Data Engineering

Services Include

Data Warehouse & Lakehouse Setup

ETL/ELT Pipeline Development

Data Ingestion & Transformation

Real-Time Stream Processing

Data Quality & Governance

Why Data Engineering Matters More Than Ever

In today’s data-driven world, the success of analytics, machine learning, and business intelligence efforts hinges on the strength of your data foundation. Without the right architecture, governance, and real-time capabilities, even the best analytics tools fall short.

Our data engineering team ensures your data ecosystem is future-ready, scalable, and high-performing, helping you move from siloed data chaos to unified data intelligence.

Our AWS Technology Stack for Data Engineering

We specialize in building scalable, secure, and cost-efficient data platforms using the full power of AWS services. Whether you’re modernizing legacy systems or launching greenfield data initiatives, our AWS-certified engineers craft solutions that are cloud-native and future-proof.

Data Storage & Warehousing

Amazon S3 – Scalable data lake for structured & unstructured data

Amazon Redshift – Fully managed, petabyte-scale data warehouse

AWS Glue Data Catalog – Centralized metadata repository for data discovery

Amazon RDS / Aurora – Relational database services for transactional workloads

ETL / ELT & Data Processing

AWS Glue – Serverless ETL service for transforming and preparing data

Amazon EMR – Big data processing with Apache Spark, Hive, Hadoop

AWS Step Functions – Workflow orchestration for ETL pipelines

Amazon DataBrew – No-code data preparation and transformation

Streaming & Real-Time Processing

Amazon Kinesis Data Streams – Real-time data ingestion at scale

Kinesis Data Firehose – Streaming delivery to S3, Redshift, or Elasticsearch

Amazon MSK (Managed Kafka) – Apache Kafka for real-time event streaming

AWS Lambda – Serverless compute for on-the-fly data transformation

Monitoring, Security & Governance

AWS Lake Formation – Secure, governed data lakes on S3

AWS CloudTrail & CloudWatch – Monitoring, logging, and alerting

IAM (Identity and Access Management) – Fine-grained access control

AWS Shield & KMS – DDoS protection and data encryption

Analytics & Machine Learning Integration

Amazon Athena – Serverless SQL queries on S3 data

Amazon QuickSight – Scalable business intelligence and visualization

Amazon SageMaker – Build, train, and deploy ML models

Amazon Forecast & Personalize – ML-powered predictive analytics

Talk to an AWS Data Engineer

Use Cases We Power

Customer 360 & Personalization

Real-Time Fraud Detection & Alerting

Marketing & Sales Analytics

AI/ML Model Enablement & Data Enrichment

IoT Data Ingestion & Time-Series Processing

Enterprise Data Lakes for Centralized Reporting

Get Started with Scalable Data Solutions

Industries We Serve

Our data engineering solutions are adaptable across sectors:

Finance & Fintech

Secure, real-time data pipelines for compliance and analytics

Retail & E-Commerce

Unified data layers for recommendation engines and sales forecasting

Healthcare

HIPAA-compliant architecture and clinical data transformation

Logistics & Supply Chain

Real-time tracking, demand prediction, and optimization

Media & Entertainment

Analytics-ready data for content strategy and audience insights

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How We Work

1

Discovery & Assessment

Understand your existing data ecosystem, pain points, and desired outcomes.

2

Architecture Design

Plan scalable, cloud-native solutions tailored to your goals.

3

Implementation

Develop robust pipelines, ETL/ELT workflows, and real-time streaming solutions.

4

Testing & Optimization

Ensure accuracy, quality, and performance before go-live.

5

Ongoing Support

Continuous monitoring, maintenance, and enhancement post-deployment.

Start Building Your Data Pipeline

Key Benefits

Faster insights with real-time processing

Improved data reliability & availability

Reduced operational overhead via automation

Better data quality & compliance

Foundation for AI/ML & predictive analytics

Design a Future-Ready Data Platform

Client Testimonials

Eagle in Cloud helped us rebuild our entire data pipeline. We now get real-time insights across regions — it has transformed the way we make decisions.

Head of Analytics, Fintech Client

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Frequently Asked Questions

Our data engineering services cover everything from designing data architectures and building ETL/ELT pipelines to implementing data lakes, stream processing, and ensuring data quality and governance.

Yes, we specialize in AWS, but we also support Azure and GCP. Our solutions are cloud-native, scalable, and tailored to your existing infrastructure or migration needs.

Absolutely. We help modernize outdated data infrastructure by migrating to cloud-native platforms, optimizing pipelines, and implementing real-time processing where needed.

We’ve delivered data engineering solutions across fintech, retail, healthcare, logistics, media, and more. Our approach is flexible and can be adapted to any domain.

We implement best practices for validation, error handling and lineage tracking using tools like AWS Glue, Great Expectations, and Lake Formation.

Data engineering focuses on building the infrastructure and pipelines to collect, store, and prepare data, while data science involves analyzing that data to generate insights and predictions.

Yes. We work with tools like Apache Kafka, AWS Kinesis, and Spark Streaming to build low-latency, event-driven pipelines for real-time analytics and decision-making.

Timelines vary based on complexity. A small ETL project may take a few weeks, while large-scale architecture builds or cloud migrations can take several months. We provide a project plan during onboarding.

Yes. We offer ongoing maintenance, monitoring, and optimization services to ensure your data systems stay reliable and up-to-date.

The Backbone of Modern Data Strategy