Data Engineering & Analytics
- Batch and real-time pipelines that just run
- Lakehouse and warehouse design on Snowflake, Databricks and BigQuery
- dbt-powered, tested and documented transformations
- Data quality and governance built in
Why Data Engineering with VN Softverg
Every dashboard, forecast and AI model depends on one thing: data that is complete, accurate and on time. Yet most businesses have data spread across ERPs, CRMs, spreadsheets, SaaS tools and operational databases, each with its own definitions.
Our data engineers design and build modern data platforms that bring it all together. We ingest data with Fivetran, Airbyte, Kafka or custom connectors, transform it with dbt and Spark, orchestrate with Airflow or Dagster, and store it in warehouses and lakehouses such as Snowflake, Databricks, BigQuery, Redshift or Microsoft Fabric.
Data quality checks, lineage, access control and documentation are part of the build, not an afterthought, so analysts, data scientists and business users can rely on the numbers.
Everything you need, under one roof
Data Platform Architecture
Choose and design the right warehouse or lakehouse, with medallion layers, storage formats like Delta Lake or Apache Iceberg, and a clear cost model.
- Platform architecture blueprint
- Technology selection matrix
- Cost and scaling model
Data Ingestion & Integration
Reliable connectors from databases, SaaS apps, APIs and files using Fivetran, Airbyte, CDC with Debezium, or custom Python pipelines.
- Source-to-target mapping
- Ingestion pipelines
- Change data capture setup
Transformation & Modeling
Modular, version-controlled transformations with dbt or Spark, and dimensional models that reflect how your business actually works.
- dbt project and models
- Dimensional data model
- Business metric definitions
Real-Time Streaming
Event streaming with Apache Kafka, Amazon Kinesis or Azure Event Hubs and stream processing with Spark Structured Streaming or Flink for live use cases.
- Streaming architecture
- Topic and schema design
- Real-time processing jobs
Orchestration & DataOps
Scheduled, monitored and alerting pipelines with Airflow, Dagster or Azure Data Factory, plus CI/CD for data code and environment promotion.
- Orchestration DAGs
- Pipeline monitoring and alerts
- Data CI/CD workflow
Data Quality & Governance
Automated tests with dbt tests or Great Expectations, lineage, cataloging with Unity Catalog or Purview, and role-based access to sensitive data.
- Data quality test suite
- Data catalog and lineage
- Access control policies
Measurable from day one
Target for scheduled pipeline runs completing on time, monitored and alerted.
Typical latency goal for near-real-time data feeds where required.
Target for business-critical tables covered by automated data quality tests.
Typical timeline to deliver a first production-ready data domain.
A clear path from kickoff to results
Discover
Map data sources, consumers, key metrics and pain points, and assess current data quality.
Architect
Design the platform, data model and governance approach, and select the toolset.
Build
Implement ingestion, transformation and orchestration for the first priority domain.
Validate
Reconcile numbers with business owners, add quality tests and document the data.
Scale
Extend to further domains, optimize cost and performance, and enable self-service.
The right stack for the job
- A single, trusted source of truth for metrics
- Less time spent wrangling spreadsheets and reconciling numbers
- Faster, fresher data for decision-making
- A solid foundation for BI, machine learning and generative AI
- Controlled access to sensitive data for compliance
- Scalable costs aligned with actual usage
Built for teams like yours
Unified Reporting
Businesses consolidating ERP, CRM and finance data into one warehouse for consistent reporting.
Legacy Warehouse Modernization
Teams moving from on-premises SQL Server, Oracle or Teradata warehouses to cloud platforms.
Real-Time Operations
Logistics, retail and IoT use cases that need live data for monitoring and alerts.
AI Readiness
Organizations preparing clean, governed data for machine learning and generative AI initiatives.
Customer 360
Combining customer data from sales, support, marketing and product into a unified profile.
A warehouse stores structured, modeled data optimized for SQL analytics. A lakehouse combines low-cost lake storage with warehouse-like reliability using formats like Delta Lake or Iceberg, supporting both BI and machine learning on the same data.
Related services
Business Intelligence & Dashboards
Power BI, Tableau and Looker dashboards, semantic models and self-service analytics that turn data into clear, timely business decisions.
Learn moreAI & Machine Learning Solutions
Predictive models, computer vision, NLP and MLOps, from use-case discovery to production deployment and monitoring.
Learn moreDatabase Administration (DBA) Services
Remote and managed DBA services for Oracle, SQL Server, PostgreSQL, MySQL and MongoDB, covering performance tuning, high availability, backups, upgrades and migrations.
Learn moreReady to start with Data Engineering?
Tell us about your project, team gap or IT challenge. A senior consultant replies within one business day with next steps — no obligation.