Deepa

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in SQL Programming Training

8

Class Location

Online class via Zoom

Student's Home

Tutor's Home

Years of Experience in Python Training classes

5

Teaching Experience in detail in Python Training classes

I have hands-on teaching experience focused on PySpark for large-scale data processing and analytics. I train professionals on core PySpark concepts such as RDDs, DataFrames, and Spark SQL. My sessions cover Spark execution fundamentals including DAG, lazy evaluation, stages, and tasks. I teach how to write efficient PySpark code for transformations, actions, and data processing pipelines. Learners are guided on handling large datasets using distributed computing principles. I emphasize performance optimization techniques such as partitioning, caching, and broadcast joins. I provide practical training on working with structured and semi-structured data like JSON, CSV, and Parquet. My courses include real-time ETL use cases and end-to-end data pipeline development. I incorporate hands-on coding sessions to strengthen problem-solving and debugging skills in PySpark. Overall, I focus on helping learners build scalable, optimized, and production-ready PySpark solutions.

Class Location

Online class via Zoom

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Years of Experience in Big Data Training

12

Teaching Experience in detail in Big Data Training

I have hands-on teaching experience in Big Data technologies, focusing on Apache Spark, Databricks, SQL, and AWS-based data engineering. I train working professionals on core Spark concepts such as Driver vs Executor, DAG execution, lazy evaluation, stages, and tasks. My sessions emphasize understanding Spark internals to help learners confidently explain concepts in interviews. I also teach advanced SQL, including complex joins, window functions, and query optimization for real-world scenarios. I integrate SQL with Big Data workflows to ensure end-to-end data processing understanding. I provide practical training on Databricks, including cluster usage, notebooks, and job execution. Learners are guided on performance tuning techniques like caching, partitioning, and join optimization. I use real-time project scenarios such as ETL pipelines and large-scale data transformations. My approach includes hands-on coding in PySpark along with interview-focused question practice. Overall, I focus on bridging the gap between theory and real-world implementation in Big Data.

Class Location

Online class via Zoom

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Years of Experience in Amazon Web Services Training

5

Teaching Experience in detail in Amazon Web Services Training

I have hands-on teaching experience in AWS-based Data Engineering, focusing on building scalable data pipelines. I train professionals on core AWS services such as Amazon S3, AWS Glue, Amazon Redshift, and AWS Lambda. My sessions cover designing end-to-end ETL pipelines using cloud-native architectures. I teach how to process large-scale data using Spark on Databricks integrated with AWS. Learners are guided on data ingestion, transformation, and loading strategies in real-time scenarios. I emphasize performance optimization techniques such as partitioning, file formats, and efficient query design. I also cover orchestration using tools like Apache Airflow and AWS-native scheduling. My teaching includes working with structured and semi-structured data (JSON, Parquet, CSV). I incorporate real-world use cases like data lake architecture and analytics pipelines. Overall, I focus on helping learners build production-ready AWS data engineering solutions with practical exposure.

Class Location

Online class via Zoom

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Years of Experience in Azure Databricks Courses

5

Teaching Experience in detail in Azure Databricks Courses

I have hands-on teaching experience focused on Azure Databricks for building scalable data engineering solutions. I train professionals on core components like Azure Databricks, Azure Data Lake Storage, and Azure Data Factory. My sessions cover Spark fundamentals within Azure Databricks, including clusters, notebooks, and job execution. I teach end-to-end data pipeline development using Azure-native architecture and Databricks workflows. Learners are guided on handling large-scale data using PySpark with real-time use cases. I emphasize performance tuning techniques such as partitioning, caching, and optimizing Spark jobs on cloud storage. I also cover integration of Databricks with ADF for orchestration and pipeline automation. My training includes working with structured and semi-structured data formats like Parquet, JSON, and Delta Lake. I incorporate real-world projects such as building data lakehouse architectures using Medallion (Bronze–Silver–Gold) layers. Overall, I focus on enabling learners to design, build, and explain production-ready Azure Databricks solutions confidently.