Business Professionals
Techno-Business Professionals
Power BI | Power Query | Advanced DAX | SQL - Query &
Programming
Microsoft Fabric | Power BI | Power Query | Advanced DAX |
SQL - Query & Programming
Microsoft Power Apps | Microsoft Power Automate
Power BI | Adv. DAX | SQL (Query & Programming) |
VBA | Web Scrapping | API Integration
Power BI | Power Apps | Power Automate |
SQL (Query & Programming)
Power BI | Adv. DAX | Power Apps | Power Automate |
SQL (Query & Programming) | VBA | Web Scrapping | API Integration
Power Apps | Power Automate | SQL | VBA |
Web Scraping | RPA | API Integration
Technology Professionals
Power BI | DAX | SQL | ETL with SSIS | SSAS | VBA
Power BI | SQL | Azure Data Lake | Synapse Analytics |
Data Factory| Azure Analysis Services
Microsoft Fabric | Power BI | SQL | Lakehouse |
Data Factory (Pipelines) | Dataflows Gen2 | KQL | Delta Tables
Power BI | Power Apps | Power Automate | SQL | VBA | API Integration
Power BI | Advanced DAX | Databricks | SQL | Lakehouse Architecture
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6 Weeks | 60 Hours
30 Sessions, 2 Hrs Each
Live Online, Instructor-Led
Batch starts on
th
For Modern Cloud BI & Data Engineers
Where Data Engineering Meets Scalable BI.
Built for professionals designing data solutions on the modern Lakehouse stack, this course helps you master Databricks integration with Power BI, optimize pipelines, and deliver analytics at cloud scale. Gain the expertise to bridge raw data and business insights — the skillset every enterprise now demands.
Engineer Scalable Data Pipelines – Automate and orchestrate workflows using Databricks Workflows.
Build a Unified Lakehouse – Manage structured and unstructured data with Delta Lake and Azure Data Lake.
Transform Data at Scale – Cleanse, process, and enrich data using SQL, PySpark, and Python.
Integrate BI Seamlessly – Connect Databricks with Power BI to deliver real-time analytics and insights.
Optimize for Performance & Governance – Caching, security, and tuning best practices.
Power Business Decisions – Deliver production-grade, cloud-scale BI that bridges data engineering and analytics.
Engineer Scalable Data Pipelines – Automate and orchestrate workflows using Databricks Workflows.
Build a Unified Lakehouse – Manage structured and unstructured data with Delta Lake and Azure Data Lake.
Transform Data at Scale – Cleanse, process, and enrich data using SQL, PySpark, and Python.
Integrate BI Seamlessly – Connect Databricks with Power BI to deliver real-time analytics and insights.
Optimize for Performance & Governance – Caching, security, and tuning best practices.
Power Business Decisions – Deliver production-grade, cloud-scale BI that bridges data engineering and analytics.
Cloud Data Engineers
Building scalable data pipelines with Databricks and Delta Lake.
BI Developers
Connecting Power BI with Databricks for cloud-scale analytics.
Data Analysts
Using Databricks SQL and Power BI for advanced reporting.
Data Architects
Designing unified, secure Lakehouse architectures.
ETL Specialists
Automating data ingestion and transformation workflows.
Tech Leads & Decision Makers
Modernizing enterprise BI with the Databricks Lakehouse.
A snapshot of what you'll be learning in 6-weeks.
Objective: Get hands-on experience with advanced administration settings, permissions, Data refresh times etc.
Work on real-world projects using Azure Databricks, and Power BI to design scalable ETL pipelines, manage Delta Lake, and deliver real-time analytics. Gain practical, job-ready skills to master the end-to-end data lifecycle — from ingestion to insight.
Power BI
SQL & PySpark
Databricks Workflows
Delta Lake & Lakehouse
Python
Enterprise Sales Performance Dashboard
Develop an interactive Power BI dashboard to analyze regional and product-wise sales performance. Utilize DAX measures for YOY growth, sales variance, and customer segmentation, while Power Query is used to clean and transform raw sales data from multiple sources.
Customer Churn Prediction
Build a churn prediction model by integrating customer interaction data, support tickets, and transactional history. Use Power Query for data transformation and DAX measures to calculate churn probability based on behavioral patterns.
HR Analytics & Workforce Planning
Create an HR dashboard to track employee retention, hiring trends, and performance metrics. Connect multiple data sources to Power BI, apply DAX formulas to calculate attrition rates, and implement role-based security for restricted views.
Supply Chain & Inventory Optimization
Design a Power BI solution to monitor inventory levels, supplier performance, and stock movement across locations. Use Power Query to merge purchase order data with warehouse stock levels and apply DAX for predictive analytics on stock replenishment.
Financial Reporting & Budget vs. Actuals Analysis
Automate financial reporting using DAX calculations for variance analysis, custom KPIs, and trend forecasting. Apply Power Query transformations to consolidate financial statements from different departments into a unified report.
Sales & Marketing Data Integration
Combine sales and marketing datasets from multiple sources using PySpark and SQL. Cleanse data, handle missing values, and create aggregated tables for downstream analytics.
Customer Segmentation & LTV
Analyze customer behavior using PySpark to calculate lifetime value, segment customers, and generate insights for targeted marketing campaigns.
Product Performance Analysis
Use PySpark to calculate product-level KPIs, perform trend analysis, and create summary tables for reporting in Power BI.
Streaming Event Log Processing
Ingest and transform application log data in Databricks using PySpark streaming. Aggregate metrics for operational monitoring and anomaly detection.
Data Validation & Quality Checks
Build SQL and PySpark scripts to validate datasets, enforce constraints, and identify inconsistencies before loading into Delta Lake.
Automated Sales ETL Pipeline
Ingest daily sales CSV/JSON files from Azure Data Lake, transform using Databricks, and load into Delta Lake tables for reporting.
Batch Data Processing Pipeline
Design a pipeline that processes historical data in batches, aggregates KPIs, and refreshes analytics tables automatically.
Real-Time Streaming Pipeline
Ingest live IoT or clickstream data into Databricks, perform transformations, and store in Delta Lake for real-time dashboards.
Data Quality & Validation Pipeline
Automate data validation steps, generate alerts for anomalies, and ensure only clean, verified data is loaded for analytics.
Pipeline Monitoring & Logging
Create dashboards and alerts to monitor ETL workflow health, runtime performance, and data freshness.
Delta Table Management
Implement ACID-compliant Delta Lake tables, perform updates, deletes, and optimize table performance.
Time Travel Analytics
Use Delta Lake time travel to query historical versions of data and generate trend analyses for business reporting.
Partitioning & Optimization
Partition large tables to improve query performance, reduce storage costs, and speed up downstream analytics.
Lakehouse Integration Project
Integrate structured and unstructured datasets into a unified Lakehouse architecture for enterprise reporting.
Data Governance & Security
Apply access controls, enforce schema validation, and implement lineage tracking in Delta Lake tables.
Data Cleansing Pipeline for Lakehouse
Use Python (via Notebooks in Fabric) to clean, deduplicate, and normalize raw CSV/parquet files before storing them in Delta format in the Fabric Lakehouse. Integrate with a Data Factory pipeline for scheduled ingestion.
Automated Data Quality Checks on Delta Tables
Build a reusable Python script to perform column-level validation (null checks, data types, range thresholds) on Delta tables in the Lakehouse. Automatically log failures to a monitoring table and trigger alerts.
Generate KPI Summary Tables for Power BI
Write a Python routine to compute summary KPIs (e.g., weekly sales, customer retention, churn rates) and store results in a Gold layer table. These output tables are optimized for reporting in Power BI dashboards.
Notebook-Driven ETL for Semi-Structured Data
Automate ETL for JSON or nested data (e.g., API exports, logs) using Python in a Fabric Notebook. Transform and flatten the data structure, then write the result into Delta tables for KQL/Power BI consumption.
Automated Archival and Partition Management
Use Python to periodically move older data partitions to cold storage and maintain optimized table size for querying. This improves performance and cost-efficiency within the Lakehouse.
Develop an interactive Power BI dashboard to analyze regional and product-wise sales performance. Utilize DAX measures for YOY growth, sales variance, and customer segmentation, while Power Query is used to clean and transform raw sales data from multiple sources.
Build a churn prediction model by integrating customer interaction data, support tickets, and transactional history. Use Power Query for data transformation and DAX measures to calculate churn probability based on behavioral patterns.
Create an HR dashboard to track employee retention, hiring trends, and performance metrics. Connect multiple data sources to Power BI, apply DAX formulas to calculate attrition rates, and implement role-based security for restricted views.
Design a Power BI solution to monitor inventory levels, supplier performance, and stock movement across locations. Use Power Query to merge purchase order data with warehouse stock levels and apply DAX for predictive analytics on stock replenishment.
Automate financial reporting using DAX calculations for variance analysis, custom KPIs, and trend forecasting. Apply Power Query transformations to consolidate financial statements from different departments into a unified report.
Combine sales and marketing datasets from multiple sources using PySpark and SQL. Cleanse data, handle missing values, and create aggregated tables for downstream analytics.
Analyze customer behavior using PySpark to calculate lifetime value, segment customers, and generate insights for targeted marketing campaigns.
Use PySpark to calculate product-level KPIs, perform trend analysis, and create summary tables for reporting in Power BI.
Ingest and transform application log data in Databricks using PySpark streaming. Aggregate metrics for operational monitoring and anomaly detection.
Build SQL and PySpark scripts to validate datasets, enforce constraints, and identify inconsistencies before loading into Delta Lake.
Ingest daily sales CSV/JSON files from Azure Data Lake, transform using Databricks, and load into Delta Lake tables for reporting.
Design a pipeline that processes historical data in batches, aggregates KPIs, and refreshes analytics tables automatically.
Ingest live IoT or clickstream data into Databricks, perform transformations, and store in Delta Lake for real-time dashboards.
Automate data validation steps, generate alerts for anomalies, and ensure only clean, verified data is loaded for analytics.
Create dashboards and alerts to monitor ETL workflow health, runtime performance, and data freshness.
Implement ACID-compliant Delta Lake tables, perform updates, deletes, and optimize table performance.
Use Delta Lake time travel to query historical versions of data and generate trend analyses for business reporting.
Partition large tables to improve query performance, reduce storage costs, and speed up downstream analytics.
Integrate structured and unstructured datasets into a unified Lakehouse architecture for enterprise reporting.
Apply access controls, enforce schema validation, and implement lineage tracking in Delta Lake tables.
Use Python (via Notebooks in Fabric) to clean, deduplicate, and normalize raw CSV/parquet files before storing them in Delta format in the Fabric Lakehouse. Integrate with a Data Factory pipeline for scheduled ingestion.
Build a reusable Python script to perform column-level validation (null checks, data types, range thresholds) on Delta tables in the Lakehouse. Automatically log failures to a monitoring table and trigger alerts.
Write a Python routine to compute summary KPIs (e.g., weekly sales, customer retention, churn rates) and store results in a Gold layer table. These output tables are optimized for reporting in Power BI dashboards.
Automate ETL for JSON or nested data (e.g., API exports, logs) using Python in a Fabric Notebook. Transform and flatten the data structure, then write the result into Delta tables for KQL/Power BI consumption.
Use Python to periodically move older data partitions to cold storage and maintain optimized table size for querying. This improves performance and cost-efficiency within the Lakehouse.
Data Engineering & Business Intelligence Expert (On-Cloud)
Fabric Data Engineering Specialist
Data Analytics & BI Specialist using MS-SQL and Power BI
Power BI Associate
MIS Reporting & Business Modeling Specialist using MS Excel
Shareable certificate
Add to your LinkedIn profile
Data Engineering & Business Intelligence Expert (On-Cloud)
Level : EXPERT
Certificate Code : EG-EXP-105
Eligibility : On clearing post-training assessment
Fabric Data Engineering Specialist
Level : SPECIALIST
Certificate Code : EG-SPL-008
Eligibility : On clearing post-training assessment
Data Analytics & BI Specialist using MS-SQL and Power BI
Level : SPECIALIST
Certificate Code : EG-SPL-004
Eligibility : On clearing post-training assessment
Power BI Associate
Level : ASSOCIATE
Certificate Code : EG-ASC-003
Eligibility : On clearing post-training assessment
MIS Reporting & Business Modeling Specialist using MS Excel
Level : SPECIALIST
Certificate Code : EG-SPL-001
Eligibility : On clearing post-training assessment
Limited Seats. Registration Closing Soon
Databricks is one of the fastest-growing platforms for cloud data engineering and analytics. Learning Databricks with Power BI equips you to build scalable ETL pipelines, manage Delta Lake, and deliver real-time insights. Companies adopting modern Lakehouse architectures are actively seeking professionals with these skills, making this combination highly valuable for career growth.
A basic understanding of SQL or data concepts helps, but this course is designed for data engineers, BI developers, and analytics professionals at various experience levels who want to upskill in cloud data engineering.
Yes, Databricks requires a subscription, though free trials are available for learning and experimentation. Subscriptions provide enterprise features ideal for scalable data pipelines and Lakehouse analytics.
Yes! Corporate invoices are available. You can pay via company card or forward an invoice to your finance team.
These options are available on the Sign-Up form.
Yes, discounts are available for teams of 5 or more. Customized corporate training is also offered.
Contact us for group pricing.
Roles include:
These roles are in high demand in technology, finance, healthcare, and e-commerce industries.
Choose based on your organization’s cloud stack and your role goals. Databricks is ideal for engineers working with large-scale, real-time data and modern Lakehouse architectures.
Yes! Hands-on projects cover end-to-end pipelines, cloud integration, Delta Lake management, and real-time analytics—giving you job-ready experience.
Yes. Tool-specific certificates are awarded for Databricks, Delta Lake, SQL, and Power BI, plus a master certificate: “Data Engineering & BI: Databricks Expert.”
Live, instructor-led classes include exercises, real-world case studies, and Q&A sessions to ensure practical learning.
No. This is a live interactive course, but you’ll receive assignments, templates, and documentation for practice.
Class notes and exercises are provided to catch up, and you can attend the same session in a future batch (subject to availability).
Yes, sessions can be retaken in future batches; full re-enrollment may require an additional fee.
Unlike pre-recorded courses, you’ll work on real-world Databricks datasets with expert guidance and personalized feedback, making it more practical and career-focused.
More questions ?
End-to-End Data Pipelines
Cloud Data Integration
Real-Time Processing
Data Modeling & Transformation
Lakehouse & Delta Table Management
Data Visualization
Cloud Workflow Automation
ETL Optimization
AI-Driven Analytics
Data Governance & Security
Cloud Analytics Collaboration
Scalable Solution Design
Mr. Sami
MCT, MCP, MEE, MOS
30,000+
Students Trained
18+
Year of Experience
4.9
Reviews
Mr. Sami is an exceptionally accomplished and certified Microsoft Trainer, possessing extensive expertise in the fields of Finance, HR, and Information Technology. With an impressive 14-year tenure in the industry, he has successfully trained and empowered over 23,000 professionals, and the number continues to grow.
He has undertaken assignments with the renowned IRS, The World Bank, Tata Chemicals, Buckman Laboratories, Standard Chartered, ING Barings and much more. His nature of going that Extra Mile has got him the startling popularity amongst the Excelgoodies prominent clients.
Build Real-World Solutions During the Course
The Excelgoodies Difference
We Spot Trends Before They Become Industry Standards
The analytics industry moves fast. We move faster. We constantly update our courses to match the latest industry needs, so you’re always learning what’s in demand—before everyone else.
Learn What Matters, Not Just What’s Trending
BI & Analytics isn’t about knowing one tool—it’s about knowing how to use the right tools together. Our courses don’t just teach software; they teach end-to-end reporting, automation, and cloud-driven analytics workflows—exactly what businesses need.
Tech-Enabled Learning,
Zero Hassles
Forget scattered emails and outdated PDFs. Our AI-powered student portal keeps everything in one place—live classes, assignments, progress tracking, instructor feedback, invoices, and instant support—so you stay focused on learning.
Real Projects, Real Experience, Real Confidence
No more theory-only learning—you’ll walk out of our courses with proven expertise in the tools and techniques hiring managers want.
Daniel Carter
Business Associate
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Sami is great, a lot to learn but helps guide through the process and move at a pace which is workable for all.
Dean Mckinney | Finance Director | Republic Of Media | UK (Power BI Training)
Its been great learning something new again. Very interactive sessions. Thanks Sami for taking us through everything set by step and ensuring we all understood everything
Hazel Lowe | Systems Support Analyst | Complementary Pathways (Full Stack BI Reporting & Automation Course)
Sami's very knowledgeable and professional and clearly an expert in what he does. He was patient with us and provided us with a lot of content, giving us the tools to succeed while not giving away the farm sot to speak. I feel like I learned a great deal and looking forward to going back to reference material as well as completing the project for this class completion. thank you.
Susan Rodezno | Analyst, Process Improvement, Corporate Budgeting & Reporting | Essex Property Trust | United States (Power BI Training)
This is an excellent training. Sami is a great and patient instructor. He made sure everyone is on course and understands the topic fully before he moves on, if help is needed beyond class time, he also was there before the class to answer questions. I am really impressed with his knowledge and expertise, it seems he always knows the answer to questions. I will definitely take another class with Sami and Excelgoodies again.
Tao Peng | Information Technology Specialist | Minnesota State College Southeast (Power BI Training)
Great class! Very fast paced and my only issue was not having a computer that could keep up, but the class itself was awesome and I learned a lot. I highly recommend this class! Sami is a great teacher.
Michelle Harris | SC Assoc Analyst | Pepsico (Power BI & SQL Training)
Dean Mckinney
Power BI Training
Finance Director
Republic Of Media
UK
Sami's very knowledgeable and professional and clearly an expert in what he does. He was patient with us and provided us with a lot of content, giving us the tools to succeed while not giving away the farm sot to speak. I feel like I learned a great deal and looking forward to going back to reference material as well as completing the project for this class completion. thank you.
Hazel Lowe
Full Stack BI Reporting & Automation Course
Systems Support Analyst
Complementary Pathways
Its been great learning something new again. Very interactive sessions. Thanks Sami for taking us through everything set by step and ensuring we all understood everything
Susan Rodezno
Power BI Training
Analyst, Process Improvement,Corporate Budgeting & Reporting
Essex Property Trust
US
Sami's very knowledgeable and professional and clearly an expert in what he does. He was patient with us and provided us with a lot of content, giving us the tools to succeed while not giving away the farm sot to speak. I feel like I learned a great deal and looking forward to going back to reference material as well as completing the project for this class completion. thank you.
Tao Peng
Power BI Training
Information Technology Specialist
Minnesota State College Southeast
This is an excellent training. Sami is a great and patient instructor. He made sure everyone is on course and understands the topic fully before he moves on, if help is needed beyond class time, he also was there before the class to answer questions. I am really impressed with his knowledge and expertise, it seems he always knows the answer to questions. I will definitely take another class with Sami and Excelgoodies again.
Michelle Harris
Power BI & SQL Training
SC Assoc Analyst
Pepsico
Great class! Very fast paced and my only issue was not having a computer that could keep up, but the class itself was awesome and I learned a lot. I highly recommend this class! Sami is a great teacher.
Excellent hands on training in manageable chunks. Sami is very knowledgeable and patient with the team, and I would love to attend more of his training programmes.
Excellent Power BI training! Sami is a knowledgeable and well-organised trainer who delivered the content clearly and effectively. I highly recommend his training to anyone looking to enhance their Power BI skills.
Great course to help with reporting. Instructor made sure everyone understood the steps.
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