Turn your scattered data into reliable, up-to-date and unified data flows.

We bring data from applications, databases, files, APIs and cloud services together in a shared structure. With ETL/ELT, real-time streaming, data validation and reconciliation mechanisms, we build integration solutions that deliver reliable data to reporting, analytics and operational processes.

Let's discuss your data needs Message us on WhatsApp
  • End-to-End Data Integration
  • Validated, consistent data
  • Scheduled and real-time flows
  • Traceable data quality
What Is Data Integration?

A data flow approach that combines data from different sources under shared quality, transformation and governance rules and makes it ready for use.

Data integration is more than moving records from one database to another. By addressing sources, data ownership, schema and format differences, quality rules, freshness needs and error scenarios together, it produces reliable data for operations, reporting and analytics.

Source and target connections

Bring data from applications, databases, files, APIs and cloud services into a shared flow through secure connectors and access methods.

ETL, ELT and data transformation

Transform data into the consistent structure the target system needs through extraction, cleansing, mapping, merging and loading steps.

Data quality and reconciliation

Identify missing, incorrect, duplicate and malformed records with validation rules, and compare source and target totals.

Orchestration and monitoring

Manage scheduled, event-driven or real-time pipelines with dependency, retry, log, alert and data lineage views.

Data Integration Scope

Manage the entire data flow lifecycle, from source connection to a reliable data product.

We treat data integration not as mere pipeline development but as a sustainable data capability in which source discovery, modeling, transformation, quality, security, orchestration and operations are designed together.

Data source and flow analysis

Make visible the systems where data is produced, existing transfers, owners, consumers and critical quality issues.

  • Source and target inventory
  • Data flow and lineage map
  • Freshness, volume and quality analysis

Source and target connectors

Integrate databases, APIs, files, applications and cloud platforms through secure, reusable connection components.

  • SQL and NoSQL databases
  • API, file and SaaS sources
  • Data warehouse, lake and lakehouse targets

ETL, ELT and transformation processes

Make raw data ready for use through cleansing, standardization, mapping, enrichment and merging steps.

  • Batch and incremental loading
  • Schema and format transformation
  • Business rules and data modeling

Real-time data flows

Move events and changed records with low latency to feed operations, notifications and analytics with current data.

  • Streaming and event pipelines
  • Change Data Capture
  • Queue, topic and consumer management

Data quality and governance

Identify missing, duplicate and invalid records with automated rules; build trust through data ownership, catalog and lineage.

  • Quality rules and profiling
  • Reconciliation and quarantine processes
  • Catalog, metadata and lineage

Orchestration and DataOps

Automate pipeline dependencies, scheduling, testing and deployment; monitor errors, latency and capacity centrally.

  • Workflow and dependency management
  • Data testing and CI/CD
  • Logs, alerts and reprocessing
Batch Data Integration or Real-Time Streaming?

The right approach depends on how quickly data needs to be updated and how latency affects the business.

A batch approach can be efficient for daily reporting, bulk reconciliation and large-volume historical loads. When you need instant operations, personalization, alerts or event processing, streaming and CDC-based flows deliver better results.

SCHEDULED FLOW

Batch and incremental integration

Suited to reporting and bulk processing needs where data can be updated on minute, hourly or daily schedules.

  • Large-volume bulk processingEfficient loading within a defined time window
  • Simpler operationsScheduled and traceable pipeline structure
  • Historical data supportEasy backfill and periodic recalculation
  • Data latencyA source change reaches the target only at the next run
Best suited for Reporting · Reconciliation · Historical load
REAL-TIME FLOW

Streaming and CDC integration

Designed for processes where source changes and events must reach analytical or operational systems within seconds.

  • Low data latencyDeliver events to consumers the moment they occur
  • Event-based automationTrigger processes and notifications with each change
  • Incremental data movementContinuously process only changed records
  • Real-time analyticsInstant dashboards, alerts and decision support
Best suited for Instant operations · Alerts · Personalization

Signs that you need real-time data integration

If several of the situations below apply, a streaming or CDC approach may be a better choice than scheduled loads alone.

  • Data latency affects operational decisions or the customer experience.
  • When a source changes, an instant action must start in other systems.
  • Alerts for risks, errors or anomalies are expected within seconds.
  • Large intraday bulk loads put strain on source systems.
  • The user experience needs to change based on current behavior and transaction data.
  • Changed records need to be tracked reliably and in order.
Data Integration Use Cases

Feed needs ranging from operational data to reporting and AI with reliable pipelines.

We tailor the data integration approach to your source systems, data volume, freshness expectations and consumption purpose. We address varied scenarios, from enterprise reporting to a unified customer view and from data migration to real-time analytics.

Unified data warehouse from ERP, CRM and finance data

Build a reliable reporting layer by combining customer, sales, inventory, cost and finance data from different systems under shared dimensions and metrics.

Data Warehouse ETL BI KPI

Unified customer data view

Create a single, current, enriched profile by matching customer records across CRM, sales, support, web and mobile channels.

Customer 360 Identity MDM

Moving data from a legacy system to a new platform

Profile, cleanse and transform legacy data, then migrate it safely to a new ERP, CRM or custom application with reconciliation checks.

Migration Cleanse Reconcile

Event and change streams

Process order, payment, device and user events through streaming pipelines to feed instant dashboards, alerts and automations.

Streaming CDC Events

Feeding data lakes and lakehouses

Combine structured or raw data from on-premises and cloud sources in scalable storage and processing layers.

Data Lake Lakehouse Cloud

Model and feature pipelines

Regularly feed machine learning models with clean, historized and reproducible training and prediction datasets.

ML Feature Training
Data Integration Process

A controlled working model that runs from data source discovery to validated, traceable production pipelines.

We don't start data integration by writing transfer code. We first clarify the meaning of the data, its ownership, consumption purpose, quality expectations and freshness needs; then we design and validate pipelines with measurable acceptance criteria.

  1. 01

    Source, consumer and data needs analysis

    We clarify the systems where data is produced, target users and processes, existing transfers, data owners and expected outputs.

  2. 02

    Data model and quality analysis

    By examining schemas, fields, formats, relationships and distributions, we identify missing, duplicate, invalid and inconsistent data issues.

  3. 03

    Target architecture and data model

    We design the batch, incremental or streaming operating model, along with layers, transformations, storage approach and lineage structure.

  4. 04

    Connectors, transformation and orchestration

    We develop source connections, data transformations, quality rules and pipeline dependencies in a modular, re-runnable way.

  5. 05

    Testing, reconciliation and go-live

    We test schema, transformation, quality, performance and error scenarios, compare source and target data, and bring the pipeline live in a controlled way.

  6. 06

    Monitoring and continuous data quality

    We monitor pipeline success, latency, record volume, schema changes and quality indicators to catch issues early.

Benefits of Data Integration

Speed up your decisions with consistent data, reduce manual preparation effort and scale your analytics processes.

Well-designed data flows reduce the time teams spend collecting and fixing data from different sources. Reporting, analytics, AI and operational processes are fed with current, reliable data that shares the same definitions.

A single, reliable view of data

Reduce data conflicts between teams by combining records from different sources under shared definitions and quality rules.

More up-to-date reporting

Deliver the data your reports and dashboards need without delay through scheduled or real-time pipelines.

Less manual data preparation

Automate file merging, copying, cleansing and checking so specialists can focus on analysis.

Measurable data quality

Automatically monitor completeness, accuracy, uniqueness and freshness rules to catch issues before they reach reports.

Analytics and AI readiness

Feed modeling and machine learning processes with clean, historized, explainable and reproducible datasets.

Scalable data platform

Bring new sources, consumers and data volumes into the system faster with standard connector and pipeline components.

Data Security and Governance

Protect data from its source to the point where it is consumed with access, encryption and lineage controls.

We don't see data security as just permissions in the target database. We design the entire lifecycle in a controlled way, from source connection to pipeline runtime identity, transport encryption to sensitive data masking, and retention policy to audit trail.

Identity and data access

Manage pipeline, service and user accounts with least privilege; restrict access to sources, layers, tables and fields by role. RBAC Service Identity MFA Least Privilege

Encryption and sensitive data protection

Encrypt data in transit and at rest; protect personal and sensitive fields with masking, tokenization or anonymization. TLS Encryption Masking Tokenization

Metadata, catalog and lineage

Strengthen discoverability and impact analysis by recording the meaning, owner, source and transformations of data. Catalog Metadata Lineage Ownership

Policy, retention and audit

Apply retention, deletion, usage and sharing rules by data class; keep access and pipeline operations auditable. Retention Audit Policy Compliance

Data Integration Deliverables

Not just working pipelines, but a complete data integration package your teams can manage with confidence.

We prepare the technical assets of the solution, from source and lineage maps to data models, from working ETL/ELT code to quality tests, and from security policies to monitoring dashboards and operations guides.

Data flow and target architecture

A design showing source, connector, processing, storage and consumption layers along with security boundaries, dependencies and operating models.

Data model and mapping document

A dictionary defining source and target schemas, field transformations, business rules, data ownership and sensitivity classes.

Working ETL/ELT and streaming pipelines

Modular, version-controlled data flows covering connector, ingestion, transformation, quality, loading and orchestration steps.

Data quality and reconciliation package

Completeness, accuracy, uniqueness, format and freshness tests; source-to-target comparisons and quarantine rules.

Metadata, lineage and security definitions

Governance assets including the data catalog, ownership, lineage, access, masking, retention and audit policies.

DataOps and operations package

An operations set including dashboard, alert, log, SLA, retry, replay, backfill, schema change and incident management steps.

Frequently asked questions

Frequently asked questions about Data Integration

The most common questions about timelines, costs, working model and support afterwards.

Send your question
What is data integration?

A solution that combines data from different applications, databases, files, APIs and cloud sources into a shared, ready-to-use structure through extraction, transformation, validation and loading processes.

How do you start a data integration project?

First the data sources, target consumers, use cases, ownership, data volume, freshness and quality expectations are determined. Then the source data is profiled and the target architecture is designed.

How long does data integration take?

The duration varies with the number of sources and targets, data volume, schema and transformation complexity, quality issues, historical loading, security and freshness needs.

How is the cost of data integration determined?

Cost depends on the number of connectors and pipelines, data volume and frequency, transformation and quality rules, batch or streaming architecture, platform, security, testing and operations support.

What is the difference between ETL and ELT?

In the ETL approach, data is transformed before being loaded into the target. In the ELT approach, raw data is first loaded into a scalable target platform and transformations are performed there. The choice depends on the source, platform, volume and governance needs.

Does every data flow need to be real-time?

No. Freshness needs should be determined by business impact. Batch may be enough for daily reporting, while instant operations, alerts or personalization may require streaming or CDC.

Can data be extracted from legacy systems?

In most cases, yes. Database, file output, API, report or custom connector options are reviewed, and a secure extraction method that doesn't overload the source system is designed.

Do you provide maintenance and support after go-live?

Yes. Depending on scope, we can provide support for pipeline monitoring, error response, data reconciliation, schema changes, adding new sources, and performance and cost optimization.

Related services
System Integration

Build secure, traceable transaction flows between ERP, CRM, finance and operations systems.

Learn more API Integration

Develop production-ready connections with identity, quota, data transformation, retry and monitoring mechanisms.

Learn more AI Consulting

Combine model development, feature preparation, prediction and production monitoring processes with your data strategy.

Learn more Digital Transformation Consulting

Manage data sources, technology priorities and organizational capabilities in a shared transformation roadmap.

Learn more Cloud Infrastructure Consulting

Balance performance, security, continuity and cost requirements with a shared cloud governance model.

Learn more Maintenance & Support Services

Manage pipeline, schema, quality, capacity and source changes with regular maintenance and operations support.

Learn more
Free intro call

Let's bring your scattered data together in reliable, ready-to-use flows.

Let's review your data sources, quality issues and reporting or analytics goals together. We'll pin down the right pipeline approach, the priority data flow and a practical first step.

Tell us about your project Message us on WhatsApp
Service area
Across Türkiye, on site and remote