DATA engineering
Design, Build, and Scale Cloud Native Data Infrastructure
Transform fragmented data into actionable intelligence
Modern data engineering is the backbone of data-driven organizations. At Exilon, we architect scalable, secure, and AI ready data pipelines that empower you to make faster decisions, automate workflows, and unlock the full potential of cloud analytics platforms.
Our Data Engineering Offerings
End-to-end solutions to architect, modernize, and manage data pipelines across platforms.
Cloud Data Architecture
Design scalable, secure, and cost effective cloud architectures on Azure Synapse, AWS Redshift, Snowflake, or Databricks Lakehouse.
Data Pipeline Development
Build resilient, batch, and real-time pipelines using Azure Data Factory, AWS Glue, Airflow, or Databricks Workflows for seamless data movement and transformation.
ETL / ELT Modernization
Migrate from legacy ETL tools to cloud native ELT processes to improve performance and flexibility using Spark, dbt, or Snowflake.
Streaming & Real-Time Data Engineering
Leverage platforms like Kafka, Event Hubs, and Kinesis to enable real-time dashboards, anomaly detection, and personalization at scale.
Lakehouse Implementation
Unify data lakes and warehouses for structured and unstructured data analysis, ML readiness, and lower TCO using Databricks Lakehouse.
DataOps & Automation
Implement CI/CD for data workflows, version control, monitoring, and testing to ensure agility and reliability across environments.
Why Exilon?
Experience, Expertise, and Excellence
Data engineering enables reliable, real-time, and cost-efficient data delivery across your business. With scalable pipelines and modern architectures, you gain the speed, quality, and flexibility needed to drive BI and AI initiatives.
We specialize in modern cloud ecosystems and hybrid solutions
Azure
Synapse Analytics, Data Factory, Event Hubs, Purview, Fabric
AWS
Glue, Redshift, Kinesis, S3, Lake Formation
Snowflake
ELT design, secure data sharing, cost optimization
Databricks
Spark, Delta Lake, ML pipelines, Lakehouse
Let’s Engineer Your Data Advantage
Whether you’re building new data pipelines, optimizing existing ones, or integrating multiple platforms, Exilon ensures performance, reliability, and future readiness.
FAQs
What is data engineering and why is it important for businesses?
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Data engineering is the process of designing, building, and maintaining data pipelines and architectures that transform raw data into usable formats. It forms the foundation for analytics, machine learning, and business intelligence by ensuring data is clean, accessible, and reliable.
What data engineering services does Exilon offer?
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Exilon provides end-to-end data engineering services including data pipeline development, ETL/ELT processing, data integration, cloud migration, data quality checks, and real-time streaming. We build modern data infrastructure on platforms like Azure, AWS, Databricks, and Snowflake.
How does Exilon ensure scalable and efficient data architectures?
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We design cloud-native, distributed systems that scale automatically with data volume and usage. By leveraging modern frameworks like Apache Spark, Kafka, and Delta Lake, Exilon ensures high-performance, fault-tolerant, and cost-optimized data environments.
Can Exilon help modernize legacy data systems?
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Yes. Exilon specializes in modernizing outdated data platforms by migrating them to cloud-native, scalable solutions. We ensure zero data loss, minimal disruption, and seamless integration with your existing analytics and AI systems.
What industries benefit most from Exilon’s data engineering solutions?
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Our data engineering services support industries such as finance, healthcare, telecom, retail, logistics, and manufacturing. We tailor data solutions to meet each industry’s specific requirements around compliance, scale, speed, and security.
What business benefits does Exilon deliver through data engineering?
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Partnering with Exilon enables you to:
- Future-proof your data ecosystem
- Build reliable and automated data pipelines
- Ensure clean, consistent, and accessible data
- Accelerate time-to-insight for analytics and AI
- Improve operational efficiency and agility
- Reduce costs through optimized infrastructure
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