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DATA INTEGRATION : BRIDGING THE GAP
BACHELOR OF ENGINEERING in COMPUTER SCIENCE AND ENGINEERING
191CSC701T – Data Science
CONTENTS
INTRODUCTION
WHAT IS DATA INTEGRATION
TYPES OF DATA INTEGRATION
BENEFITS OF DATA INTEGRATION
COMMON DATA INTEGRATION TOOLS
DATA INTEGRATION PROCESS
DATA INTEGRATION CHALLENGES
REAL- WORLD EXAMPLES
FUTURE TRENDS
INTRODUCTION
Data integration is the process of combining data from multiple sources to provide a
unified and comprehensive view.
In today's data-driven world, businesses rely on data integration to make informed
decisions.
Efficient data integration saves time and resources by eliminating the need to manually
gather and reconcile data.
It plays a pivotal role in data-driven industries like finance, healthcare, and e-commerce.
WHAT IS DATA INTEGRATION
• Data integration is the process of collecting, transforming, and loading data from various
sources into a single repository.
 It helps organizations create a consistent, accurate, and up-to-date view of their data.
 Data integration is essential for breaking down data silos, where different departments store data
separately.
 It involves techniques like data cleansing, data enrichment, and data mapping.
 The ultimate goal is to provide a unified view of data for analysis and decision-making.
TYPES OF DATA INTEGRATION
Types of data integration are:
1.ETL
2.Real time data integration
3.Batch data integration
4.Hybrid approaches
BENEFITS OF DATA INTEGRATION
Improved Data Quality
Faster Decision-Making
Enhanced Business Insights
Cost Reduction
Competitive Advantage
COMMON DATA INTEGRATION TOOLS
• Apache Nifi
• Talend
• Informatica
• Microsoft Azure Data Factory
• Apache Kafka
DATA INTEGRATION PROCESS
• Extraction
• Data Transformation
• Data Loading
• Data Synchronization
• Data Validation
DATA INTEGRATION CHALLENGES
• Data Security
• Compatibility Issues
• Data Silos
• Data Volume
• Data Governance
REAL-WORLD EXAMPLES
• Netflix
• Healthcare Systems
• E-commerce
• Financial Institutions
• Manufacturing
FUTURE TRENDS
• AI-driven Integration
• Cloud-native Solutions
• Data Mesh
• IoT Integration
• DataOps
Data_Integration.pptx

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Data_Integration.pptx

  • 1. DATA INTEGRATION : BRIDGING THE GAP BACHELOR OF ENGINEERING in COMPUTER SCIENCE AND ENGINEERING 191CSC701T – Data Science
  • 2. CONTENTS INTRODUCTION WHAT IS DATA INTEGRATION TYPES OF DATA INTEGRATION BENEFITS OF DATA INTEGRATION COMMON DATA INTEGRATION TOOLS DATA INTEGRATION PROCESS DATA INTEGRATION CHALLENGES REAL- WORLD EXAMPLES FUTURE TRENDS
  • 3. INTRODUCTION Data integration is the process of combining data from multiple sources to provide a unified and comprehensive view. In today's data-driven world, businesses rely on data integration to make informed decisions. Efficient data integration saves time and resources by eliminating the need to manually gather and reconcile data. It plays a pivotal role in data-driven industries like finance, healthcare, and e-commerce.
  • 4. WHAT IS DATA INTEGRATION • Data integration is the process of collecting, transforming, and loading data from various sources into a single repository.  It helps organizations create a consistent, accurate, and up-to-date view of their data.  Data integration is essential for breaking down data silos, where different departments store data separately.  It involves techniques like data cleansing, data enrichment, and data mapping.  The ultimate goal is to provide a unified view of data for analysis and decision-making.
  • 5. TYPES OF DATA INTEGRATION Types of data integration are: 1.ETL 2.Real time data integration 3.Batch data integration 4.Hybrid approaches
  • 6. BENEFITS OF DATA INTEGRATION Improved Data Quality Faster Decision-Making Enhanced Business Insights Cost Reduction Competitive Advantage
  • 7. COMMON DATA INTEGRATION TOOLS • Apache Nifi • Talend • Informatica • Microsoft Azure Data Factory • Apache Kafka
  • 8. DATA INTEGRATION PROCESS • Extraction • Data Transformation • Data Loading • Data Synchronization • Data Validation
  • 9. DATA INTEGRATION CHALLENGES • Data Security • Compatibility Issues • Data Silos • Data Volume • Data Governance
  • 10. REAL-WORLD EXAMPLES • Netflix • Healthcare Systems • E-commerce • Financial Institutions • Manufacturing
  • 11. FUTURE TRENDS • AI-driven Integration • Cloud-native Solutions • Data Mesh • IoT Integration • DataOps