Topics:

• What is Data Engineering

• Data Engineer Roles & Responsibilities

• Difference Between ETL Developer & Data Engineer

• Types of Data

• Steaming Vs Batch Data

Topics:

• Cloud Introduction and GCP Basics

• GCP Implementation Models: IaaS, PaaS, SaaS

• Overview of GCP Data Engineer Role

• Understanding GCP Storage Components

• Introduction to GCP ETL & Streaming Components

Topics:

• Google Cloud SQL Deployment and Management

• Introduction to BigQuery: Serverless Data Warehouse

• Performance Tuning: Understanding Slots and Query Pricing

• Managing IAM Roles and Secure Connections (e.g., VPC, Firewalls)

Topics:

• GCP Resources and Resource Types

• Introduction to Google Dataflow and Google Dataproc

• Basic Concepts of Data Movement and Processing

Topics:

• BigQuery Architecture: Storage, Query Engine, and Dremel

• Data Loading and Unloading with BigQuery

• Table Creation, Partitioning, and Clustering for Performance Optimization

• Managing Workloads and Query Optimization

Topics:

• Google Dataflow Concepts: Pipelines, PCollections, and Transforms

• Constructing ETL Pipelines with Dataflow

• Integrating Dataflow with GCS, Cloud SQL, BigQuery, and other GCP Services

• Monitoring and Debugging Dataflow Jobs

Topics:

• Incremental Data Loading and Handling On-Premise Data Sources

• Advanced Dataflow Features: Windows, Triggers, and Stateful Processing

• Implementing Real-Time Data Integration with Pub/Sub

Topics:

• Integrating BigQuery with Google Cloud Storage for Big Data Queries

• Utilizing BigQuery ML for Machine Learning Inside Data Warehousing

• Performance Optimization and Data Transformation Techniques

Topics:

• Security Measures with Google Cloud Identity and Access Management (IAM) and Role-Based Access Control

• Managing Encryption and Security in Dataflow and BigQuery

• Utilizing Google Cloud Marketplace Datasets for Advanced Analytics

Topics:

• GCP Storage Essentials: Buckets, Objects, and Classes

• Introduction to Google Cloud Storage (GCS)

• Configuring and Managing GCS Buckets

• GCS Object Lifecycle Policies and Versioning

Topics:

• Managing GCS: Object Storage and Nearline/Coldline for Archival

• Utilizing Google Cloud Console and gsutil for Efficient Storage Management

• Directory and File Operations in GCS

• Best Practices for Organizing Data in GCS

Topics:

• Implementing Security Measures in Google Cloud Storage

• Access Control with GCS Bucket Policies, ACLs, and IAM Roles

• Encryption Options: Customer-Managed Encryption Keys (CMEK) and Default Encryption

• Compliance Features: HIPAA, PCI-DSS, and Data Sovereignty

Topics:

• Strategies for Database Migrations to GCP

• Integrating Google Cloud SQL with GCS

• Utilizing Google Data Transfer Service and Transfer Appliance

• Data Migration Tools and Techniques (e.g., Database Migration Service)

Topics:

• Advanced Concepts in GCS: Object Lock, Multi-Part Uploads, and Signed URLs

• Data Replication and Cross-Region Replication

• Optimizing Storage Costs with GCS Storage Classes

• Leveraging GCS for Big Data Analytics

Topics:

• Fundamentals of Google Cloud Pub/Sub

• Developing Pub/Sub Pipelines for Real-Time Insights

• Integrating IoT Devices with GCP for Data Streaming

• Processing and Analyzing Streaming Data

Topics:

• Understanding GCP Event Services: Cloud Functions, Cloud Tasks, and Pub/Sub

• Configuring Pub/Sub with Cloud Functions for Real-Time Processing

• Patterns for Real-Time and Event-Driven Data Processing

• Use Cases for Event-Driven Architectures

Topics:

• Monitoring GCP Storage and Pub/Sub Resources with Cloud Monitoring and Logging

• Performance Tuning for GCP Data Services

• Implementing Disaster Recovery and High Availability

• Using Google Cloud Security Command Center for Security and Compliance

Topics:

• Introduction to Python: Overview of Python's history, key features, and comparison with other languages. Setting up the Python environment, writing your first program.

• Core Programming Concepts: Variables, data types, conditional statements, loops, control flow. Introduction to strings, string manipulation, and basic functions.

Topics:

• Deep Dive into Collections: Understanding lists, tuples, dictionaries, sets, and frozen sets. Functions, methods, and comprehensions for collections.

• Functional Programming in Python: Exploring function arguments, anonymous functions, and special functions (map, reduce, filter).

• Object-Oriented Programming (OOP): Classes, objects, constructors, destructors, inheritance, polymorphism. Encapsulation, data hiding, magic methods, and operator overloading.

Topics:

• Mastering Exception Handling: Exception handling mechanisms, try & finally clauses, user-defined exceptions.

• File Handling Essentials: Basics of file operations, handling Excel and CSV files.

• Database Programming: Introduction to database connections and operations with MySQL.

Topics:

• Getting Started with Flask: Setting up Flask, creating simple applications, routing, and middleware.

• Exploring Django: Introduction to Django, MVC model, views, URL mapping.

Topics:

• Automation and Scripting: Enhancing file handling, database automation, and web scraping with BeautifulSoup.

• GUI Development with TKinter: Basics of TKinter for developing desktop applications.

• Version Control with Git: Managing projects with Git, understanding repository management, commits, merging, and basic Git commands.

Topics:

• Cloud Computing Fundamentals: Overview of cloud service models (IaaS, PaaS, SaaS) and deployment models (public, private, hybrid).

• Basics of DevOps: Understanding the DevOps culture, practices, and its significance in cloud environments.

• Data on the Cloud: Exploring cloud storage solutions, databases, and big data services provided by major cloud providers (AWS, Azure, Google Cloud).

• Introduction to Infrastructure as Code (IaC): Concepts and tools for managing infrastructure through code.

Topics:

• Cloud Storage Solutions: Differences between object storage, file storage, and block storage. Use cases for each.

• Cloud Databases: Overview of relational and NoSQL database services in the cloud (e.g., AWS RDS, Azure SQL Database, Google Cloud Firestore).

• Data Warehousing and Big Data Solutions: Introduction to cloud-based data warehousing services (e.g., Amazon Redshift, Google BigQuery, Azure Synapse Analytics).

• Data Migration to Cloud: Strategies and tools for migrating data to cloud environments.

Topics:

• Automated Data Pipelines: Designing and implementing automated data pipelines using cloud services.

• Continuous Integration and Continuous Delivery (CI/CD) for Data: Applying CI/CD practices to data pipeline development, including version control, testing, and deployment strategies.

• Monitoring and Logging: Tools and practices for monitoring cloud resources and data pipelines, understanding logs and metrics for troubleshooting.

• Infrastructure as Code (IaC) for Data Systems: Using IaC tools (e.g., Terraform, CloudFormation) to provision and manage cloud data infrastructure.

Topics:

• Serverless Data Processing: Leveraging serverless architectures for data processing tasks (e.g., AWS Lambda, Azure Functions).

• Containerization and Data Services: Using containers (e.g., Docker, Kubernetes) for deploying and scaling data applications and services in the cloud.

• Machine Learning and AI in the Cloud: Introduction to cloud-based machine learning services and integrating AI capabilities into data pipelines.

• Data Analytics and Visualization: Tools and services for analyzing and visualizing data directly in the cloud (e.g., Amazon QuickSight, Google Data Studio, Power BI on Azure).

Topics:

• Introduction to Databases and SQL: Understanding relational databases and the role of SQL.

• SQL Syntax Overview: Keywords, statements, and clauses.

• Basic SQL Commands: SELECT, FROM, WHERE, and ORDER BY.

• Filtering Data: Using conditions to retrieve specific data (AND, OR, NOT).

Topics:

• Understanding Table Relationships: Primary keys, foreign keys, and the importance of relationships in databases.

• Join Operations: INNER JOIN, LEFT JOIN, RIGHT JOIN, and FULL JOIN.

• Subqueries and Nested Queries: Using subqueries in the SELECT, FROM, and WHERE clauses.

• Aggregating Data: Using GROUP BY and aggregate functions (COUNT, SUM, AVG, MIN, MAX).

Topics:

• Data Manipulation Commands: INSERT, UPDATE, DELETE.

• Managing Tables: Creating and altering tables (CREATE TABLE, ALTER TABLE, DROP TABLE).

• Advanced Filtering Techniques: Using LIKE, IN, BETWEEN, and wildcard characters.

• Working with Dates and Times: Understanding and manipulating date and time data.

Topics:

• Advanced SQL Functions: String functions, mathematical functions, and date functions.

• Window Functions: Overviews of ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG, and their applications.

• Query Performance Optimization: Indexes, query planning, and execution paths.

• Common Table Expressions (CTEs): Writing cleaner and more readable queries with WITH clause.

Topics:

• Analytical SQL for Reporting: Building complex queries to answer analytical questions.

• Pivoting Data: Transforming rows to columns (PIVOT) and columns to rows (UNPIVOT).

• Data Warehousing Concepts: Introduction to data warehousing practices and how they apply to SQL querying.

• Integrating SQL with Data Analysis Tools: Connecting SQL databases with tools like Excel, Power BI, and Python for deeper data analysis.