Introduction:
Sisense is a powerful Business Intelligence (BI) platform. It helps users analyze complex data from multiple sources. Moreover, it supports data integration, transformation, and visualization. In addition, it enables data blending to combine datasets for deeper insights. As a result, users can make better business decisions.

Learner Prerequisites:

• Basic understanding of data and reporting concepts
• Familiarity with Excel or Google Sheets
• Awareness of tables, rows, and columns
• Basic knowledge of BI tools is helpful
• No prior Sisense experience is required

Table of Contents

1. Introduction to Data Blending in Sisense
1.1 First, what is data blending
1.2 Next, importance of data blending in BI
1.3 Then, overview of Sisense blending features
1.4 Moreover, difference between blending and joining
1.5 Finally, real-world use cases

2. Understanding Data Sources for Blending
2.1 First, types of data sources in Sisense
2.2 Next, connecting multiple data sources
2.3 Then, structuring data for blending
2.4 Moreover, ensuring data compatibility
2.5 Finally, managing data relationships

3. Data Modeling for Blending
3.1 First, introduction to data models
3.2 Next, creating relationships between datasets
3.3 Then, using primary and foreign keys
3.4 Moreover, handling different data granularity
3.5 Finally, optimizing model performance

4. Core Data Blending Techniques
4.1 First, joining datasets in Sisense
4.2 Next, merging data from multiple sources
4.3 Then, using Elasticube for blending
4.4 Moreover, applying filters during blending
4.5 Finally, handling mismatched fields

5. Advanced Data Blending Concepts
5.1 First, blending large datasets efficiently
5.2 Next, using calculated fields in blended data
5.3 Then, managing real-time blended data
5.4 Moreover, resolving data conflicts and duplicates
5.5 Finally, optimizing blended queries

6. Working with Blended Data in Dashboards
6.1 First, visualizing blended datasets
6.2 Next, creating dashboards from multiple sources
6.3 Then, applying filters on blended data
6.4 Moreover, building interactive reports
6.5 Finally, validating blended results

7. Best Practices in Data Blending
7.1 First, designing efficient data models
7.2 Next, ensuring data accuracy and consistency
7.3 Then, improving query performance
7.4 Moreover, avoiding common blending mistakes
7.5 Finally, maintaining scalable structures

Conclusion
This training provides a clear understanding of data blending in Sisense. First, it explains data sources and modeling. Then, it covers blending techniques and advanced concepts. Finally, it helps learners combine datasets effectively for better insights.