vector stores (e.g., Pinecone, Weaviate, FAISS, pgvector) as the authoritative source of truth for AI knowledge
With standardized definitions, lineage, and access controls, your warehouse keeps data and AI outputs transparent, auditable, and compliant
Modern architecture reduces latency so insights—and decisions—are made on relevant, up-to-date information
We create embeddings and sync them to vector stores (Pinecone, Weaviate, FAISS, pgvector) while keeping the DW as the authoritative source of truth. The outcome? Higher retrieval precision and fewer hallucinations.
We map schemas to business entities and craft retrieval-ready views for specific AI tasks, accelerating time-to-value for copilots and agents.
We orchestrate embedding refresh, evaluate retrieval quality, and monitor drift so AI answers reflect current data, not yesterday’s snapshot.
We apply role-based access controls (RBAC) and link data lineage to model lineage—a must for regulated environments that require audit-ready AI systems.
Aggregate data from multiple sources into a centralized repository so that everyone can make business decisions based on consistent, trusted data
Rely on clean, accurate, complete, and timely data enabled by best practices in data management
Get 360-degree visibility into trends, performance, and anomalies with real-time dashboards and self-service BI
Extract hidden patterns and actionable intelligence to support business decisions and power AI systems with high-quality, trusted data
Companies use data warehouse consulting to transform fragmented, inconsistent data into a reliable foundation for decision-making. With expert advice, you can streamline your architecture, improve data quality, and implement a DWH that grows with your company. It’s more than just reporting; it’s about laying the groundwork for better, faster decisions and long-term growth.
If you’re dealing with unreliable reports, manual data workarounds, or scattered systems that don’t talk to each other, it may be time. Our data warehouse consultants will help assess your current setup, uncover inefficiencies, and propose a roadmap for improving performance, scalability, and readiness for AI and advanced analytics.
A data warehouse consultant assesses your current data landscape, identifies pain points, and recommends solutions that are tailored to your business objectives. This encompasses everything from architecture design and tool selection to data modeling, pipeline optimization, and performance tuning. At ITRex, we combine technical expertise with real-world experience to ensure that your DWH works today and evolves for tomorrow.
Yes—as part of data warehouse implementation, we design and automate ETL/ELT workflows to bring data from multiple systems into the warehouse. We configure batch or real-time ingestion using tools such as Azure Data Factory, Databricks, Microsoft Fabric, or dbt to ensure that high-quality, current data is delivered into the warehouse for analytics and AI.
As a result, there are no more manual exports, CSV uploads, or stale data in your warehouse; everything is always up to date.
We create and implement governance frameworks to promote trustworthy, consistent, and compliant data throughout the organization. This includes establishing data ownership policies, improving data lineage, standardizing KPIs, and implementing controls to ensure accuracy and accessibility, which are critical for both business users and AI systems.
Yes. Legacy systems frequently contain valuable business data but have poor interoperability with modern technologies. Our team specializes in connecting these systems to cloud or hybrid warehouses through ETL/ELT pipelines, API connectors, or custom integrations, ensuring continuity while preserving historical insights.
We begin by mapping your data sources, which may include ERP, CRM, IoT, and third-party platforms. Next, we create robust ingestion and transformation workflows to integrate that data into a unified warehouse, eliminating silos and allowing for consistent reporting, analytics, and AI use cases.
Cloud-based DWHs provide greater scalability, flexibility, and cost efficiency than on-premise solutions. Migration opens the door to faster data processing, real-time insights, and smoother integration with modern analytics and AI platforms—without the maintenance burden of physical infrastructure.
Costs vary depending on the project’s scope, data complexity, and goals. A minor optimization could take a few weeks, whereas a full-scale migration or redesign will necessitate more time and resources. We typically start with a discovery phase to assess your requirements and provide a transparent, tailored estimate based on business impact.
The key components of a data warehousing solution include:
Our data warehouse consulting services emphasize the significance of each component in developing a solid data warehouse strategy. To learn more, read our article on enterprise data warehouse architecture.
A data lake stores vast amounts of raw, unstructured data in its native format.
A data warehouse, on the other hand, holds structured data that has been processed and structured for specific purposes.
A data lakehouse combines the two, offering a way to manage both raw and processed data, supporting diverse analytics and ML in a single, scalable architecture, and allowing businesses to leverage broad data capabilities, from real-time analytics to deep learning, within one platform.
Learn more about the difference in our comprehensive article, “Cutting Through The Confusion: Data Warehouse vs. Data Lake vs. Data Lakehouse.”
Integrated planning & discovery: This phase of data warehouse consulting services establishes a solid groundwork for the project’s success. This is achieved by ensuring the alignment of stakeholder expectations and goals with an in-depth examination of the organization’s current tech landscape. Key activities done by data warehouse consultants include identifying business needs, cataloging data sources and connectors, evaluating existing reports and machine learning models, projecting budgetary needs and team structure, assessing the quality of data, and analyzing metrics. This phase culminates in conceptualizing a data warehouse strategy and ETL framework to propose solutions with different tech stacks and develop a prototype.
Development & Testing: Based on the chosen tech stack and defined needs, this phase focuses on constructing a resilient data warehouse. Key tasks involve setting up the database structures and schemas, integrating various data sources, creating ETL processes, conducting data profiling, importing past data records, enforcing data integrity measures, fine-tuning automation processes, and conducting rigorous testing to ensure reliability and performance.
Ongoing Support: This phase involves establishing a dedicated team to provide support services for the data warehouse, encompassing the resolution of issues in compliance with SLA conditions, minimizing expenses related to storage and processing, executing minor improvements, overseeing system operations, and troubleshooting.
To ensure data security and privacy within a warehouse, our data warehouse consulting services include implementing several measures:
The time to implement a data warehouse solution varies significantly based on the complexity of the data, the technology chosen, the scale of the project, and the specific requirements of the business. Typically, a basic implementation can take a few months, while more complex projects might take a year or more.
Leveraging deep expertise, our data warehouse consulting team ensures rapid project delivery. We streamline every phase, from planning to testing, to exceed expectations efficiently.
Our data warehouse consulting team provides extensive post-implementation maintenance and support, including system monitoring, troubleshooting, updates, user support and training, data quality checks, and adjustments based on feedback and analytics. This ensures your data warehouse remains efficient, secure, and responsive to your business needs.
Tell us about your challenges, and we’ll come up with a viable solution!
