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Data Engineering Services

Turning data chaos into insight harmony.

Our data engineering and ETL services are designed to optimize your data infrastructure with advanced ETL pipelines and scalable data lakes. We implement data extraction using APIs, message queues, and CDC (Change Data Capture) techniques, followed by transformation through Spark, Python, and SQL-based processing. Our solutions ensure efficient data loading into cloud-based data warehouses like AWS Redshift, Google BigQuery, and Snowflake. With automated orchestration using tools like Apache Airflow and Prefect, we ensure seamless data flow, high availability, and real-time analytics readiness.

Bridge the gap between raw data and actionable intelligence. Discover how our ETL services can enhance your analytics.  

We help you solve
your pressing challenges.

Data Engineering Services

Our Complete Suite of Data Engineering Services

Data drama? We help you get rid of it, in nice way!
Data Pipeline

We craft scalable architectures using microservices, data lakes, and real-time streaming with Apache Kafka and Flink. Our pipelines are optimized for performance, handling complex ETL processes with precision and speed. Whether you're managing massive data sets or ensuring smooth data governance, we make sure your architecture is future-proof and ready to grow with your business needs.

  • Data Lake Design and Implementation
  • Real-time Data Streaming and Integration
  • ETL Pipeline Development and Optimization
  • Data Pipeline Orchestration
DataOps Managed Services

We handle the end-to-end management of your data pipelines, from orchestration with Apache Airflow to monitoring with Grafana and Prometheus. Our team ensures continuous integration and delivery of data solutions, minimizing downtime and maximizing efficiency. With proactive incident management and performance optimization, we keep your data operations running smoothly.

  • Pipeline Orchestration and Automation
  • CI/CD for Data
  • Real-time Monitoring and Alerting
  • Performance Tuning and Optimization
Data Governance

We implement robust frameworks for data quality, integrity, and privacy using tools like Apache Atlas and Collibra. Our approach includes defining data ownership, establishing clear data policies, and enforcing access controls. With real-time monitoring and auditing, we help you maintain regulatory compliance while enabling trustworthy data management.

  • Metadata Management and Data Cataloging
  • Data Ownership and Stewardship
  • Data Access Control and Security
  • Regulatory Compliance Audits
Big Data Analysis

We leverage technologies like Hadoop, Spark, and distributed databases to process and analyze massive data sets with speed and accuracy. From real-time data streaming to complex predictive modeling, we provide actionable insights that drive strategic decisions. With our expertise, you can transform big data challenges into opportunities for growth and innovation.

  • Data Processing with Hadoop and Spark
  • Real-time Data Analysis and Visualization
  • Data Warehousing and OLAP
  • Machine Learning and Advanced Analytics

Wondering how to build scalable, reliable data pipelines? Get a ballpark estimation within 36 hours.

What’s Trending Now!

Discover the forefront of data engineering. Stay agile and competitive with real-time processing and automated data quality enhancements.

  • Decentralized data architecture for distributed data ownership. 
  • Combination of data engineering and DevOps. 
  • Embedding machine learning models directly into ETL pipelines.
  • Employing AI-driven solutions for automatic data quality checks.
What's Trending Now!
Support

Data Engineering Support: From Strategy to Success & Beyond

After your data engineering and ETL solutions are in place, our support team becomes your go-to partner for seamless operations and innovation. We provide proactive maintenance, rapid troubleshooting, and ongoing optimization.
  • Support for scaling data infrastructure/pipelines as data volumes grow.   
  • Assistance with integrating new data sources into existing pipelines. 
  • Strategies and support for data recovery and system restoration.  
  • Configurable alerts for monitoring pipeline health, data anomalies.  

Explore the Possibilities with Our Data Engineering Services

Technologies: The Engine Room

We constantly dig deeper into new technologies and push the boundaries of old technologies, with just one goal - client value realization.
Technologies

Why Azilen is the right choice

We deliver nothing less than excellence.
Achiever
Adaptable
Agile
Ambitious
Analytical
Attentive

Case Studies: Real Transformations, Real Results

Explore how we've turned client challenges into measurable results.

The Spirit Behind Engineering Excellence

We instantly fall in love with your challenges and steal it from you!
  • 400+
    Product Engineers
  • 15+
    Years of Experience
  • 100+
    Delivered Lifecycles
  • 10M+
    Lives Touched
  • 3M+
    Man Hours Invested
  • 10+
    Wellness Programs

Unfiltered Customer Reviews

The essence (in case you don't read it all): We nail it, every time!

Product Engineering is in Our DNA.

And we are not a development outsourcing company!
THE AZILEN Promise
THE AZILEN Promise Upheld
Product Lifecycle Management
Strategic Innovation and R&D
Cross-Disciplinary Expertise
Product Ownership and Vision
Scalable Architecture Design
Agile and Iterative Development
Long-Term Strategic Partnerships

Frequently Asked Questions (FAQ's)

Get your most common questions around Data Engineering services answered.

Data engineering involves designing, building, and maintaining the systems and infrastructure that allow organizations to collect, store, and analyze data efficiently. For software products, effective data engineering ensures that data is accessible, reliable, and actionable, enabling better decision-making, enhanced product features, and improved user experiences.

Key components typically include data ingestion, data processing, data storage, data integration, and data orchestration. Data engineers work on creating pipelines that move data from various sources to storage systems, transforming and cleaning the data along the way to ensure it is usable for analysis and reporting.

Data sources can include relational databases, NoSQL databases, data warehouses, APIs, cloud storage services, and external data feeds. Data engineering services can integrate data from various sources, including structured and unstructured data, to create a comprehensive data ecosystem.

Common challenges include handling large volumes of data, ensuring data quality and consistency, managing data integration from diverse sources, and maintaining system performance and scalability. Data engineering services address these challenges through robust architecture design, data validation techniques, and efficient data processing frameworks.

Data engineering focuses on building the infrastructure and pipelines required to collect, store, and process data, while data science involves analyzing and interpreting that data to extract insights and build predictive models. Both disciplines work together, with data engineering providing the foundation for data science to perform meaningful analysis.

ETL stands for Extract, Transform, Load. It is a critical process in data engineering where data is extracted from various sources, transformed into a usable format, and then loaded into a data warehouse or database. This process ensures that data is cleansed, standardized, and integrated for analysis and reporting.

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