AWS Data Engineering Training & Certification

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 AWS Basics

AWS Implementation Models: IaaS, PaaS, SaaS

Overview of AWS Data Engineer Role

Understanding AWS Storage Components

Introduction to AWS ETL & Streaming Components

Topics

Amazon RDS (Relational Database Service) Deployment

Amazon Redshift (Data Warehousing) Overview and Setup

Performance Tuning: Understanding Compute, Memory, and Storage

Managing Security Groups and Secure Connections (e.g., SSH, IAM Roles)

Topics

AWS Resources and Resource Types

Introduction to AWS Glue and AWS Lake Formation

Basic Concepts of Data Movement and Processing

Topics

Redshift Clusters, Nodes, and Data Distribution

Data Loading and Unloading with Redshift Spectrum

Table Creation, Compression, and Distribution Keys for Performance

Managing Workloads and Query Optimization

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AWS Glue Concepts: Crawlers, Jobs, and Triggers

Constructing ETL Pipelines with Glue

Integrating Glue with S3, RDS, Redshift, and other AWS Services

Monitoring and Debugging Glue Jobs

Topics

Incremental Data Loading and Handling On-Premise Data Sources

Advanced Glue Features: Data Catalog, Data Batching, and Error Handling

Implementing Real-Time Data Integration with Kinesis Data Firehose

Topics

Integrating Redshift with Athena for Big Data Queries

Utilizing Redshift ML for Machine Learning Inside Data Warehousing

Performance Optimization and Data Transformation Techniques

Topics

Security Measures with AWS Identity and Access Management (IAM) and Role-Based Access Control

Managing Encryption and Security in Glue and Redshift

Utilizing AWS Marketplace Datasets and S3 for Advanced Analytics

Topics

AWS Storage Essentials: Files, Buckets, and Objects

Introduction to Amazon S3 (Simple Storage Service)

Configuring and Managing S3 Buckets

S3 Object Lifecycle Policies and Versioning

Topics

Managing S3: Object Storage, Glacier for Archival

Utilizing AWS S3 Console and CLI for Efficient Storage Management

Directory and File Operations in AWS S3

Best Practices for Organizing Data in S3

Topics

Implementing Security Measures in AWS S3

Access Control with S3 Bucket Policies, ACLs and IAM Roles

Encryption Options: S3-Managed, SSE-S3, SSE-KMS, and Client-Side Encryption

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

Topics

Strategies for Database Migrations to AWS

Integrating Amazon RDS with S3

Utilizing AWS Data Pipeline for Data Movement and Transformation

Data Migration Tools and Techniques (e.g., AWS DMS)

Topics

Advanced Concepts in S3: Object Lock, Multi-Part Uploads, and Presigned URLs

Data Replication and Cross-Region Replication

Optimizing Storage Costs with S3 Intelligent-Tiering and Storage Classes

Leveraging S3 for Big Data Analytics

Topics

Fundamentals of AWS Kinesis (Data Streams, Firehose, and Analytics)

Developing Stream Analytics Jobs for Real-Time Insights

Integrating IoT Devices with AWS for Data Streaming

Processing and Analyzing Streaming Data

Topics

Understanding AWS Event Services: SNS, SQS, and Lambda

Configuring Kinesis with Lambda for Real-Time Processing

Patterns for Real-Time and Event-Driven Data Processing

Use Cases for Event-Driven Architectures

Topics

Monitoring AWS Storage and Kinesis Resources with CloudWatch - Performance Tuning for AWS Data Services - Implementing Disaster Recovery and High Availability - Using AWS Config, CloudTrail, and GuardDuty for Security and Compliance

Topics

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

Introduction to AWS EMR (Elastic MapReduce): Configuration, Cluster Management

Spark on AWS EMR: Configurations, Node Types, and Resource Management

Using HDFS, S3, and Glue with EMR

Topics

Integrating Python with Spark: PySpark Basics

Data Loading Techniques: Using PySpark for Data Ingestion and Processing

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

Advanced Data Transformation: Working with Spark SQL for Data Analytics

Topics

Configuring AWS S3 for use with EMR

Data Management: Reading and Writing Data to S3 using PySpark and Scala

Secure Data Access: Managing Permissions and Security between EMR and S3

Topics

Understanding EMR Architecture: Master, Core, and Task Nodes, RDDs, and DAGs

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

Implementing Best Practices for Reliable Data Lakes with Delta Lake Concepts

Topics

Machine Learning Fundamentals in EMR: Using MLlib and SageMaker for Predictive Modeling

Data Exploration and Visualization: Leveraging Notebooks for Insights

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

Topics

EMR Security: Integrating with AWS IAM and VPCs

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

Compliance and Data Governance: Best Practices in EMR Environments

Topics

Streaming Data with EMR: Concepts and Practical Applications

Integrating Kinesis and Redshift with EMR for Real-Time Analytics

Processing Live Data Streams: Building and Deploying Stream Analytics Solutions

Topics

Automating Workflows with AWS Step Functions and EMR

CI/CD for EMR: Automation and Version Control Integration

Deployment Strategies: Best Practices for Production Deployments in AWS

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.