Ashok Sahu

UrbanPro Certified Tutor

For Data Modeling

Years of Experience in Data Modeling Training

20

Teaching Experience in detail in Data Modeling Training

I have more than 20 Years of Data Modelling experience. Worked with many Leading Global Companies like IBM, Tech M etc.

Years of Experience in RDBMS Training

10

Teaching Experience in detail in RDBMS Training

High Quality Training. Study materials will be provided

Years of Experience in SQL Programming Training

15

Teaching Experience in detail in SQL Programming Training

ddl, Dml, Dcl etc of all databases like Oracle, Microsoft Sql Server, MySQl, Postgresql, etc

Years of Experience in PL/SQL Classes

10

Teaching Experience in detail in PL/SQL Classes

Years of Experience in Big Data Training

10

Teaching Experience in detail in Big Data Training

Very high quality training. Course contents will be shared

Years of Experience in Azure Data Factory Training

7

Teaching Experience in detail in Azure Data Factory Training

Very high quality training

Years of Experience in Azure Databricks Courses

10

Teaching Experience in detail in Azure Databricks Courses

High-quality training

Years of Experience in Microsoft Azure Training

11

Teaching Experience in detail in Microsoft Azure Training

Day 1: Introduction to Azure Data Services - Overview of Azure architecture and data platform - Azure Storage (Blob, Data Lake Gen2) and Azure SQL Database - Introduction to Azure Synapse Analytics and Azure Databricks Day 2: Data Ingestion & Integration - Azure Data Factory (ADF): pipelines, triggers, and linked services - Copy activity, data flows, and integration runtimes - Ingesting data from on-premises and cloud sources Day 3: Data Transformation & Processing - Azure Databricks: notebooks, clusters, and Spark basics - Data wrangling with PySpark and Delta Lake - Stream processing with Azure Stream Analytics Day 4: Data Storage & Management - Designing partitioning and indexing strategies in Azure Synapse - Managing data lakes with hierarchical namespaces - Implementing data retention and archival policies Day 5: Security, Monitoring & Optimization - Role-based access control (RBAC), managed identities, and key vault integration - Data encryption at rest and in transit - Monitoring pipelines with Azure Monitor and Log Analytics Day 6: Capstone Project & Exam Readiness - Real-world case study: building an end-to-end data pipeline - Performance tuning and cost optimization strategies - DP-203 exam tips, practice questions, and Q&A

Years of Experience in Data Analytics classes

10

Teaching Experience in detail in Data Analytics classes

AWS Data Analytics and other courses

Years of Experience in Microsoft Power BI classes

15

Teaching Experience in detail in Microsoft Power BI classes

Objectives: - Equip participants with end-to-end Power BI skills—from data ingestion to dashboard deployment. - Enable real-time business decision-making through interactive visualizations and DAX-based analytics. - Foster self-sufficiency in report creation and Power BI Service usage. 🛠️ Curriculum Highlights: Day 1: Introduction & Data Preparation - Overview of Power BI ecosystem (Desktop, Service, Mobile) - Connecting to various data sources (Excel, SQL Server, Web APIs) - Data transformation using Power Query Editor (M language basics) Day 2: Data Modeling & Relationships - Creating star schemas and managing relationships - Introduction to DAX (Data Analysis Expressions) - Calculated columns vs. measures Day 3: Visualizations & Storytelling - Building interactive reports with slicers, filters, and bookmarks - Custom visuals and themes - Best practices for data storytelling and user experience Day 4: Advanced Analytics - Time intelligence functions (YTD, QTD, MTD) - What-if parameters and scenario analysis - Row-level security (RLS) implementation Day 5: Publishing & Collaboration - Publishing to Power BI Service - Creating dashboards and setting up data refresh schedules - Sharing, commenting, and using Power BI apps for collaboration Teaching Methodology: - Hands-on labs with real-world datasets (e.g., sales, inventory, customer feedback) - Live demos followed by guided exercises - Group projects: Each team built a dashboard to solve a business case - Daily quizzes and a final capstone presentation

Years of Experience in Tableau Training

15

Teaching Experience in detail in Tableau Training

Objectives: - Introduce participants to Tableau Desktop for data visualization and dashboard creation - Enable users to connect, clean, and analyze data using Tableau’s visual interface - Empower teams to build interactive dashboards for real-time business insights Day 1: Tableau Essentials & Data Connection - Introduction to Tableau ecosystem (Desktop, Prep, Server, Public) - Connecting to data sources (Excel, CSV, SQL databases) - Data interpreter, metadata management, and data types - Live vs. Extract connections Day 2: Visual Analytics & Calculations - Building core charts: bar, line, scatter, maps, and heatmaps - Using filters, parameters, and sets for interactivity - Table calculations and Level of Detail (LOD) expressions - Creating calculated fields for KPIs and metrics Day 3: Dashboards, Storytelling & Publishing - Designing responsive dashboards with containers and layout best practices - Dashboard actions: filter, highlight, and URL actions - Story points for guided analytics - Publishing to Tableau Server/Public and setting up data refresh schedules

Years of Experience in Snowflake Training

10

Teaching Experience in detail in Snowflake Training

Day 1: Snowflake Architecture & Setup - Overview of Snowflake’s multi-cluster shared data architecture - Warehouses, databases, schemas, and virtual warehouses - Setting up roles, users, and access controls - Understanding compute vs. storage separation Day 2: Data Loading & Querying - Loading structured and semi-structured data (CSV, JSON, Parquet) - Using Snowpipe for continuous data ingestion - Querying with SQL: joins, window functions, and CTEs - Time Travel and Fail-safe features Day 3: Data Transformation & Optimization - Creating views, materialized views, and stored procedures - Using Tasks and Streams for ELT workflows - Performance tuning: clustering keys, caching, and query profiling - Cost management and warehouse sizing strategies Day 4: Security, Sharing & Real-World Project - Role-based access control (RBAC) and masking policies - Secure data sharing across accounts and regions - Capstone project: building an end-to-end data pipeline with Snowflake + BI tool (e.g., Tableau or Power BI) - Best practices for production deployment