15 Topics
10 Days
75 Sessions
Programme Overview
Training Description
Who Should Attend
Session Objectives
- Understand the core principles and challenges of enterprise data integration
- Explore various data federation architectures and when to use them
- Design scalable ETL and ELT pipelines for batch and real-time data movement
- Use data virtualization tools for unified access to distributed datasets
- Integrate cloud and on-premise data seamlessly
- Apply security, governance, and compliance policies across federated environments
- Implement metadata management to drive data discoverability
- Monitor and optimize data integration workflows
- Create API-based data services for flexible data access
- Enable business intelligence platforms through real-time data federation
- Develop strategic roadmaps for enterprise-wide data unification initiatives
About the Course
This advanced training equips participants with the knowledge and practical skills required to integrate, unify, access, and manage data across complex enterprise environments. The course focuses on modern data integration and federation approaches that enable organizations to connect distributed databases, cloud platforms, data lakes, warehouses, APIs, and operational systems without requiring unnecessary duplication of data.Participants will explore ETL/ELT, data virtualization, federated querying, API-based integration, data pipelines, event-driven architectures, metadata, data quality, governance, security, and performance optimization. The training emphasizes designing scalable and interoperable data ecosystems that provide reliable access to information across departments, applications, and hybrid or multi-cloud environments.
Curriculum & Topics
15 Topics | 75 Sessions
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Workshop 1.1:
Overview of data integration goals and business impact
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Workshop 1.2:
Types of data integration (ETL, ELT, replication, federation)
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Workshop 1.3:
Data latency considerations: batch, micro-batch, streaming
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Workshop 1.4:
Integration tools and technologies overview
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Workshop 1.5:
Common challenges in large-scale data integration
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Workshop 2.1:
Centralized vs. decentralized integration approaches
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Workshop 2.2:
Federated data access models and hybrid patterns
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Workshop 2.3:
Query federation across heterogeneous systems
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Workshop 2.4:
Role of metadata in data federation
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Workshop 2.5:
Comparison of key data federation platforms
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Workshop 3.1:
ETL vs. ELT architectures in modern data stacks
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Workshop 3.2:
Extract strategies: API-based, change data capture, file-based
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Workshop 3.3:
Data transformation frameworks and tools
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Workshop 3.4:
Loading strategies for relational and non-relational targets
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Workshop 3.5:
Handling schema evolution and data type conversions
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Workshop 4.1:
Understanding data virtualization concepts
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Workshop 4.2:
Tools: Denodo, Dremio, SAP HANA, etc.
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Workshop 4.3:
Query pushdown and performance optimization
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Workshop 4.4:
Virtual views vs. materialized views
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Workshop 4.5:
Governing access in virtualized environments
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Workshop 5.1:
Hybrid data pipeline design considerations
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Workshop 5.2:
Connecting to legacy systems and mainframes
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Workshop 5.3:
Securing cloud data transfers
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Workshop 5.4:
Latency and consistency across environments
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Workshop 5.5:
Tool support for hybrid data integration
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Workshop 6.1:
Designing RESTful and GraphQL APIs for data access
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Workshop 6.2:
Data-as-a-service (DaaS) models
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Workshop 6.3:
API gateways and throttling
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Workshop 6.4:
JSON, XML, and protocol support
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Workshop 6.5:
Integrating APIs with ETL and orchestration tools
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Workshop 7.1:
Role of metadata in integration and federation
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Workshop 7.2:
Building enterprise data catalogs
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Workshop 7.3:
Automated metadata extraction
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Workshop 7.4:
Lineage tracking and data profiling
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Workshop 7.5:
Enhancing data discovery and compliance
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Workshop 8.1:
Ensuring data quality during integration
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Workshop 8.2:
Data cleansing and enrichment techniques
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Workshop 8.3:
Standardizing formats, units, and naming conventions
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Workshop 8.4:
Validation rules and error handling
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Workshop 8.5:
Monitoring and auditing transformation pipelines
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Workshop 9.1:
Event-based architecture principles
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Workshop 9.2:
Ingestion tools: Kafka, Flink, Kinesis, etc.
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Workshop 9.3:
Stream processing frameworks
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Workshop 9.4:
Capturing change data using CDC
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Workshop 9.5:
Combining real-time and batch pipelines
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Workshop 10.1:
Access control and data masking strategies
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Workshop 10.2:
Policy enforcement for federated data
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Workshop 10.3:
Compliance with GDPR, HIPAA, CCPA, etc.
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Workshop 10.4:
Role-based and attribute-based access models
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Workshop 10.5:
Audit trails and activity logging
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Workshop 11.1:
Scheduling and dependency management
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Workshop 11.2:
Tools: Apache Airflow, Prefect, Control-M
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Workshop 11.3:
Handling retries, failures, and alerts
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Workshop 11.4:
Parameterization and environment variables
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Workshop 11.5:
Building end-to-end workflow DAGs
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Workshop 12.1:
Tuning ETL and ELT for large data volumes
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Workshop 12.2:
Load balancing and parallel processing
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Workshop 12.3:
Query optimization for federated sources
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Workshop 12.4:
Metrics, logging, and dashboards
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Workshop 12.5:
Proactive bottleneck identification
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Workshop 13.1:
Enabling analytics with consistent datasets
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Workshop 13.2:
Connecting integrated data to BI tools
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Workshop 13.3:
Semantic modeling and data cubes
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Workshop 13.4:
Aggregation logic and derived metrics
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Workshop 13.5:
Supporting ad-hoc queries and dashboards
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Workshop 14.1:
Financial data integration across business units
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Workshop 14.2:
Healthcare system data federation use case
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Workshop 14.3:
Telecom real-time streaming pipeline example
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Workshop 14.4:
Manufacturing IoT data consolidation
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Workshop 14.5:
Government open data federation
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Workshop 15.1:
Designing a full-stack enterprise integration architecture
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Workshop 15.2:
Aligning integration strategy with business goals
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Workshop 15.3:
Creating data source inventories and mapping flows
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Workshop 15.4:
Roadmap for integration modernization
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Workshop 15.5:
Presentation and peer feedback
