Our Data Services
Our data services are structured to address key business challenges in assessment, implementation, and R&D. By organizing the offerings clearly, businesses can easily identify the right entry point and understand what’s required to achieve their data goals.
End-to-End Data Development Service
We provide complete data infrastructure development from scratch or rebuild outdated systems to meet new needs:
- Assessment & Consulting: Business understanding, feasibility studies, roadmap design, capability building.
- Implementation: Data acquisition, cleansing, ETL/ELT pipelines, warehouse/lakehouse setup, BI, cloud/on-premise deployment, monitoring & support.
- R&D: Advanced analytics, data optimization, PoC development, and ongoing evaluation.
Data Migration as a Service (DMaaS)
We move your data securely and strategically to modern platforms:
- Assessment: Evaluate current environment and define migration scope.
- Design & PoC: Map conversion patterns, run PoC, refine migration plans.
- Migration: Execute with automation tools to reduce time and errors.
- Managed Support: Ensure post-migration system stability and readiness for scaling.
Big Data Platforms & Optimization
We integrate and optimize modern big data ecosystems to support scalable performance and insight generation:
- Platforms include AWS, Google Cloud, Azure, Snowflake, Databricks, and Datadog.
- We support ingestion, storage, processing, monitoring, and analytics for large-scale enterprise data systems.
With NTQ’s structured approach to data services, businesses don’t just migrate or store data – they activate it. Whether you’re building from scratch, modernizing legacy systems, or scaling with cloud-native tools, our end-to-end services empower you to make faster, insight-driven decisions. From assessment and architecture to ongoing support, we ensure every stage of your data journey is secure, scalable, and built for long-term growth.
Let’s discuss how we can tailor a solution that gives your business a strategic edge – a conversation worth having.
Compare Our Data Services
By comparing your current needs with the capabilities of each service, you can avoid over-investing in unnecessary features or underestimating future demands. This clarity allows you to make informed, scalable decisions and adjust quickly as your data strategy evolves.
| Criteria / Service | End-to-End Data Development | Data Migration (DMaaS) | Big Data Platform |
| Assessment & Roadmap Design |  |
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| Data Cleansing & Enrichment | |
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| Migration from Legacy Systems | |
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| Data Pipeline Implementation | |
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| Real-time & Large-scale Analytics | |
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| Cloud-native Deployment | |
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| Post-migration Maintenance & Support | |
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Data Delivery Process
Our data delivery follows a practical, step-by-step approach, giving you visibility and control throughout each phase.
1
Audit & Discovery
Understanding your data situation, including what’s working and what’s not, is the first step. We take time to map the full picture, because in data, what’s missing often matters just as much as what’s there.
2
Design the Flow
A well-designed data flow reflects how your business thinks and operates. Whether it’s batch processing with ETL or real-time streaming, we focus on building structures that make sense for how your teams actually use and move data every day.
3
Clean & Align the Dataset
This step ensures the data is structured in a way that matches the system’s logic and the use case it supports. We resolve data quality issues so everything flows smoothly and delivers accurate results.
4
Validation & Simulation
We test the full logic using production-like datasets to verify completeness and business alignment. Validations are often run through orchestrators like Airflow or custom testing pipelines before any go-live.
5
Deployment & Business Integration
Going live means activating pipelines and setting up monitoring, while making sure that outputs like dashboards or models are embedded into daily operations. The goal isn’t deployment for its own sake, but turning data into something teams can actually use.
Why Choose NTQ Europe for Data Service
At NTQ Europe, we don’t just deliver pipelines and dashboards; we build systems that make data truly useful for your teams.
Business-Centric Data Logic
We design data pipelines and systems that reflect how your business actually operates. This ensures every output, dashboard, or model aligns with real workflows and decision-making processes.
Secure, Scalable Delivery
Your data stays protected with GDPR-compliant practices, secure pipelines, and strict access control. Whether on-premise or cloud, we build with both performance and privacy in mind.
Local Expertise with Offshore Efficiency
With a consulting team based in Europe and a delivery team in Vietnam, we bring hands-on collaboration and scalable implementation; delivering enterprise-grade results at a competitive cost.
NTQ Europe combines strong data engineering practices with flexible delivery models and domain-specific insight. From cloud-native data lakes to enterprise-grade pipelines and visualization tools, we help you unlock value from data you already have; and build the foundation for what’s next. Our teams are built to scale with you, delivering long-term results with speed, clarity, and security.
End-to-End Data Services to Drive Business Growth
Our Data Capabilities
Working with data isn’t just about tools, it’s about how you approach structure, quality, and long-term usability
Data Ingestion & Pipeline
- Apache Airflow
- AWS Glue
- Azure Data Factory
- Kafka
- Fivetran
- DBT
Storage & Warehousing
- Amazon Redshift
- Snowflake
- BigQuery
- Azure Synapse
- PostgreSQL
- Delta Lake
Processing & Lakehouse
- Databricks
- Apache Spark
- Microsoft Fabric
Advanced Analytics Framework & Library
- TensorFlow
- PyTorch
- Keras
- OpenCV
- Scikit-learn
- SpaCy
Monitoring & Observability
- Datadog
- Prometheus
- Grafana
Data Analysis
- SAS
- Pandas
- Seaborn
- Matplotlib
- Dask
- Plotly
Business Intelligence
- Tableau
- Superset
- Power BI
- Amazon QuickSight
Advanced Analytics Framework & Library
- TensorFlow
- PyTorch
- Keras
- OpenCV
- Scikit-learn
- SpaCy
Data Engineering
- Apache Spark
- Apache Kafka
- Apache Airflow
- MongoDB
- ElasticSearch
- MySQL
Deployment
- AWS
- Azure
- Google Cloud
- Docker
- Kubernetes
- Streamlit
- FastAPI
Our Success Stories
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FAQs
Can you still work with fragmented or messy data?
Absolutely. In fact, that’s the reality for most businesses we work with and rarely is data clean or well-structured from the start. We typically handle this by cleaning and deduplicating records, mapping inconsistent schemas across sources, or even reconstructing missing logic based on available patterns. These steps are already built into our delivery process, so you won’t face extra charges.
Is your SaaS solution customizable or off-the-shelf?
We offer fully managed SaaS platforms that are customizable at key levels. While the infrastructure and core functionality are pre-built for speed, we tailor the configuration to fit your business context. This hybrid approach gives you the benefit of fast deployment without compromising flexibility. You get a solution that feels purpose-built, but without the time and cost of building it from scratch.
How do you ensure data security throughout the project?
We secure your data across all layers using a multi-layered approach. This includes three key practices: encrypted storage and transmission to protect sensitive content, strict role-based access control to limit exposure, and audit logging to track all activity throughout the project. We also follow regional compliance standards and conduct internal reviews at every stage.
How long does a typical project take?
Project duration depends on the service and complexity, but we always provide a clear timeline before starting. A focused data migration may take 3 to 6 weeks, while a full end-to-end data platform can run from 2 to 5 months. SaaS onboarding is typically quicker, often within 1 to 3 weeks. With our in-house data team ready to deploy, we minimize ramp-up time and keep delivery on track from day one.
Which industries benefit most from your data services?
Industries with large amounts of data such as finance, healthcare, retail, and logistics often see strong returns from data services because their operations depend heavily on timely, accurate insights. That said, even sectors with modest data footprints can benefit. For instance, an education provider using basic learner activity data to adjust course content in real time can drive measurable improvement in engagement. The advantage doesn’t come from volume alone, but from how intentionally the data is used.
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