Nalla Surendhar

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Azure Databricks Courses

14

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Big Data Training

5

Teaching Experience in detail in Big Data Training

Having strong technical knowledge of PySpark with coding and scenario based situations. PySpark is the Python API for Apache Spark, designed to process and analyze large volumes of data efficiently using distributed computing. It allows data engineers to perform data transformation, cleansing, aggregation, filtering, and complex analytics across multiple machines. PySpark supports DataFrames, Spark SQL, Structured Streaming, and integration with cloud platforms such as Azure and AWS. It is widely used in modern data engineering pipelines, especially with Databricks, Delta Lake, and large-scale ETL workflows.

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Python Training classes

5

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Computer Classes

15

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in MS SQL Development Training

5

Teaching Experience in detail in MS SQL Development Training

I have experience teaching MS SQL Development with a strong focus on practical, real-time development concepts. My training covers SQL fundamentals through advanced topics, including database design, DDL, DML, DQL, joins, subqueries, CTEs, views, stored procedures, functions, triggers, temporary tables, indexes, and transactions. I teach students how to write efficient and optimized SQL queries using real-world business scenarios. Key areas include complex joins, aggregations, window functions such as ROW_NUMBER, RANK, and DENSE_RANK, handling NULL values, duplicate records, date functions, string functions, and conditional logic. I also provide hands-on training in stored procedure development, query optimization, indexing strategies, execution plans, normalization, constraints, and transaction management. The training includes practical exercises on developing SQL solutions for reporting, data validation, data transformation, and troubleshooting performance issues. My teaching approach combines concepts, coding demonstrations, interview-oriented questions, and real-time SQL development scenarios so that learners can confidently write, debug, optimize, and maintain MS SQL Server queries and database objects.

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Azure Data Factory Training

5

Teaching Experience in detail in Azure Data Factory Training

I have experience providing practical training on Azure Data Factory (ADF), focusing on both fundamental concepts and real-time data engineering scenarios. My training covers the complete ADF workflow, from creating linked services and datasets to designing, executing, monitoring, and troubleshooting data pipelines. I teach core ADF components such as pipelines, activities, datasets, linked services, integration runtimes, triggers, parameters, and variables. The training includes hands-on practice with Copy Activity, Lookup, ForEach, If Condition, Filter, Set Variable, Execute Pipeline, Stored Procedure, Web Activity, and Get Metadata activities. A major focus of my training is building real-time ETL and ELT pipelines using Azure Data Factory. I explain how to move data between different sources and destinations such as SQL Server, Azure SQL Database, Azure Data Lake Storage Gen2, Blob Storage, and other cloud or external sources. I also cover incremental data loading using watermark columns, change data capture (CDC), metadata-driven pipelines, dynamic parameterization, schema mapping, error handling, retry mechanisms, pipeline dependencies, and monitoring. I provide practical scenarios such as loading multiple tables dynamically, handling failures, avoiding duplicate loads, and implementing restartable pipelines. The training also includes Integration Runtime concepts, including Azure IR and Self-hosted Integration Runtime, along with security practices such as Azure Key Vault integration and secure credential management. My teaching approach combines theoretical explanations, live demonstrations, hands-on exercises, interview-oriented questions, and real-world project scenarios. The objective is to help learners independently design, develop, monitor, troubleshoot, and optimize Azure Data Factory pipelines in enterprise data engineering environments.

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in ETL Training

5

Teaching Experience in detail in ETL Training

I teach different approaches to data extraction from relational databases, flat files, APIs, and other structured and semi-structured sources. The transformation topics include data cleansing, filtering, joining, aggregations, deduplication, data validation, data type conversions, handling NULL values, business-rule implementation, and data quality checks. I also provide hands-on training on designing ETL workflows, incremental and full loads, change data capture (CDC), Slowly Changing Dimensions (SCD Type 1 and Type 2), surrogate keys, lookup operations, error handling, logging, auditing, and restartable ETL processes. The training includes practical scenarios such as processing large volumes of data, handling duplicate and invalid records, implementing incremental loads, managing dependencies between ETL workflows, and troubleshooting failed jobs. My teaching approach combines theoretical concepts, practical demonstrations, hands-on exercises, real-world business scenarios, and interview-oriented questions. The objective is to help learners understand ETL architecture and develop reliable, scalable, and maintainable ETL pipelines