Data warehousing services

  • Drive business analytics and improve marketing strategies with instant access to required datasets via a reliable data repository

  • Ensure stable service delivery, as your business grows with a well-integrated and scalable data storage solution that handles increasing volumes of data

  • Maintain your customer trust by storing critical business data in a consolidated and industry-compliant single source of truth

  • Get timely and relevant business insights by configuring your BI and advanced analytics tools to their maximum potential

  • Become a data-centric organization by following a custom cross-company data management and analytics strategy

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Real-life results of setting up a data warehouse

  • UP TO 70%

    faster time-to-insight

  • UP TO 40%

    decrease in operational costs

  • UP TO 95%

    increase in data accessibility

  • UP TO 80%

    drop in data reporting errors

Data warehousing services Yalantis provides

A custom DWH solution lets you adapt seamlessly to market changes. With a reliable data engineering partner, you can scale your data acquisition and processing power from terabytes to petabytes—without compromising performance, data integrity, security, or cost efficiency.

  • Data warehouse design and implementation

    • Gathering business requirements

    • Choosing a data warehouse model

    • Setting up data architecture

    • Identifying data sources to ingest in a centralized data storage

  • ETL/ELT development

    • Defining suitable ETL/ELT tools

    • Rolling out custom data processing infrastructure

    • Ensuring data transformation in a server or data warehouse

    • Monitoring ETL/ELT pipelines

  • Cloud migration services

    • Defining migration scope

    • Choosing a cloud migration strategy

    • Selecting the appropriate cloud environment

    • Monitoring the migration process to ensure security

  • Data integration

    • Discovering and connecting data sources

    • Cataloging data

    • Preparing and processing raw data

    • Managing data quality

    • Direct data querying

  • Advanced analytics

    • Setting up advanced analytics tools

    • Enabling predictive and prescriptive analytics

    • Applying AI/ML models for data analytics

    • IoT analytics

    • Data visualization and reporting

  • Data governance and compliance support

    • Implementing standards for data collection, processing, storage, and analysis

    • Following industry-specific regulatory and legal requirements

    • Tracking data flows and usage

    • Enforcing policies to organize and secure data in the cloud

    • Minimizing data management risks

  • Performance optimization and scalability

    • Optimizing data querying

    • Increasing the speed of data retrieval

    • Applying partitioning techniques

    • Enabling high adaptability and availability of a data warehouse

  • Post-deployment support and training

    • Data warehouse support and management

    • Implementing security controls

    • Fine-tuning data analytics tools

    • Composing training documentation

    • Ongoing data management and analytics consultations

Scale data analytics capabilities

Roll out a cloud-based data warehouse to analyze large volumes of critical data without dependence on hardware and software components.

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Data warehousing use cases across industries

  • Finance

    • Compliance reporting

    • Fraud detection

    • Risk management

    • Creditworthiness analysis

    • Efficient investment decision-making

    • Tracking customer behavior to cross-sell or upsell services

  • Healthcare

    • Patient care optimization with predictive analytics

    • Medical imaging analysis

    • Data aggregation for clinical trials

    • Bed occupancy rate improvement

    • Tracking treatment outcomes

    • Personalized patient care

  • Supply chain

    • Shipment monitoring

    • Delivery routes optimization

    • Order fulfillment optimization

    • Supplier performance analysis

    • Demand prediction

    • Optimization of inventory management

    • Enhanced operational efficiency

  • Manufacturing

    • Equipment failure prediction

    • Predictive maintenance

    • Targeted product development

    • Production line quality control

    • Production schedule analysis

    • Equipment investment optimization

    • Waste reduction

    • Downtime prevention

Get a data warehouse that fits your budget and goals

Built with industry compliance in mind, optimized for your digital transformation initiatives.

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Yalantis: A company with a proven portfolio

Data-driven decision management for food manufacturer

  • Data migration from manual files into a data warehouse

  • Efficient decision-making with quick access to accurate busines data

  • Future-proofing business initiatives with AI/ML implementation

Data lifecycle management in food manufacturing logo

See full case study

Breaking data silos for the radiology department

  • Automated ETL engine for data extraction and transformation

  • Data warehouse for storage and processing

  • Unified disparate clinical and patient data

  • Configured Tableau BI tool for data analytics

BI for a radiology department logo

See full case study

Custom data strategy for mobile banking provider Oxygen

  • Scaled data infrastructure for streamlined decision-making

  • Improved business reporting with optimized data workflow monitoring and management

  • Data governance framework to maintain data quality and security

Data strategy

See full case study

Scalable data lake solution for a manufacturing company

  • File exchange process between on-premises systems and a data lake

  • Automated ETL engine

  • Server-side and client-side encryption for secure data access and retrieval

Data lake for a manufacturing company logo

See full case study

Data Modernisation:

Unlock your business data’s potential with a modern, cost-effective system that makes data accessible, actionable, and easy for your team to embrace.

Read whitepaper

Insights from an experienced data warehousing company

How to

Central repository for your data: data mart, data warehouse, or data lake?

Learn the differences between a data warehouse, a data mart, and a data lake and pick the right data repository to cover your industry-specific needs.

Data warehouse
How to

How to develop an enterprise data warehouse from scratch to foster a data-driven culture

Get a set of clear steps to develop a domain-driven EDW design; learn about integral elements of an EDW and key stages of its development.

How to

An enterprise data governance framework: Solve your biggest challenges with data security, quality, compliance

Is data chaos costing your business growth and innovation? Discover how to tackle data challenges around security, consistency, quality, and compliance with a single solution.

FAQ

How long does it take to implement a data warehouse?

A data warehouse implementation process is incremental. It includes:

  • gathering your functional, non-functional, and customization requirements
  • data infrastructure assessment
  • evaluation of hardware and software components
  • selection of suitable data sources
  • designing a data warehouse schema and architecture
  • deployment and post-deployment support

Depending on your company’s current level of digital maturity, this process can range from a few months to a year. However, cooperating with a data warehousing company that uses modern data engineering methodologies and cloud-based solutions can significantly expedite the implementation of your custom data warehouse.

How do you as a data warehouse company ensure the security of a data warehouse?

Data security in a data warehouse is critical, as it consolidates data from multiple sources. If compromised, the whole enterprise is at risk. To avoid data breaches, our data warehousing services team enables role-based access to the data warehouse with well-defined system and object privileges. We also keep records of user activities to track any unplanned data modifications. Thus, each department can view only the datasets needed for their work. Our data engineering team also follows required data privacy laws, regulations, and rules (GDPR, HIPAA, PCI DSS) to maintain data security in the data warehouse.

How do you ensure the data warehouse integration with our existing infrastructure?

If you decide to roll out a cloud-based data warehouse, we provide streamlined data warehouse services and use modern data integration tools. Depending on your data analytics needs, you can decide which data stays on your on-premises databases and which is loaded into a data warehouse. Such methods as data migration, data replication, ETL/ELT processes, and data virtualization enable unified data analytics across on-premises and cloud environments. We also ensure change data capture (CDC) mechanisms to maintain consistency across databases and data repositories.

What kind of support do you offer after implementation that differentiates your company from other data warehouse service providers?

Yalantis provides regular audit sessions to ensure data warehouse performance fulfills your business needs. Our team is proactive and knows the latest trends in the data management industry. Thus, we can offer you timely recommendations to lower data warehouse maintenance costs. We can also conduct workshops and knowledge sessions to help your team adjust to the new data workflow.

 

Plus, Yalantis provides professional cybersecurity services with an established security center of excellence. We help you maintain a secure data infrastructure for years to come. For one of our clients in the financial industry, we have developed a custom cybersecurity ecosystem for automated detection and management of software vulnerabilities.

Core components of a data warehouse to efficiently analyze data and drive gradual business growth

Before investing in data warehouse solution development, an organization should prioritize their business requirements and tactical and strategic goals. For instance, you could develop an enterprise data warehouse as a single source of truth. The main value of an enterprise data warehouse solution is data availability for cross-company analytics.

To be well-prepared for the implementation process, businesses should understand the core stages and components of data warehousing services.

Data warehousing fuel: Defining and analyzing data sources

Once you’ve established your goals for DWH development, the next step would be identifying and connecting to the relevant data sources. The number of sources depends on your needs. For an enterprise data warehouse, you’ll need to connect to all of the available systems at your organization to feed a data warehouse with the maximum amount of data. For department-specific data analysis, data warehouses can collect and store data from fewer sources.

When analyzing data sources, we define data types, data volume, data sensitivity levels, and data quality. After that, it’s critical to clean and transform the data if necessary.

ETL/ELT processes: Streamlining raw data flow

ETL and ELT are crucial data processing approaches that enable automated and timely data ingestion into data warehouses.

  • ETL: Following this approach, data engineers build pipelines that:
    • extract data from sources
    • transform datasets on a secondary processing server
    • load transformed datasets into a data warehouse

ETL pipelines support structured data in the form of rows and columns.

  • ELT: These pipelines:
    • extract data from relevant sources
    • load raw data straight into a data warehouse
    • transform those datasets whenever necessary for data analytics

An ELT process can handle all types of data including unstructured, semi-structured, and structured which is best for fulfilling wide enterprise data warehouse services.

Data storage: Data warehouse cloud services vs on-premises data warehouses

For reliable data storage, you can opt for a cloud-based or on-premises data warehouse. To make the right choice, you should take into account:

  • industry requirements
  • budget constraints
  • customization needs

With an on-premise data warehousing service, you have full control over your hardware and software upgrades, system availability, and scaling capabilities. It’s a plus if such a level of ownership is a must for your organization but an on-premises infrastructure requires significant upfront and ongoing expenses.

On the contrary, data warehouse service providers in the cloud (Amazon Redshift, Google BigQuery, and Snowflake) offer pay-as-you-go pricing models with flexible and fully managed functional and scalability opportunities.

Analytics and BI tools to put data into action

Extracting business data and storing it without further analysis is meaningless. Collecting relevant data and analyzing it to discover how your business performs, what customers expect the most from your services, and whether you meet your KPIs and goals is what justifies cooperation with a data warehouse services team. With the help of data integration tools, business intelligence and advanced analytics solutions, you can create insightful dashboards, detailed reports, interactive visualizations, and predictive systems.

  • Business intelligence tools: Tools like Tableau, Power BI, and Looker are common options for business-specific data analytics. Yalantis data/BI engineers are experienced in optimizing these tools to fit your business needs.
  • Advanced analytics tools: AI/ML models enable data scientists to perform complex data mining and statistical analysis.

Data governance: Ensuring continuous data quality and security in an on-premises or cloud data warehouse solution

Yalantis follows clear policies for maintaining and monitoring data quality, security, privacy, and compliance to ensure the long-term reliability and trustworthiness of your data warehouse. We set up an entire data lifecycle management flow which involves:

  • Data quality management: Maintaining data quality in a data warehouse is an ongoing process. It includes continuous validation of whether data is complete, consistent, unique, timely, and relevant. We conduct various data tests to define:
    • NULL values
    • Referential integrity
    • Numeric distribution
    • Uniqueness
    • Volume
  • Data security: We maintain the required level of security and data privacy with regular security audits and system component updates. Our OpSec team helps you minimize system risks by implementing security controls and best practices at all SDLC stages.
  • Data compliance: Our data warehouse services team ensures ongoing compliance checks with industry security requirements and provides a roadmap to ensure high data security standards.

How to build a successful data warehouse: Final tips

  • Focus on use cases: Define specific business issues you want to target with data analytics based on data warehouses. Such issues can be:
    • improving patient outcomes
    • effective fraud detection in financial transactions
    • achieving high supply chain efficiency

If you focus on what’s critical for your business, you can timely define root causes and compose winning improvement strategies.

  • Scalable design: A modular approach (data marts or layers) during data warehousing solution development allows for incremental growth without rearchitecting the entire system once your business needs change.
  • Optimized performance: Implement data partitioning, indexing, and compression techniques to speed up queries and reduce data warehouse storage costs. Our data warehouse services team develops cost-efficient custom data solutions, using the right tools and performance optimization strategies.
  • Clear data ownership: Assign clear responsibilities for data quality, accuracy, and security within your in-house team or together with your third-party software development partner.
  • Enterprise-wide data literacy: Train your employees to understand data concepts, tools, and techniques. Help them understand and interpret data insights to use to their advantage and improve performance.
  • Custom dashboards: Tailor visualizations to each stakeholder’s needs—C-suite executives, analysts, and frontline managers.
  • Emerging technologies: Stay prepared to integrate AI/ML for predictive analytics and other advanced capabilities. Facilitating data science projects can help your business stand out from the competition and effectively plan for the future.

Building successful data warehouses is about more than just technology—it’s about creating a system that aligns with your business goals, adapts to growth, and delivers value to all stakeholders. Whether you’re improving patient care, optimizing financial operations, or streamlining manufacturing processes, a well-designed data warehouse is the foundation for data-driven success. Invest in the right tools, plan for future needs, and keep your users at the center of the process to ensure long-term value and adoption.

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    Lisa Panchenko

    Senior Engagement Manager

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