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Data Integration Services

  • · 19 years in data integration and warehousing
  • · 1,200 clients from 70+ countries
  • · 10+ years of Azure and AWS experience
Data Integration Services - ScienceSoft
Data Integration Services - ScienceSoft

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

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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.

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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.

Go for implementation

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.

Go for support

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).

ETL Integration Method - ScienceSoft

ScienceSoft's best practices for optimizing ETL

ScienceSoft's best practices for optimizing ETL:

  • Using incremental data extraction instead of retrieving the full dataset on each ETL run. Such mechanisms help minimize the burden on the source systems and avoid performance issues.
  • Optimizing loading mechanisms (e.g., using compression or encoding to minimize the size of the files being transferred to reduce computing resource consumption and solution operating costs).

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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).

ELT Integration Method - ScienceSoft

ScienceSoft's best practices for optimizing ELT

ScienceSoft's best practices for optimizing ELT:

  • Optimizing queries for massively parallel processing (MPP) and implementing data partitioning to improve the system’s performance, resulting in faster processing times.
  • Writing tailored functions and stored procedures to enable custom transformation logic within the target system and adapt the data to unique business requirements, including security and compliance ones.

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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).

Data Virtualization Integration Method - ScienceSoft

ScienceSoft's best practices for optimizing data virtualization

ScienceSoft's best practices for optimizing data virtualization:

  • Using data federation mechanisms to present multi-source, multi-format raw data in a single format and avoid data heterogeneity issues (e.g., geographical, currency, and temporal data variations, terminology discrepancies).
  • Implementing data materialization (e.g., creating physical tables to store the results of complex queries) to increase the speed of BI query execution.
  • Caching frequently used data to avoid constantly retrieving it from the sources and improve system performance.

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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).

Data Propagation Integration Method - ScienceSoft

ScienceSoft's best practices for optimizing data propagation

ScienceSoft's best practices for optimizing data propagation:

  • Implementing change data capture (CDC) mechanisms (e.g., log-based change data capture, API-driven capture, database triggers) within the source systems to ensure timely data updates in the target systems.
  • Utilizing case-specific mechanisms for efficient change propagation, including message queues, replication tools, streaming platforms, and direct API integrations.

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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.

Senior Solution & Integration Architect, ScienceSoft

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

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.

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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.

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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.

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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.

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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.

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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.

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Controlled change management

We analyze the feasibility and cost-benefit ratio of each change request to accommodate shifting business requirements while minimizing scope creep.

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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.

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Centralized knowledge management

We build a centralized project knowledge base and share it with our customers to have a single point of truth and avoid the bus factor throughout the data integration project.

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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.

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90% faster report preparation

thanks to the availability of BI-ready data.

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80% reduction in cloud costs

due to optimal cloud service configurations.

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Unlock These and Other Benefits of Data Integration for Your Business

Let’s discuss your data integration challenge — our experts would be glad to share their experience from similar projects to see how we can tackle your unique case.

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