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Data Integration Services
- · 19 years in data integration and warehousing
- · 1,200 clients from 70+ countries
- · 11+ years of Azure and AWS experience
Data integration services imply the consolidation of multi-source enterprise data in a centralized storage optimized for high data accuracy and availability to enable smooth business operation, comprehensive analytics, and 100% regulatory compliance.
With 19 years of experience in data integration and an in-house PMO for large-scale and complex projects, ScienceSoft offers comprehensive data integration services covering:
Technology and business consulting for data integration.
Designing databases, data warehouses, ETL/ELT pipelines.
Implementing off-the-shelf data integration solutions.
Developing custom integration software components and APIs.
Designing data management frameworks and policies.
Data security and regulatory compliance services.
Maintenance and support for data integration systems.
Modernizing and troubleshooting legacy integration systems.
Data Integration Tools and Platforms We Are Proficient At
ScienceSoft’s Portfolio: Our Data Integration Success Stories
Our Clients Share Their Experience Working with ScienceSoft
The team was always responsive to all our questions, and we truly appreciated such a collaborative approach.
They offered a simple, yet well-functioning, way to integrate our financial portal with the data management and accounting systems, which allowed us not to get stuck with an overcomplicated data synchronization process.
ScienceSoft's team members proved to be extremely flexible and responsive.
The invoicing system created by ScienceSoft automates data integration from different sources, invoice generation, and provides visibility into the invoicing process. We have already engaged ScienceSoft in supporting the solution and would definitely consider ScienceSoft as an IT vendor in the future.
What stood out the most was ScienceSoft’s holistic approach throughout the projects.
They consistently focused on the bigger picture, offering centralized solutions rather than just resolving isolated issues with our existing tools. Moreover, both software systems they delivered were designed with scalability in mind, ensuring seamless upgrades as our company grows.
Choose Your Preferred Service Option
Data integration consulting
We can help you optimize your existing data infrastructure or build an efficient integration system from scratch. Our data engineers will design a flexible and cost-efficient solution architecture, recommend the optimal tools for it, and develop a secure and compliant data management framework.
Data integration implementation
We can set up and customize your chosen data integration platform, develop custom software components (e.g., ETL pipelines, APIs), or implement a comprehensive data warehousing and analytics system following your unique requirements.
Data integration support
We can maintain and troubleshoot your data integration solution to ensure its long-term stability or evolve it according to the emerging requirements. We can also monitor and streamline your data quality management processes to ensure ongoing data accuracy and consistency.
Data Integration Strategies We Offer: What’s Best for You?
By integration method
ETL
Essence: data is extracted (E) from the source systems, transformed (T) according to the data model, and loaded (L) into a centralized storage like a database or a data warehouse.
Best for: batch processing and structured data (e.g., a sales reporting solution that downloads fresh CSV and JSON files from several enterprise systems every 24 hours).
ScienceSoft's best practices for optimizing ETL
ELT
Essence: data is extracted (E) from the source systems, loaded (L) to the centralized storage in the raw format, and transformed (T) afterward.
Best for: real-time and big data processing (e.g., integrating an ecommerce website with internal OMS and inventory to accurately reflect live product availability and offer personalized suggestions based on user behavior and purchase history).
ScienceSoft's best practices for optimizing ELT
Data virtualization
Essence: creating a virtual database that offers a unified view of several disparate databases without physically moving or copying the data.
Best for: viewing integrated data from distributed internal and external systems that can’t be physically integrated (e.g., a virtual database enabling healthcare providers to access patient records from several partnering hospital systems).
ScienceSoft's best practices for optimizing data virtualization
Data propagation
Essence: unlike ETL/ELT, this method doesn’t process entire datasets. It only identifies changes in the source data and transmits them to the target systems to ensure real-time synchronization between platforms.
Best for: dynamic datasets that require low latency and real-time consistency (e.g., communicating financial transactions across banking and payment systems).
ScienceSoft's best practices for optimizing data propagation
By deployment model
On-premises
Best for:
- Legacy systems that can’t be efficiently integrated within a cloud environment.
- Organizations that avoid third-party data hosting (e.g., healthcare, defense, public sector).
What you pay for: one-time data integration software license (e.g., Informatica PowerCenter, Oracle Data Integrator); on-premises hardware; in-house or outsourced IT staff for solution implementation and maintenance.
Common challenge: low scalability (the need to add physical servers to handle increasing load or data volume).
How we solve it: we parallelize data integration tasks across multiple servers to enable processing power to increase as the data volume grows. We also use solid-state drives and in-memory caching to minimize data access latency.
Cloud
Best for:
- Accommodating constant data volume growth.
- Easy data access for distributed teams.
- Using integrated data on cloud-based data analytics, machine learning, and AI platforms.
What you pay for: integration platform subscription fees (e.g., AWS Glue, Azure Data Factory) based on the number of users and available features; cloud resource consumption fees; in-house or outsourced IT staff for solution implementation and maintenance.
Common challenge: cloud vendor lock-in.
How we solve it: we use common data storage formats (e.g., JSON, XML) to ensure smooth portability across different cloud platforms. We also apply open-source data integration tools to avoid dependence on a certain cloud ecosystem.
Hybrid (cloud + on-premises)
Best for:
- Companies with on-premises solutions that plan a gradual transition to the cloud.
- Organizations that want to maintain some of their data on-premises due to security or regulatory requirements.
What you pay for: the costs incurred by on-premises and cloud parts of the solution; in-house or outsourced IT staff for solution implementation and maintenance.
Common challenge: poor data consistency across cloud and on-premises data sources.
How we solve it: we implement data replication from on-premises to cloud databases and use data synchronization for continuous change updates across the environments.
By data storage type
Data warehouse (DWH)
Stores highly structured data optimized for BI querying and reporting.
Data lake
Stores raw data in its initial format until it is processed and uploaded to DWH. A data lake can also be used without a DWH to accumulate multi-source data in one place.
Data hub
A hybrid solution combining data lake and DWH capabilities to collect and view data in various formats.
A common concern for cloud-based integrations is data security. In this context, each deployment model calls for case-specific security measures. For instance, cloud-only deployment can pose sensitive data exposure risks because of inefficient tenant segregation within the cloud. The issue can be addressed by implementing robust isolation mechanisms like virtual private clouds or dedicated instances. Within the hybrid model, the challenge is usually about managing user identities and access across environments. A single sign-on solution can be the cure for the case, allowing users to access multiple systems with a single set of credentials.
How We Guarantee Quality Data Integration and Predictable Project Flow
Driving project success regardless of time and budget constraints, as well as changing requirements, is ScienceSoft's #1 priority.
Extensive collaboration with stakeholders
We use tailored collaboration forms to gather input from stakeholders at different organizational levels. This ensures that the integration meets their expectations, including software performance and its impact on business profitability.
Meticulous project scoping
We take time to accurately assess the scope of work and the needed resources at the start, making it easier to deliver predictable results without delays or budget overruns.
Accurate cost estimation
We are always upfront about the required investments and provide detailed cost breakdowns to make sure you know exactly what you’re paying for at each stage of the integration project.
Mature risk management
We plan and execute our projects following a detailed and continuously updated risk mitigation plan. We assess the risks of every new initiative and immediately inform our clients about emerging threats.
Steady focus on business goals
We develop tailored KPIs for project health and product quality and use them as success benchmarks to drive each project to its business goals despite budget and time constraints and changing requirements.
Comprehensive project reporting
We consistently update our clients on the project status, achievements, and issues to ensure full transparency and accountability of our teams. We tailor the reporting format and frequency to the client’s preferences to avoid under- or over-communication.
Controlled change management
We analyze the feasibility and cost-benefit ratio of each change request to accommodate shifting business requirements while minimizing scope creep.
Exhaustive software documentation
We create comprehensive, clear, and concise software documentation at all project stages to support smooth software maintenance and compliant reporting to the authorities.
Centralized knowledge management
We build a centralized project knowledge base and share it with our clients to have a single point of truth and avoid the bus factor throughout the data integration project.
Technologies We Work With
Data Integration Costs
Data integration costs may vary from $70,000 to $1,000,000, depending on the project scope. Below, we present ballpark estimates for integrating multi-source enterprise data (e.g., from CRM, ERP, SCM, and accounting solutions) into an analytics-ready data warehouse.*
$70,000–$200,000
For small companies.
$200,000–$400,000
For midsize companies.
$400,000–$1,000,000
For large companies.
*Software license fees are not included.
How Data Integration Translates to Business Efficiency: Real Examples from ScienceSoft’s Experience
100x faster processing of BI queries
due to efficient integration of 1000+ raw data types.
