6 Month Mentorship Program in Data Engineering with Gen AI & Agentic AI

Prerequisites:

• Basic computer knowledge
• Basic mathematics and logical reasoning
• No prior Python or SQL experience required

Topics:

• Data Engineering overview
• Structured vs unstructured data
• Databases and data warehouses
• OLTP vs OLAP
• ETL vs ELT
• Data formats
• Relational database concepts
• Tables, keys and relationships
• Programming fundamentals

Topics:

• Python syntax and data types
• Lists, tuples, sets and dictionaries
• Conditions and loops
• Functions
• Modules and packages
• Exception handling
• File handling
• CSV, JSON and YAML
• Object-oriented programming fundamentals
• Regular expressions
• Virtual environments
• pip
• Logging
• Python database connectivity
• APIs and REST
• Pandas
• NumPy
• Data cleaning and transformation
• Python automation

Topics:

• SELECT and filtering
• Sorting and grouping
• Joins
• Subqueries
• CTEs
• Views
• Window functions
• Stored procedures
• Functions
• Transactions
• Indexes
• Query optimization
• Execution plans
• Data quality queries
• Advanced analytical SQL

Labs:

• Python Data Engineering Environment Lab
• Python File Processing Lab
• CSV/JSON Data Processing Lab
• Python Data Cleaning Lab
• Pandas Transformation Lab
• REST API Data Extraction Lab
• SQL Database Design Lab
• SQL Joins Lab
• Advanced SQL CTE Lab
• SQL Window Functions Lab
• SQL Data Quality Lab
• SQL Query Optimization Lab
• Python-to-SQL Integration Lab
• Automated Data Validation Lab
• Python ETL Automation Lab

Assignments:

• Python data-processing scripts
• CSV data-cleaning assignment
• API data-extraction assignment
• SQL business-analysis queries
• Advanced SQL joins
• Window-function assignment
• Query optimization
• Data-quality validation
• Python database automation

Mini Projects:

• Customer Data Cleaning Pipeline
• Sales Data SQL Analytics System
• API-to-Database Data Loader
• Automated Data Quality Framework

Project Flow:

• API/Files → Python → Data Cleaning → Validation → SQL Database → Analytical Queries → Reports

Scenarios:

• Load millions of customer records
• Remove duplicate records
• Handle missing values
• Extract data from REST API
• Identify failed transactions
• Optimize slow SQL queries
• Validate incoming data
• Automate daily database loading

Troubleshooting:

• Python memory issue
• API timeout
• Invalid JSON
• Database connection failure
• Duplicate records
• SQL query performance issue
• Data-type mismatch
• Failed batch load

Tools:

• Python
• Pandas
• NumPy
• SQL
• PostgreSQL
• MySQL
• SQL Server
• VS Code
• Jupyter
• Git
• REST APIs

Best Practices:

• Modular Python code
• Error handling
• Logging
• SQL optimization
• Parameterized queries
• Data validation
• Version control
• Reusable ETL components

Certifications:

• Microsoft Azure Data Fundamentals
• AWS Certified Data Engineer – Associate
• Google Cloud Associate Data Practitioner
• Python / SQL industry certifications

Prerequisites:

• Python
• SQL
• Database fundamentals

Topics:

• Big Data concepts
• 5 Vs of Big Data
• Distributed computing
• Cluster architecture
• Batch vs streaming
• Distributed storage
• Parallel processing
• Data partitioning
• Data serialization
• Fault tolerance

Topics:

• HDFS
• NameNode
• DataNode
• YARN
• Resource management
• Distributed storage

Topics:

• Spark architecture
• Driver and executors
• SparkSession
• DataFrames
• RDD concepts
• Transformations
• Actions
• Lazy evaluation
• Spark SQL
• Joins
• Aggregations
• Partitioning
• Caching
• Performance optimization

Topics:

• Data ingestion
• Data transformation
• Data cleansing
• Data aggregation
• Window functions
• Structured data processing
• Parquet
• JSON
• CSV
• Partition management

Topics:

• Streaming concepts
• Event-driven processing
• Kafka fundamentals
• Spark Structured Streaming

Labs:

• Hadoop Architecture Lab
• HDFS File Management Lab
• YARN Cluster Lab
• Spark Installation Lab
• Spark DataFrame Lab
• PySpark Data Processing Lab
• PySpark SQL Lab
• Large Dataset Transformation Lab
• Spark Join Optimization Lab
• Partitioning Lab
• Spark Caching Lab
• Parquet Processing Lab
• Kafka Producer/Consumer Lab
• Spark Streaming Lab
• Real-Time Data Processing Lab

Assignments:

• HDFS data management
• PySpark transformation
• PySpark SQL analytics
• Large dataset processing
• Partition optimization
• Spark performance analysis
• Kafka data ingestion
• Streaming analytics

Mini Projects:

• Large-Scale Customer Data Processing
• E-Commerce Big Data Analytics
• PySpark ETL Framework
• Real-Time Transaction Processing

Project Flow:

• Data Sources → Kafka/Files → Spark → PySpark → Transformation → Parquet → Analytics Storage

Scenarios:

• Process billions of records
• Spark job taking too long
• Data skew
• Executor failure
• Kafka consumer lag
• Streaming pipeline delay
• Large-file processing
• Partition optimization

Troubleshooting:

• Spark out-of-memory
• Executor failure
• Data skew
• Shuffle performance
• Kafka lag
• HDFS storage issue
• Failed Spark job
• Corrupt input data

Tools:

• Apache Spark
• PySpark
• Hadoop
• HDFS
• YARN
• Apache Kafka
• Spark SQL
• Parquet
• Avro

Best Practices:

• Correct partitioning
• Avoid unnecessary shuffles
• Use columnar formats
• Optimize joins
• Manage cluster resources
• Monitor Spark jobs
• Implement fault tolerance

Certifications:

• Databricks Data Engineer certifications
• Cloudera Data Engineering certifications
• Confluent Kafka certifications
• Cloud data engineering certifications

Prerequisites:

• Python
• SQL
• Big Data fundamentals
• Basic cloud knowledge

Topics:

• Cloud data architecture
• Cloud storage
• Cloud databases
• Data lakes
• Data warehouses
• Cloud ETL
• Serverless data processing
• IAM
• Security
• Governance
• Cost optimization

Topics:

• S3
• IAM
• Glue
• Athena
• Redshift
• Lambda
• Kinesis
• CloudWatch

Topics:

• Azure Data Lake Storage
• Azure Data Factory
• Azure Synapse
• Azure Databricks
• Event Hubs
• Azure Functions
• Microsoft Entra ID

Topics:

• Cloud Storage
• BigQuery
• Dataflow
• Pub/Sub
• Dataproc
• Cloud Composer

Topics:

• Data lake
• Data warehouse
• Lakehouse
• Medallion architecture
• Batch pipelines
• Streaming pipelines
• Security
• Governance
• Cost management

Labs:

• AWS S3 Data Lake Lab
• AWS Glue ETL Lab
• AWS Athena Analytics Lab
• Redshift Data Warehouse Lab
• Azure Data Lake Lab
• Azure Data Factory Lab
• Azure Synapse Lab
• Azure Databricks Integration Lab
• GCP Cloud Storage Lab
• BigQuery Lab
• Pub/Sub Data Ingestion Lab
• Cloud IAM Security Lab
• Cloud Data Pipeline Lab
• Cloud Monitoring Lab
• Multi-Cloud Data Architecture Lab

Assignments:

• Cloud data-lake architecture
• AWS ETL pipeline
• Azure Data Factory pipeline
• BigQuery analytics
• Cloud security design
• Cloud data warehouse design
• Cloud cost optimization
• Batch and streaming architecture

Mini Projects:

• AWS Cloud Data Lake
• Azure Enterprise Data Pipeline
• GCP Analytics Platform
• Cloud-Based Sales Data Warehouse

Project Flow:

• Sources → Cloud Storage → ETL → Data Lake → Data Warehouse → BI/Analytics

Scenarios:

• Data pipeline failed overnight
• Cloud storage access denied
• ETL job delay
• High cloud data-processing cost
• Duplicate cloud records
• Data warehouse query performance issue
• IAM permission failure
• Cloud data ingestion failure

Troubleshooting:

• IAM AccessDenied
• Storage connectivity
• Glue/Data Factory failure
• BigQuery query issue
• Pipeline timeout
• Network connectivity
• Data schema mismatch
• Cloud job failure

Tools:

• AWS S3
• Glue
• Athena
• Redshift
• Kinesis
• Azure Data Factory
• ADLS
• Synapse
• Databricks
• Event Hubs
• BigQuery
• Dataflow
• Pub/Sub
• Dataproc

Best Practices:

• Secure IAM
• Encryption
• Data classification
• Cost monitoring
• Lifecycle management
• Partitioning
• Data governance
• Monitoring and alerting

Certifications:

• AWS Certified Data Engineer – Associate
• Microsoft Certified: Azure Data Engineer Associate
• Google Professional Data Engineer
• AWS Solutions Architect – Associate

Prerequisites:

• Python
• SQL
• Spark fundamentals
• Cloud fundamentals

Topics:

• Data Lake
• Data Warehouse
• Lakehouse
• Delta Lake
• Medallion Architecture
• ACID transactions
• Schema enforcement
• Schema evolution
• Data versioning
• Data governance

Topics:

• Workspace
• Clusters
• Notebooks
• Jobs
• Workflows
• SQL Warehouses
• Unity Catalog
• Access control

Topics:

• Delta tables
• ACID transactions
• Time travel
• MERGE
• UPDATE
• DELETE
• Schema evolution
• OPTIMIZE
• VACUUM
• Z-Ordering concepts

Topics:

• Bronze layer
• Silver layer
• Gold layer

Topics:

• PySpark
• Delta Live Tables / modern pipeline concepts
• Data quality
• Incremental processing
• CDC
• Streaming
• Performance optimization

Labs:

• Databricks Workspace Lab
• Cluster Configuration Lab
• Databricks Notebook Lab
• PySpark Data Engineering Lab
• Delta Table Lab
• Delta MERGE Lab
• Time Travel Lab
• Schema Evolution Lab
• Bronze Layer Lab
• Silver Layer Lab
• Gold Layer Lab
• Unity Catalog Lab
• Incremental Data Processing Lab
• Delta Optimization Lab
• Databricks Streaming Lab
• Data Quality Pipeline Lab

Assignments:

• Build Bronze pipeline
• Build Silver transformation
• Build Gold analytics layer
• Delta table management
• CDC implementation
• Data-quality framework
• Databricks performance optimization
• Unity Catalog security

Mini Projects:

• Retail Lakehouse
• Customer 360 Lakehouse
• E-Commerce Medallion Architecture
• Real-Time Lakehouse Analytics

Project Flow:

• Raw Data → Bronze → Silver → Gold → Data Warehouse/BI

Scenarios:

• Delta table performance degradation
• Duplicate records
• Schema changes
• Failed incremental load
• CDC issue
• Databricks cluster performance problem
• Data-quality failure
• Access-control issue

Troubleshooting:

• Spark job failure
• Cluster startup failure
• Delta schema conflict
• Slow transformation
• Data skew
• Duplicate records
• Streaming checkpoint failure
• Permission problems

Tools:

• Databricks
• Apache Spark
• PySpark
• Delta Lake
• Unity Catalog
• Azure Data Lake
• AWS S3
• SQL
• Git
• Power BI

Best Practices:

• Medallion architecture
• Delta format
• Incremental processing
• Data quality
• Partition optimization
• Governance
• Access control
• Cost-aware cluster management

Certifications:

• Databricks Certified Data Engineer Associate
• Databricks Certified Data Engineer Professional
• Azure Data Engineer Associate
• AWS Data Engineer – Associate

Prerequisites:

• Python
• SQL
• Cloud
• Spark/Databricks fundamentals

Topics:

• ETL vs ELT
• Pipeline architecture
• Batch processing
• Streaming
• DAGs
• Scheduling
• Dependencies
• Retry mechanisms
• Data lineage
• Pipeline monitoring
• Data quality

Topics:

• Architecture
• DAGs
• Tasks
• Operators
• Sensors
• Scheduling
• Variables
• Connections
• XCom
• Task dependencies
• Retries
• Backfills
• Monitoring

Topics:

• Pipelines
• Activities
• Datasets
• Linked services
• Triggers
• Integration Runtime
• Parameters

Topics:

• Incremental loads
• CDC
• Full vs incremental processing
• API ingestion
• File ingestion
• Database ingestion
• Data validation
• Error handling
• Dead-letter processing
• Metadata-driven pipelines

Topics:

• Kafka
• Event Hubs
• Spark Structured Streaming

Labs:

• Airflow Installation Lab
• First DAG Lab
• Airflow Scheduling Lab
• Task Dependency Lab
• Retry & Failure Handling Lab
• Airflow API Ingestion Lab
• Database ETL DAG Lab
• ADF Pipeline Lab
• Incremental Pipeline Lab
• CDC Pipeline Lab
• Metadata-Driven Pipeline Lab
• Data Quality Pipeline Lab
• Kafka Streaming Pipeline Lab
• Pipeline Monitoring Lab
• Production Pipeline Recovery Lab

Assignments:

• Airflow DAG creation
• ETL orchestration
• Incremental loading
• API pipeline
• CDC pipeline
• ADF orchestration
• Pipeline monitoring
• Failure recovery
• Metadata-driven architecture

Mini Projects:

• Automated Sales ETL Pipeline
• Customer Data Pipeline
• API Data Ingestion Platform
• CDC-Based Data Pipeline
• Real-Time Streaming Pipeline

Project Flow:

• Multiple Sources → Ingestion → Validation → Transformation → Lakehouse/Warehouse → Monitoring → Alerting

Scenarios:

• Daily pipeline failed
• Dependency failure
• API unavailable
• Duplicate ingestion
• Late-arriving data
• Schema change
• Pipeline SLA breach
• Failed Spark task
• Backfill requirement

Troubleshooting:

• DAG failure
• Scheduler failure
• Task timeout
• Connection failure
• API authentication
• Data-quality failure
• Duplicate data
• Missing records
• Kafka consumer lag

Tools:

• Apache Airflow
• Azure Data Factory
• Databricks Workflows
• AWS Glue Workflows
• Kafka
• Spark Structured Streaming
• dbt
• Git
• Great Expectations

Best Practices:

• Idempotent pipelines
• Modular DAGs
• Retry strategies
• Data validation
• Pipeline observability
• Alerting
• Metadata management
• Dependency management
• Secure credentials

Certifications:

• Astronomer Airflow certifications/training
• Microsoft Azure Data Engineer Associate
• AWS Data Engineer – Associate
• Databricks Data Engineer certifications

Prerequisites:

• Python
• SQL
• Data Engineering fundamentals
• APIs
• Cloud fundamentals
• Basic ML/AI concepts helpful but not mandatory

Topics:

• AI vs ML vs GenAI
• LLM fundamentals
• Tokens and embeddings
• Prompt engineering
• Vector databases
• RAG architecture
• AI agents
• Tools and function calling
• Agent memory
• Data governance
• AI security
• Responsible AI

Topics:

• LLM architecture concepts
• Prompt engineering
• Structured output
• Function calling
• AI-assisted SQL
• AI-assisted Python
• AI-assisted data transformation
• AI-generated documentation
• AI-assisted data-quality analysis
• AI-assisted pipeline troubleshooting

Topics:

• Document ingestion
• Chunking
• Embeddings
• Vector search
• Retrieval
• Context construction
• RAG pipelines
• Metadata filtering
• RAG evaluation
• RAG security

Topics:

• AI agents
• Agent architecture
• Tool calling
• Function calling
• Planning
• Memory
• Multi-step workflows
• Human-in-the-loop
• Agent orchestration
• Data-engineering agents

Topics:

• Natural-language-to-SQL
• Data-quality agent
• Pipeline monitoring agent
• Data catalog assistant
• Metadata agent
• Data lineage assistant
• Incident investigation agent
• Automated documentation
• Data engineering copilot

Topics:

• Prompt injection
• Data leakage
• Sensitive data protection
• Access control
• RAG security
• Agent permissions
• Tool/API security
• AI output validation

Labs:

• LLM API Integration Lab
• Prompt Engineering for Data Engineers
• AI SQL Assistant Lab
• AI Python Code Assistant Lab
• AI Data Transformation Lab
• Embedding Generation Lab
• Vector Database Lab
• RAG Pipeline Lab
• RAG Data-Quality Lab
• Natural Language to SQL Lab
• Data Documentation Agent Lab
• Data Pipeline Monitoring Agent Lab
• Data Quality Agent Lab
• Agent Tool-Calling Lab
• Multi-Step Data Engineering Agent Lab
• AI-Assisted Incident Investigation Lab
• RAG Security Testing Lab
• Agent Security & Access-Control Lab

Assignments:

• Prompt engineering
• AI-generated SQL validation
• RAG architecture
• Vector search
• Natural-language analytics
• Data-quality agent
• Pipeline monitoring agent
• Data documentation agent
• Agent tool-calling
• AI security assessment

Mini Projects:

• GenAI SQL Data Assistant
• AI Data Quality Assistant
• RAG-Based Enterprise Data Assistant
• Data Engineering Documentation Agent
• AI Pipeline Monitoring Agent

Project Flow:

• User → LLM → RAG → Metadata/Data Catalog → SQL/Data Sources → Response

Project Flow:

• Data Source → Pipeline → Data Quality → Monitoring → AI Agent → Investigation → Recommendation → Human Approval → Action

Scenarios:

• Business user asks a natural-language data question
• Pipeline failure requires AI-assisted investigation
• Data-quality anomaly is detected
• Schema change needs analysis
• AI generates SQL for analyst validation
• Data engineer needs pipeline documentation
• Data catalog requires automated descriptions
• Production incident requires log analysis
• RAG assistant must answer from enterprise documentation
• Agent needs controlled access to data tools

Troubleshooting:

• Incorrect AI-generated SQL
• Hallucinated answer
• RAG retrieval failure
• Poor document chunking
• Incorrect embeddings
• Vector-search mismatch
• Prompt injection
• Sensitive data leakage
• Agent tool failure
• API rate limit
• Incorrect agent action
• Context-window limitations

Tools:

• Python
• OpenAI / Azure OpenAI
• Databricks
• LangChain
• LangGraph
• LlamaIndex
• Vector databases
• REST APIs
• SQL
• Apache Airflow
• Spark / PySpark
• MLflow
• Cloud AI services
• RAG frameworks
• Agent orchestration frameworks

Best Practices:

• Validate AI-generated SQL and code
• Never blindly trust LLM output
• Implement access control
• Protect sensitive data
• Use grounded RAG
• Monitor retrieval quality
• Log AI interactions
• Restrict agent tools and permissions
• Human approval for high-impact operations
• Test prompts and agents continuously
• Maintain data lineage and governance

Certifications:

• Databricks Generative AI / AI-related certifications
• Microsoft Azure AI Engineer Associate
• AWS Certified AI Practitioner
• Google Cloud Generative AI certifications
• Cloud Data Engineering certifications