Data warehousing services

Data warehousing services

Why partner with Itransition

Providing IT consulting and software engineering services since 1999 

15+ years delivering data warehousing and BI solutions

Highest level of client data safety due to compliance with the ISO 27001 standard 

Partnerships with Microsoft and AWS

Data warehouse services we offer

Itransition provides a full range of data warehousing consulting and development services to help you maximize data value with flexible and secure solutions.

Data warehouse services we offer

We use various architectural approaches to design scalable and high-processing data warehousing solutions that effectively consolidate heterogeneous data from siloed corporate systems in one place.

  • Analyzing existing data flows, source systems, and management practices
  • Data warehouse architecture design
  • Selecting suitable DWH tools and services
  • Data modeling
  • ETL/ELT design
  • DWH implementation planning

We perform end-to-end data warehouse implementation — from initial needs assessment to post-launch support — and guarantee seamless integration of a data warehouse solution into your IT ecosystem.

  • Eliciting data warehousing needs
  • Conceptualizing a DWH solution and selecting an optimal tech stack
  • Designing and developing a data warehouse
  • Integrating DWH into the existing IT environment
  • Conducting quality assurance
  • Migrating and testing historical data
  • Conducting user training
  • Providing after-launch support and maintenance

We conduct a comprehensive assessment of the current data warehouse environment and make necessary functionality optimizations and on-demand data warehouse reconfigurations to improve your DWH solution's performance and cost efficiency. We also provide ongoing support and maintenance to ensure your solution's smooth operation and reliability.

  • Tuning ETL pipelines, databases, data quality management tools, and other DWH components
  • DWH design optimization (data remodeling, indexing, partitioning, etc.)
  • Integrating DWH with new services and expanding data sources
  • Configuring and administering the data warehouse environment
  • Continuous monitoring and support

We migrate your legacy data warehousing solution to the cloud environment to enhance its scalability, flexibility, and overall performance, ensuring minimized disruptions to your business processes.

  • Analyzing the existing DWH setup
  • Conceptualizing a cloud data warehouse solution
  • Choosing the optimal cloud tech stack
  • Developing a migration strategy
  • Deploying the solution in the cloud
  • Integrating the cloud DWH with the existing data environment
  • Developing data pipelines and migrating data to the cloud environment
  • Post-migration data audit

Do you have a data warehouse project in mind?

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Client spotlight

Pharmaceutical data analytics suite


faster data processing

Itransition redeveloped the customer’s flagship pharmaceutical data analytics suite. The delivered solution is now used by many of the world's leading pharmaceutical corporations to handle multi-source and multi-format data in a standardized and well-organized way.

BI system modernization for order management


faster data delivery

Itransition set up the ETL process, built a data warehouse, migrated 150+ complicated reports from Avora to Power BI, and developed intuitive dynamic dashboards for automated data-driven decision-making. The upgraded system helped the customer achieve complete supply chain visibility and optimize inventory and order lifecycle management.

Our data warehousing development expertise

Data integration

We establish an effective end-to-end process to convert data from disparate sources into a standardized format ready for storage and analysis by applying various methods, including ETL/ELT, change data capture, and data replication.

Data quality management

We set up and configure policies, technologies, and practices that help continuously monitor data quality and keep it satisfactory.

Data storage

Our team builds various storage options, such as data warehouse databases, data marts, data lakes, lakehouses, and operational data stores, to centralize business data for further analysis. We create conceptual, logical, and physical data models to structure the data within the repository and simplify access to it and its retrieval.

Online analytical processing (OLAP)

We build OLAP cubes storing data in a multidimensional format to enable rapid analysis and help non-technical users easily navigate data and conduct comprehensive self-service data analysis.

Data governance

We establish a robust data governance framework to help you ensure the availability, usability, integrity, and security of corporate information.

Metadata management

We set up practices for metadata management to effectively utilize data resources and enhance data search and discovery.

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How we ensure DWH implementation success

Data warehousing solution interoperability

We integrate your data warehouse solution with all your data sources, such as ERP, CRM, finance platforms, and corporate websites, as well as analytics and BI software. Additionally, we equip your data warehouse with pre-built connectors for easy integration with other data sources.

Future-proof architecture

We create data warehousing solutions, allowing easy upgrades and integrations and ensuring adaptability to new technologies and data formats.

Autonomy & automation

We employ various technologies that automate data warehouse administration and maintenance tasks, such as data backups, data quality management, security updates, and metadata management.

Focus on security

To protect the data stored in your DWH solution, we set up robust security mechanisms, including sensitive data encryption and masking, granular access controls, and multi-factor authentication. Our comprehensive security measures guarantee adherence to industry standards and protection against evolving cyber threats.

Data warehouse deployment models

Our consultants will help you choose a data warehouse platform and deployment model based on your business strategy and requirements and the existing IT infrastructure.

On-premises data warehouses grant you complete control over your data warehouse's hardware and software infrastructure. Our team can set up your DWH solution on a local server, ensuring minimized latency, high availability, security, and compliance with data governance regulations that require keeping data on-site.
A cloud-based data warehouse model allows for faster deployment, minimized upfront costs, and solid fault tolerance and disaster recovery. Our experts choose the most reliable cloud service providers, such as AWS, Microsoft Azure, and Google Cloud Platform, for deploying and managing your data warehouse solution.
A hybrid data warehouse combines the benefits of both cloud and on-premises deployment models. We can build a solution that lets you fully enjoy cloud flexibility and scalability while maintaining control over your data and meeting stringent regulatory requirements.

Our DWH solution implementation process


Discovery & business requirements definition

We start by interviewing end users of your future DWH solution to understand better how it should meet your business needs and objectives. At this step, we also define types of routine data analysis and data management practices currently in place and assess your data security requirements.


Data warehouse conceptualization

Using insights gathered after a careful business and system requirements analysis, we define your future DWH solution's scope and optimal feature set.


Technology selection

After defining the architectural approach to building your data warehousing solution, we choose appropriate technologies for each component. At this phase, we also help you decide on the optimal deployment model for your DWH solution — on-premises, cloud, or hybrid.


Data modeling & DWH environment design

We identify data sources, analyze the information they contain, perform conceptual and logical data modeling, and convert created models into database structures. This step also involves designing ETL pipelines and establishing data access and usage policies.



Our team configures selected technologies and develops DWH infrastructure components, including ETL pipelines and a data security component. Next, our data engineers integrate DWH components with the existing data environment.


Testing & launch

We conduct comprehensive testing activities that assess data warehouse performance and verify data quality in terms of legibility, completeness, and security. After that, we provide the necessary training to end users and deploy your DWH solution to production.


Post-launch support & maintenance

We continuously monitor DWH performance, integrate additional data sources, and fine-tune models as needed to maintain the effectiveness of your solution over time.

Benefits of our data warehousing solutions

Our data warehouse consultants create scalable and high-performing data warehousing solutions that bring tangible business benefits to your company.

Centralized data storage
Consolidate disparate structured and unstructured datasets in a unified repository

Informed decision-making
Get a 360-degree view of your company and make sound decisions at all levels

Improved data quality
Support your decisions with accurate, consistent, timely, and complete data

A solid base for analytics initiatives
Enable data mining, statistical data analysis, what-if scenario modeling, forecasting, and predictive modeling

Governed data access & administration
Make data accessible while eliminating the risk of security incidents and cases of data misuse

Streamlined data management
Automate most resource-intensive data management activities and reduce time-to-insights


Automate data management processes with Itransition

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Data warehouse technologies we master

Data storage

  • Azure SQL Database
  • Azure Blob Storage
  • Azure Cosmos DB
  • Azure Data Lake Storage
  • Azure SQL Database elastic pools
  • Azure SQL Managed Instance
  • Microsoft SQL Server
  • Amazon Simple Storage Service (Amazon S3)
  • Amazon DynamoDB
  • Amazon ElastiCache
  • Amazon Elastic File System (Amazon EFS)
  • Amazon Elastic Block Store (Amazon EBS)
  • Amazon Relational Database Service (RDS)
  • Amazon Aurora
  • PostgreSQL
  • MySQL
  • Google Cloud Storage
  • Google Cloud SQL
  • Google Cloud Bigtable
  • Google Cloud Memorystore
  • Oracle IBM Cloud Object Storage
  • SAP HANA Cloud
  • Snowflake

Data processing & analytics

  • Azure Stream Analytics
  • Azure Synapse Analytics
  • Azure Analysis Services
  • Microsoft SQL Server Analysis Services
  • Microsoft Power BI
  • Azure Databricks
  • Azure IoT Hub
  • Amazon Redshift
  • Amazon Athena
  • Amazon EMR
  • Amazon Elasticsearch
  • Amazon Kinesis Data Analytics
  • Google BigQuery
  • Google Cloud Dataproc
  • Google Cloud Dataprep
  • Google Cloud Dataflow
  • Google Cloud Pub/Sub
  • Teradata

Data integration & quality management

  • Azure Data Factory
  • Azure Functions
  • Azure Event Grid
  • Azure Event Hubs
  • Microsoft SQL Server Integration Services
  • AWS Glue
  • AWS Data Exchange
  • Amazon MWAA
  • Talend Data Fabric
  • Informatica PowerCenter
  • IBM InfoSphere Information Server
  • Anypoint Platform
  • Striim
  • SnapLogic iPaaS platform
  • Oracle Data Integrator
  • Apache NiFi
  • Syncsort
  • Pentaho
  • Fivetran
  • Matillion

Data security management

  • Azure Active Directory
  • Azure Firewall
  • Azure Key Vault
  • Azure VPN Gateway
  • AWS Cognito
  • AWS Secrets Manager

Data warehousing services FAQs

What is a data warehouse?

A data warehouse is a central repository that stores integrated data from multiple sources. Data warehouse is a core component of business intelligence designed to support BI activities like data analytics and reporting.

What are the key components of a data warehouse?

The key components of a typical data warehouse include a central database, ETL tools for data processing, and metadata repositories. Additionally, data warehouses can include data marts and OLAP cubes.

Why is data warehousing important for a business?

Data warehousing is crucial for businesses as it allows for the efficient consolidation, management, and analysis of large amounts of data from various sources, facilitating tactical decision-making and strategic planning based on comprehensive data insights.

How does data warehousing support business intelligence activities?

Data warehousing supports BI activities by providing a centralized source of structured data. This facilitates effective BI processes like data mining, analytical processing, and reporting.

Enterprise data warehousing:
architecture, types, best tools, and selection


Enterprise data warehousing: architecture, types, best tools, and selection

Learn more about the best enterprise data warehouse solutions, their capabilities, and benefits, and choose the optimal technology for your case.

Building a data warehouse: a step-by-step guide


Building a data warehouse: a step-by-step guide

We overview the process of building a data warehouse (DWH), including architectural approaches, key steps, talents required, software and best practices.

What is OLAP in a data warehouse?


What is OLAP in a data warehouse?

Learn about the role of online analytical processing OLAP in a data warehouse and how it helps organizations to improve their decision-making.

Real-time big data analytics: use cases and implementation options


Real-time big data analytics: use cases and implementation options

Take a look at our overview of the top use cases, benefits and implementation scenarios of real-time big data analytics.