Building Data Integration Solutions

In the current digital era, data integration is the backbone of actionable insights and informed decision-making. As organizations grow across cloud and hybrid ecosystems, the ability to design secure, robust, and scalable pipelines has become essential for driving innovation, compliance, and operational efficiency. This book is a practical guide designed to help you bridge the gap between fragmented data sources and a unified, scalable ecosystem.

This guide covers the entire data integration lifecycle, beginning with core ETL/ELT concepts and data mesh architectures. You will progress from data profiling and modeling to building production-ready pipelines using AWS Glue, Azure Data Factory, and Apache NiFi. The book explores real-time streaming with Kafka and Kinesis, workflow orchestration via Airflow, and real-world applications in sectors like banking and healthcare. Finally, you will master DataOps essentials, including CI/CD with Terraform, IAM security, PII masking, and Prometheus monitoring, to ensure robust data governance and reliability.

By the end of this book, readers will be equipped to design and manage production-grade data pipelines that are resilient, auditable, and future-ready. They will gain practical skills in data pipeline orchestration, solution architecture, DevOps, governance, security, and observability, empowering them to deliver trusted end-to-end data solutions and lead enterprise transformations in the age of data-driven innovation.

WHAT YOU WILL LEARN
● Architect data integration solutions for batch and real-time systems.
● Design data pipelines across cloud and hybrid environments.
● Implement CI/CD workflows for automated data pipeline delivery.
● Secure pipelines with IAM, encryption, and secrets management.
● Monitor, log, and handle errors for operational reliability.
● Apply governance frameworks, ensuring compliance and data quality.
● Explore emerging trends like data mesh and AI-driven integration.

WHO THIS BOOK IS FOR
This book targets data engineers, architects, analysts, and students transitioning to cloud-native practices. Prior experience with basic databases is helpful as you master secure, enterprise-scale pipelines. It also serves researchers and managers needing practical, real-time industry use cases.

1. Introduction to Data Integration
2. Core Concepts, Patterns, and Architectures
3. Planning Integration Projects and Designing Workflows
4. ETL and ELT Development
5. Real-time and Streaming Data Integrations
6. Orchestrating Pipelines
7. DevOps for Data Pipelines
8. Securing Data Pipelines
9. Monitoring, Logging, and Error Handling
10. Data Governance, Quality, and Metadata Management
11. Emerging New Trends in Data Integration

Sayan Guha is currently working as an associate director and principal architect in the fields of artificial intelligence, data, and analytics practice in Cognizant Technology Solutions.

Earlier, he served Wipro Technologies, IBM, and Tech Mahindra as a data and analytics architect. He is a master’s degree holder in AI and ML from Birla Institute of Technology, Pilani, India. He holds a bachelor’s degree inelectronics and communication engineering from Maulana Abul Kalam Azad University of Technology.
He serves his current organization, leading a practice of AI, generative AI, and data analytics. He has authored several research papers and Scopus-indexed articles in web computing, cloud architecture, AI, and data architecture over the years.

His vast experience of 20 years in the information technology university spans around working with data integration, architecture, platform architecture, information management and strategy, data engineering and AI engineering spanning across three geographies – he played the roles of AI architect, information architect, data integration architect in various capacities in United States, Europe and India and carries vast experience bag of data engineering and analytics across various industries like banking, consumer goods, retail and electronics.