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:

GCP Cloud Overview: Understanding SaaS, PaaS, IaaS in GCP

Introduction to Google Cloud Dataproc: Configuration, Cluster Management

Spark on Google Cloud Dataproc: Configurations, Node Types, and Resource Management

Using HDFS, GCS, and BigQuery with Dataproc

Topics:

Integrating Python with Spark: PySpark Basics

Data Loading Techniques: Using PySpark for Data Ingestion and Processing

Utilizing SQL in Dataproc: Creating and Managing Spark DataFrames and SQL Queries

Advanced Data Transformation: Working with Spark SQL for Data Analytics

Topics:

Configuring Google Cloud Storage (GCS) for use with Dataproc

Data Management: Reading and Writing Data to GCS using PySpark and Scala

Secure Data Access: Managing Permissions and Security between Dataproc and GCS

Topics:

Understanding Dataproc Architecture: Master, Worker, and Preemptible Worker Nodes, RDDs, and DAGs

Building and Monitoring Dataproc Jobs: Scheduling, Task Management, and Optimization

Implementing Best Practices for Reliable Data Lakes with Delta Lake Concepts

Topics:

Machine Learning Fundamentals in Dataproc: Using MLlib and AI Platform for Predictive Modeling

Data Exploration and Visualization: Leveraging Notebooks for Insights

Advanced Analytic Techniques: Utilizing Scala and Python for Complex Data Analysis

Topics:

Dataproc Security: Integrating with Google Cloud IAM and VPCs

Managing Permissions: IAM Policies, Security Groups, and Data Security

Compliance and Data Governance: Best Practices in Dataproc Environments

Topics:

Streaming Data with Dataproc: Concepts and Practical Applications

Integrating Pub/Sub and BigQuery with Dataproc for Real-Time Analytics

Processing Live Data Streams: Building and Deploying Stream Analytics Solutions

Topics:

Automating Workflows with GCP Cloud Composer and Dataproc

CI/CD for Dataproc: Automation and Version Control Integration

Deployment Strategies: Best Practices for Production Deployments in GCP

Topics

1. Introduction to Python

Overview of Python's history, key features, and comparison with other languages.

Setting up the Python environment, writing your first program. 2. Core Programming Concepts

Variables, data types, conditional statements, loops, control flow.

Introduction to strings, string manipulation, and basic functions.

Topics:

1. Deep Dive into Collections

Understanding lists, tuples, dictionaries, sets, and frozen sets.

Functions, methods, and comprehensions for collections.

2. Functional Programming in Python

Exploring function arguments, anonymous functions, and special functions (map, reduce, filter).

3. Object-Oriented Programming (OOP)

Classes, objects, constructors, destructors, inheritance, polymorphism.

Encapsulation, data hiding, magic methods, and operator overloading.

Topics:

1. Mastering Exception Handling

Exception handling mechanisms, try & finally clauses, user-defined exceptions.

2. File Handling Essentials

Basics of file operations, handling Excel and CSV files.

3. Database Programming

Introduction to database connections and operations with MySQL.

Topics:

1. Getting Started with Flask

Setting up Flask, creating simple applications, routing, and middleware.

2. Exploring Django

Introduction to Django, MVC model, views, URL mapping.

Topics:

1. Automation and Scripting

Enhancing file handling, database automation, and web scraping with BeautifulSoup.

2. GUI Development with TKinter

Basics of TKinter for developing desktop applications.

3. 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.