What Is Data Integration and Transformation?
- 1. Data Integration and Transformation Presented by Ms Subhasheni A Assistant Professor Department of Computer Science Sri Ramakrishna College of Arts & Science Coimbatore
- 2. Introduction • Data integration and transformation are key stages in the data preparation process. • They ensure data from multiple sources is combined and made ready for analysis. • Critical for building reliable and consistent datasets.
- 3. What is Data Integration? • The process of combining data from different sources into a unified view. • Helps in breaking data silos. • Enables seamless data analysis and reporting.
- 4. Importance of Data Integration • Enables comprehensive analysis. • Eliminates data redundancy. • Supports business intelligence and decision-making. • Improves data accessibility and consistency.
- 5. Common Data Sources for Integration • Relational databases (e.g., MySQL, Oracle) • Flat files (CSV, Excel) • APIs and Web Services • Cloud storage (AWS, Azure) • Streaming data (IoT, sensors)
- 6. What is Data Transformation? • The process of converting data from its original format into a format suitable for analysis. • Includes cleaning, normalization, aggregation, and formatting.
- 7. Key Data Transformation Techniques • Data Cleaning (removing nulls, duplicates) • Data Filtering (selecting relevant records) • Data Aggregation (summing, averaging, grouping) • Data Encoding (e.g., converting categories into numbers) • Format Conversion (e.g., text to date)
- 8. Data Integration Techniques • ETL (Extract, Transform, Load) • ELT (Extract, Load, Transform) • Data Virtualization • Data Warehousing • API-based Integration
- 9. Tools Used for Integration & Transformation • Talend • Apache NiFi • Informatica • Microsoft Power BI • Alteryx • AWS Glue
- 10. Challenges in Data Integration & Transformation • Data inconsistency • Handling large volumes of data • Integration of unstructured data • Security and privacy issues • Real-time data syncing
- 11. Best Practices • Define clear integration goals. • Use standard data formats. • Validate data quality at every step. • Automate ETL/ELT pipelines where possible. • Maintain detailed documentation.
- 12. Conclusion • Integration and transformation are vital for analytics readiness. • Enable organizations to unify and clean data for accurate insights. • Proper tools and strategies can improve efficiency and reliability.
- 13. Thank you!