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AWS Data Engineer Job Oriented Program

#No.1 Data Engineer Course

Prepzee’s AWS Data Engineering Course has been curated to help you master skills like PySpark, AWS Data Engineer Certification DEA C01, AWS Glue, Kinesis, Airflow, Kafka and Snowflake. This AWS Data Engineering Bootcamp will help you get your dream Job in Data Engineering Domain.

  • Master Data Engineer with AWS,Airflow, Kafka,Snowflake.
  • Hands-On PySpark Training for Data Engineering
  • Clear AWS Data Engineer Certification DEA C01

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Career Transition

This Data Engineering Training program is for you if

AWS Data Engineer Classes Overview

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    100+ Hours of Live Training

    Including Top 2 Data Engineering Tools according to Linkedin Jobs

  • image
    90+ Hours Hands-on & Exercises

    Learn by doing multiple labs in your learning journey.

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    8+ Projects & Case Studies

    Get a feel of AWS Data Engineering professionals by doing real-time projects.

  • image
    24*7 Technical Support

    Call us, E-Mail us whenever you stuck.

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    Learn from the Top 1% of Experts

    Instructors are Microsoft Certified Trainers.

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    Lifetime Live Training Access

    Attend multiple batches until you achieve your Dream Goal.

What You will Learn in the Program?

  • Module 1

    Python for Data Engineering 

    12 Hours

    Python Fundamentals

    Setting up Python Virtual Environment

    Implementing Conditional Statements

    Working with Loops

     Exploring Numeric Data Types(Numbers)

    Understanding Tuples and Their Operations

    Understanding Functions in Python

     Working with OOP Concepts

    Standard Libraries in Python

    Exception Handling in Python

  • Module 2

    SQL for Data Engineering

    6 Hours

    Aggregations, Grouping, Sorting, and Pivoting

    Exploring Different Types of SQL JOINs

     SQL Regular Expressions

    SQL Coding Exercises

  • Module 3

    Data Engineering Essentials

    6 Hours

    Understanding Structured, Unstructured, and Semi-Structured

    Properties of Data: Volume, Velocity, and Variety

    Comparing Data Warehouses and Data Lakes

    Managing and Orchestrating ETL Pipelines for Data Processing

    Data Modeling, Data Lineage, and Schema Evolution

    Optimizing Database Performance

  • Module 4

    PySpark for Data Engineering

    16 Hours

    Introduction to PySpark Shell

    Submitting PySpark Jobs for Execution

    Navigating the Spark Web User Interface

    Exploring Dataframes and Spark SQL

    Comparing Dataframes with RDDs

    Understanding the Lazy Evolution of Dataframes

    Analyzing Directed Acyclic Graph (DAG) Stages and Spark Jobs

    Optimizing Dataframes for Performance

     Interoperating with RDDs: Bridging the Gap

    Handling JSON and Parquet File Formats

     Spark Streaming: Concept and Applications

    Spark Streaming for Real-Time Data Processing

  • Module 5

    AWS Data Engineer Associates DEA-C01 Certification

    40 Hours

     

    Understanding Compute Services in AWS

    Understanding AWS Storage Services

    Database Services: DynamoDB, RDS, Redshift

    Amazon Database Migration Service

    AWS DataSync

    AWS Snow Family 

    Amazon Management and Governance

    AWS CloudFormation

    AWS CloudWatch

    Traditional ETL vs AWS Glue

    AWS Glue DataBrew

    Implementing a Glue ETL Job

    Understanding Amazon Kinesis Data Streams

    Kinesis Data Firehose

    Querying Data with Amazon Athena SQL

    Troubleshooting Amazon Athena Queries

    Overview of Hadoop and Serverless EMR

    Comparing EMR with AWS Glue for ETL

    Introduction to Amazon OpenSearch 

    Understanding QuickSight Pricing and Creating Dashboards

    Exploring AWS Step Functions

    Overview of Amazon SageMaker

    Utilizing SageMaker Feature Store

    Understanding Identity and Access Management (IAM)

     

  • Module 6

    Kafka/ Airflow for Data Engineering

    10 Hours

    Understanding Managed Streaming for Apache Kafka (MSK) 

    Provisioning Amazon MSK Cluster

    Exploring MSK – Connect and Serverless Features

    Processing Streaming Data with Amazon MSK

    Introduction to Managed Workflows for Apache Airflow (MWAA)

    DAGs and Components for MWAA

    Setting Up Managed Airflow Environment

    Building Workflows with Managed Apache Airflow

  • Module 7

    DevOps for Data Engineering

    6 Hours

    Understanding of ECS: Elastic Container Service

    Understanding ECR: Elastic Container Registry

    Overview of EKS: Elastic Kubernetes Service

    AWS CodeCommit

    AWS CodeBuild

    AWS CodeDeploy

    AWS CodePipeline

  • Module 8

    Compute with Snowflake

    16 Hours

    Introduction of SnowFlake Data Warehousing Service

    SnowFlake Architecture

    Complete Setup of SnowFlake

    Create Data Warehouse on SnowFlake

    Analytical Queries on SnowFlake Data Warehouse

    Understand the entire Snowflake workflow from end-to end

    Undestanding SnowPark (Execute PySpark Application on SnowFlake)

  • Module 9

    AWS Data Engineer Certification Preperation

    5 Hours

    Solve AWS Data Engineer  DEA-C01 Sample Exam Papers

    Multiple Quizzes

    Mock Certification Exam

Program Creators

Neeraj

Amazon Authorised Instructor

14+ Years of experience

Sidharth

Amazon Authorised Instructor

15+ Years of experience

Nagarjuna

Microsoft Certified Trainer

12+ Years of experience

KK Rathi

Microsoft Certified Trainer

17+ Years of experience

Where Will Your Career Take Off?

  • AWS Data Engineer

    Responsible for designing, implementing, and maintaining data pipelines and infrastructure on AWS, ensuring efficient data processing and analysis.

  • Cloud Data Engineer

    A Cloud Data Engineer specializes in managing data on cloud platforms, designing scalable solutions using cloud-native tools and services.

  • Data Integration Engineer

    Integrates data from multiple sources into a unified ecosystem, designing and implementing data integration workflows.

  • AWS Data Architect

    Designs data architectures on AWS, defining data models and storage structures to meet business requirements

  • AWS Platform Data Engineer

    The AWS Platform Data Engineer creates and manages data solutions on AWS, ensuring optimal performance and security. They develop scalable pipelines.

  • Specialist Data Engineer

    They designs and implements tailored data solutions using advanced tools, focusing on data modeling, pipeline development, and governance for optimal performance and reliability.

Skills Covered

Tools Covered

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Placement Overview

  • 500+
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  • 9 Days
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AWS Data Engineer Job Oriented ProgramLearning Path

Course 1

online classroom pass

AWS Data Engineer Job Oriented Program

Embark on your journey towards a thriving career in AWS data engineering with best Data Engineering courses. This comprehensive program is meticulously crafted to empower you with the skills and expertise needed to excel in the dynamic world of data engineering.Learn Data Engineering with Prepzee, throughout the program, you’ll explore a wide array of essential tools and technologies, including industry favorites like PySpark, Kafka and Airflow.Dive into industry projects, elevate your CV and LinkedIn presence, and attain mastery in Data Engineer technologies under the mentorship of seasoned experts.

1.1: Exploring Python Fundamentals

1.2: Applications and Use Cases of Python

1.3: Setting up Python Virtual Environment

1.4: Utilizing Pip Installer

1.5: Understanding Visual Studio Code/PyCharm

1.6: Understanding Values, Types, and Variables

1.7: Exploring Operands and Expressions

1.8: Implementing Conditional Statements

1.9: Working with Loops

1.10: Parsing Command Line Arguments

1.11: Outputting Data to the Screen

1.12: Managing File Input and Output in Python

1.13: Exploring Numeric Data Types(Numbers)

1.14: Manipulating Strings and Related Operations

1.15: Understanding Tuples and Their Operations

1.16: Exploring Lists and Their Operations

1.17: Working with Dictionaries and Their Operations

1.18: Utilizing Sets and Their Operations

Hands On:

  • Developing a “Hello World” Program
  • Illustrating Conditional Statements in Python
  • Demonstrating Various Types of Loops
  • Analyzing Tuple Properties and Operations, Contrasting with Lists
  • Exploring List Properties and Operations
  • Understanding Dictionary Properties and Operations
  • Manipulating Sets: Properties and Operations

2.1: Understanding Functions in Python

2.2: Exploring Different Types of Function Parameters

2.3: Managing Global Variables

2.4: Understanding Variable Scope and Returning Values

2.5: Exploring Lambda Functions and their Applications

2.6: Introduction to Object-Oriented Programming (OOP) Concepts

2.7: Utilizing Standard Libraries in Python

2.8: Exploring Various Modules Used in Python

2.9: Understanding Import Statements in Python

2.10: Navigating the Module Search Path

2.11: Different Ways of Package Installation

Hands On:

  • Practical Application of Functions: Syntax, Arguments, Keyword Arguments, Return Values
  • Exploring Lambda Functions: Features, Syntax, Comparison with Traditional Functions
  • Sorting Data: Sequences, Dictionaries, Addressing Limitations of Sorting
  • Handling Errors and Exceptions: Identifying Types of Issues, Implementing Remediation Strategies
  • Managing Packages and Modules: Understanding Modules, Import Options, Configuring sys Path

3.1: Analyzing Spark Components and its Architectural Framework

3.2: Introduction to PySpark Shell

3.3: Submitting PySpark Jobs for Execution

3.4: Navigating the Spark Web User Interface (UI)

3.5: Developing Your Initial PySpark Job Utilizing Visual Studio Code

Hands On:

  • Constructing and Executing a Spark Application
  • Examining the Spark Application Web User Interface (UI)
  • Understanding Various Spark Properties

 

4.1: Identifying Challenges in Traditional Computing Methods

4.2: Exploring Potential Solutions and How RDD Addresses the Challenges

4.3: Understanding RDD: Definition, Operations, Transformations, and Actions

 

5.1: Introduction to Dataframes: Concept and Utility

5.2: Comparing Dataframes with RDDs

5.3: Understanding Dataframe Structure and Data Copying Operations

5.4: Dataframe Transformations and Actions: Manipulating Data Effectively

5.5: Differentiating between Wide and Narrow Transformations

5.6: Understanding the Lazy Evolution of Dataframes

5.7: Analyzing Directed Acyclic Graph (DAG) Stages and Spark Jobs

5.8: Optimizing Dataframes for Performance

5.9: Utilizing Spark SQL Functions and Aggregations

5.10: Working with Hive Tables and SQL Queries

5.11: Exploring the Need for Spark SQL 

5.12: Understanding Spark SQL

5.13: Implementing User-Defined Functions (UDFs)

5.14: Interoperating with RDDs: Bridging the Gap

5.15: Handling JSON and Parquet File Formats

5.16: Loading Data from Various Sources

Hands On:

  • Creating Dataframes Using Spark SQL
  • Loading and Transforming Data from Diverse Sources
  • Performing Stock Market Analysis
  • Integrating Spark with Hive for Seamless Data Access

 

6.1: Identifying Limitations in Traditional Computing Approaches

6.2: Understanding the Importance and Need for Streaming Data Processing

6.3: Introduction to Spark Streaming: Concept and Applications

6.4: Key Features of Spark Streaming for Real-Time Data Processing

6.5: Navigating the Spark Streaming Workflow

6.6: Exploring Streaming Context and Discretized Streams (DStreams)

6.7: Applying Transformations on DStreams for Data Manipulation

6.8: Understanding Windowed Operators and their Significance

6.9: Essential Windowed Operators: Slice, Window, and ReduceByWindow

6.10: Utilizing Stateful Operators for Managing State Across Batches

Hands On:

  • Implementing a WordCount Program using Spark Streaming for Real-Time Data Analysis

 

7.1: Reviewing SQL Essentials: Aggregations, Grouping, Sorting, and Pivoting

7.2: Exploring Different Types of SQL JOINs

7.3: Introduction to SQL Regular Expressions: A Brief Overview

7.4: A Word on SQL Coding Exercises

Hands On:

  • Practice with Aggregation Queries in SQL
  • Practice with Grouping Queries in SQL
  • Practice with Join Queries in SQL 

 

 

8.1: Understanding Different Types of Data: Structured, Unstructured, and Semi-Structured

8.2: Exploring the Properties of Data: Volume, Velocity, and Variety

8.3: Comparing Data Warehouses and Data Lakes (Including Lakehouses)

8.4: Introduction to “Data Mesh” Concept and Its Implications

8.5: Managing and Orchestrating ETL Pipelines for Data Processing

8.6: Common Data Sources and Formats in Data Engineering

8.7: Brief Overview of Data Modeling, Data Lineage, and Schema Evolution

8.8: Optimizing Database Performance 

8.10: Exploring Data Sampling Techniques 

8.11: Understanding Data Skew Mechanisms

8.12: Implementing Data Validation and Profiling

9.1: Understanding Compute Services in AWS

9.2: Introduction to Amazon EC2: Elastic Compute Cloud

9.3: Exploring AWS Batch for Batch Computing Jobs

9.4: Introduction to ECS: Elastic Container Service

9.5: Understanding ECR: Elastic Container Registry

9.6: Overview of EKS: Elastic Kubernetes Service

9.7: Introduction to AWS Lambda and Serverless Computing

9.8: Invoking and Monitoring Lambda Functions

9.9: Understanding AWS SAM: Serverless Application Model 

Hands On:

  •  Introduction to AWS Lambda Functions
  • Accessing the Amazon Web Services Console
  • Navigating and Exploring AWS Lambda
  • Developing a Fresh Lambda Function
  • Grasping Concepts and Testing the Sample IoT Lambda Function

10.1: Understanding AWS Storage Services

10.2: Overview of Amazon S3: Simple Storage Service

10.3: Exploring Storage Classes 

10.4: Introduction to Versioning 

10.5: Utilizing Server-Access Logging for Enhanced Security

10.6: Implementing Object-Level Logging for Detailed Analysis

10.7: Understanding Object Lock for Data Governance

10.8: Utilizing Policies to Control Access in Amazon S3

10.9: Implementing Amazon S3 Replication for Disaster Recovery

10.10: Securing Data with Bucket Key Encryption

10.11: Utilizing Lifecycle Configurations for Efficient Data Management

10.12: Components and Creation of Lifecycle Configurations

10.13: Exploring Other Considerations and Limitations in Amazon S3

10.14: Introduction to Amazon Elastic Block Store (EBS)

10.15: Understanding Amazon Data Lifecycle Manager

10.16: Exploring Amazon Elastic File System (EFS)

10.17: Storage Classes and Performance Options in EFS

10.18: Creating and Managing EFS File Systems

10.19: Ensuring EFS Security and Importing Data

10.20: Introduction to AWS Backup for Data Protection

Hands On:

  • Optimizing Data Storage in S3 for Analytical Purposes
  • Accessing the Amazon Web Services Console
  • Establishing Connection to the Virtual Machine via EC2 Instance Connect
  • Transferring Data from EC2 to S3
  • Organizing Data in S3 for Effective Usage with Athena
  • Converting Data Files in S3 to Formats Compatible with Athena
  • Compressing Data Files in S3 for Improved Performance with Athena

Hands On:

  • Managing S3 Object Events with Lifecycle Policies and Server Access Logging
  • Accessing the Amazon Web Services Console
  • Creating a New Amazon S3 Bucket
  • Configuring Lifecycle Policies on the S3 Bucket
  • Activating Server Access Logging on the S3 Bucket

11.1: Introduction to Database Services in AWS(DEA-C01)

11.2: Amazon Relational Database Service: Overview 

11.3: Walkthrough: Setting Up an Amazon RDS Database

11.4: Purchasing Options for RDS Instances

11.5: Pricing for Database Storage and I/O

11.6: Cost Breakdown for Backup Storage

11.7: Understanding Backtrack Storage Pricing

11.8: Snapshot Export Costs

11.9: Data Transfer Pricing in AWS

11.10: Introduction to Amazon DynamoDB

11.11: Core Features of DynamoDB

11.12: DynamoDB Terminology: Understanding the Basics

11.13: Comparative Analysis of DynamoDB with Other Database Solutions

11.14: Interacting with DynamoDB: Console Operations

11.15: Data Manipulation in DynamoDB via Code

11.16: Querying and Scanning DynamoDB Programmatically

11.17: Performance Optimization Strategies for DynamoDB

11.18: Provisioning Table Access in DynamoDB

11.19: Best Practices for DynamoDB Table Modeling

11.20: Creating and Deleting DynamoDB Tables Using the AWS Console

11.21: Introduction to Partitioning in DynamoDB

11.22: Achieving Balance in DynamoDB Partitioning

11.23: DynamoDB Accelerator (DAX): An Overview

11.24: Introduction to Amazon Neptune: A Graph Database Service

Demo:Deploying an Amazon Neptune Database

11.25: Introduction to Amazon Redshift: Features and Benefits

11.26: Overview of Redshift’s COPY Command

11.27: Upserting Data in Amazon Redshift

11.28: Resizing Operations in Amazon Redshift

11.29: Concurrency Scaling in Amazon Redshift

11.30: Understanding Data Distribution in Redshift

11.31: Different Types of Distribution Styles in Redshift

11.32: Distribution Keys vs. Sort Keys: A Comparative Overview

11.33: Example Illustration of Distribution Styles

11.34: Managing Cold Data in Amazon Redshift

11.35: Introduction to Amazon Redshift Spectrum

11.36: Step-by-step Guide to Executing a Spectrum Query

11.37: Insights into Spectrum Internals

11.38: Considerations for Amazon Redshift Spectrum

11.39: Cost Implications of Amazon Redshift Spectrum

11.40: Exploring Materialized Views in Amazon Redshift

11.41: Utilizing the VACUUM Command in Amazon Redshift

11.42: Data Sharing in Amazon Redshift

11.43: Leveraging the Data API for Amazon Redshift

11.44: Introduction to Amazon Redshift Serverless

11.45: Amazon DocumentDB: MongoDB Compatibility in AWS

Demo: Setting Up an Amazon DocumentDB Cluster

11.46: Amazon Keyspaces: Apache Cassandra in AWS

Demo: Creating a Keyspace and Table in Amazon Keyspaces

11.47: Amazon MemoryDB for Redis: An Overview

Hands On: DynamoDB Essentials

  • Accessing the AWS Management Console
  • Provisioning a DynamoDB Table with a Partition Key
  • Configuring DynamoDB Table with Local and Global Secondary Indexes
  • Inserting Data Items into DynamoDB Table
  • Updating Records within DynamoDB Table
  • Querying Data in DynamoDB
  • Deleting a DynamoDB Table

 

Hands On : Introduction to Amazon Redshift

    • Accessing the AWS Management Console
    • Setting Up a Redshift Cluster
    • Establishing Connection to the Virtual Machine using EC2 Instance Connect
    • Loading Data into Redshift Cluster
    • Executing Sample Queries on Redshift
    • Resizing the Redshift Cluster for Optimization
    • Environment Cleanup After Lab Completion                                         

Hands On : Configuring Distribution Styles and Access Control in Amazon Redshift

      • Accessing the AWS Management Console
      • Introduction to Configuring and Securing Amazon Redshift
      • Connecting to the Amazon Redshift Cluster
      • Creating Tables and Setting Distribution Styles
      • Configuring Column-Level Access Controls

 

12.1: Introduction to Networking and Content Delivery (DEA-C01)

12.2: Understanding Virtual Private Cloud (VPC)

12.3: Exploring Subnets in VPC

12.4: Network Access Control Lists (NACLs) Overview

12.5: Security Groups: Enhancing Network Security

12.6: NAT Gateway: Facilitating Outbound Internet Traffic

12.7: VPN & Direct Connect: Secure Network Connectivity Options

12.8: Analyzing VPC Flow Logs for Network Monitoring

12.9: VPC Peering: Connecting VPCs Securely

12.10: Exploring VPC Endpoints for Service Access

12.11: Introduction to AWS PrivateLink

12.12: Working with Amazon CloudFront for Content Delivery

12.13: Common Patterns for Implementing Amazon CloudFront

12.14: Introduction to Amazon Route S3 DNS Service

12.15: Managing DNS Records with Amazon Route S3

12.16: Performing Health Checks with Amazon Route S3

12.17: Understanding Routing Policies in Amazon Route S3

12.18: Configuring Traffic Flow with Amazon Route S3

12.19: Utilizing Amazon Route S3 Resolver for Hybrid Clouds

12.20: Overview of Application Recovery Controller

12.21: Summary of Using Amazon Route S3

 

13.1: Introduction to Migration and Transfer

13.2: Exploring Application Discovery Service & Application Migration Service

13.3: Understanding AWS Database Migration Service (AWS DMS)

Hands On:

  • Experience with AWS Database Migration Service (AWS DMS)
  • Introduction to AWS DataSync
  • Overview of AWS Snow Family 
  • Hands-On Lab with AWS Snow Family
  • Overview of AWS Transfer Family for Secure File Transfer

 

 

14.1: Introduction to Management and Governance (DEA-C01)

14.2: Understanding Amazon CloudWatch

14.3: Exploring CloudWatch Dashboards 

14.4: CloudWatch Subscriptions 

14.5: AWS CloudTrail: Tracking API Activity in AWS

14.6: Configuring AWS Config for Governance and Compliance

14.7: Introduction to AWS Systems Manager for Operational Management

14.8: Evaluating the Role of Systems Manager in the AWS Tool Set

14.9: Managing Resource Groups in AWS

14.10: Requirements and Building Blocks of AWS Systems Manager

14.11: Utilizing AWS Systems Manager Parameter Store 

14.12: Introduction to AWS CloudFormation 

14.13: Understanding the Structure of a CloudFormation Template

14.14: Building a CloudFormation Template: Hands-On Demonstration

14.15: Deploying a CloudFormation Template: Practical Exercise

14.16: Overview of Amazon Managed Grafana 

14.17: Introduction to AWS Budgets for Cost Management

14.18: Analyzing Costs with Cost Explorer in AWS

15.1: Introduction to Security, Identity, and Compliance (DEA-C01)

15.2:Understanding Identity and Access Management (IAM)

15.3: Features of IAM and User Dashboard Overview

15.4: Creating and Managing IAM Users

15.5: User Group Management with IAM

15.6: IAM Roles and Their Users

15.7: Leveraging AWS Service Roles for Resource Access

15.8: Granting Temporary Access with IAM User Roles

15.9: Federated Access with IAM Roles

15.10: AWS Policy Types and Structure

15.11: Crafting AWS IAM Policies

15.12: Understanding Encryption and AWS Key Management Service (KMS)

15.13: Exploring Amazon Macie 

15.14:Parameters and Secrets Management

15.15: Introduction to DDoS and AWS Shield

15.16: Overview of AWS WAF and Rule Groups

15.17: Creating and Configuring Web ACL 

15.18: Introduction to CloudHSM

Hands On:

  • Accessing the AWS Management Console
  • Learning Key Management Service (KMS) Terms
  • Enabling CloudTrail with S3 Logging and Encryption
  • Creating a Customer Master Key (CMK)
  • Launching an EC2 Instance and Creating Encrypted EBS Volume
  • Disabling the Customer Master Key

16.1: Introduction to Analytics

16.2: Understanding AWS Glue and Its Components

16.3: Authoring Solutions in AWS Glue Studio Console

16.4: Data Quality Assessment in AWS Glue

16.5: Modifying Glue Data Catalog from ETL Scripts

16.6: Developing ETL Jobs with Glue ETL

16.7: Cost Considerations and Best Practices for AWS Glue

16.8: Exploring AWS Glue Studio and Data Quality Features

16.9: Introduction to AWS Glue DataBrew

16.10: Demonstrating AWS Glue DataBrew

16.11: Managing PII Data with DataBrew Transformations

16.12: Introduction to AWS Glue Workflows

16.13: Comparing Amazon EMR and AWS Glue for ETL

Hands On: ETL Workloads with AWS Glue

  • Accessing the AWS Management Console
  • Reviewing Data Sources
  • Implementing a Glue ETL Job
  • Executing a Glue ETL Job

17.1: Understanding Amazon Kinesis Data Streams

17.2: Key Components and Scaling of Kinesis Data Streams

17.3: Handling Duplicates and Security in Kinesis Data Streams

17.4: Introduction to Kinesis Data Firehose

17.5: Troubleshooting and Performance Tuning for Kinesis Data Streams

17.6: Overview of Kinesis Analytics and Amazon Managed Service for Apache Flink (MSAF)

Hands On: Processing Streaming Metadata with Amazon Kinesis Data Streams

  • Accessing the AWS Management Console
  • Creating an Amazon Kinesis Data Stream
  • Establishing Connection to EC2 Instance
  • Utilizing Data Stream with API Gateway
  • Consuming Data Stream Records with AWS Lambda

Hands On : Sessionizing Clickstream Data with Amazon Kinesis and Managed Apache Flink

  • Accessing the AWS Management Console
  • Starting a Managed Apache Flink Studio Notebook
  • Connecting to EC2 Instance
  • Simulating Real-Time Clickstream
  • Sessionizing Clickstream Data with Apache Flink
  • Storing Sessions in DynamoDB with Lambda

18.1: Querying Data with Amazon Athena SQL

18.2: Utilizing Athena for Apache Spark

18.3: Performance and Transactional Capabilities of Athena

18.4: Fine-Grained Access Control with AWS Glue Data Catalog

18.5: CTAS Functionality and Spark Integration

Hands On: Analyzing Log Data with Kinesis Agent and Amazon Athena

  • Accessing the AWS Management Console
  • Setting Up Kinesis Firehose Delivery Stream
  • Connecting to EC2 Instance
  • Installing and Configuring Kinesis Agent

Hands On: Troubleshooting Amazon Athena Queries

  • Accessing the AWS Management Console
  • Configuring Amazon Athena
  • Troubleshooting Query Performance

19.1: Understanding EMR Characteristics and Architecture

19.2: Integration with AWS Services and Storage

19.3: Overview of Hadoop and Serverless EMR

19.4: Comparing EMR with AWS Glue for ETL

Hands On: Getting Started with Amazon EMR

  • Accessing the AWS Management Console
  • Creating an Amazon S3 Bucket
  • Setting Up an Amazon EMR Cluster
  • Adding Steps to EMR Cluster and Viewing Results

20.1: Understanding Amazon MSK and Kafka Components

20.2: Provisioning Amazon MSK Cluster

20.3: Exploring MSK – Connect and Serverless Features

Hands On: Processing Streaming Data with Amazon MSK

  • Accessing the AWS Management Console
  • Configuring Amazon MSK Cluster
  • Connecting to EC2 Instance
  • Managing Topics and Data Processing

21.1: Introduction to Managed Workflows for Apache Airflow (MWAA)

21.2: DAGs and Components for MWAA

21.3: Setting Up Managed Airflow Environment

Hands On: Building Workflows with Managed Apache Airflow

  • Accessing the AWS Management Console
  • Exploring Managed Airflow Console
  • Creating Data Source and DAG
  • Running and Monitoring DAG Execution

22.1: Introduction to Amazon OpenSearch Service

22.2: Managing OpenSearch Indexes and Designing for Stability

22.3: Optimizing Amazon OpenSearch Service Performance

22.4: Exploring Serverless Options with Amazon OpenSearch

22.5: Overview of Amazon QuickSight

22.6: Understanding QuickSight Pricing and Creating Dashboards

22.7: Leveraging ML Insights with QuickSight

Hands On: Visualizing Data in Amazon QuickSight

  • Accessing the AWS Management Console
  • Opening the Jupyter Notebook for the Lab

23.1: Introduction to Application Integration (DEA-C01)

23.2: Understanding Decoupled and Event-Driven Architecture

23.3: Exploring EventBridge

23.4: Deep Dive into Decoupling Applications with Queuing Services

23.5: Leveraging Simple Notification Service (SNS) for Notifications

23.6: Introduction to AWS Step Functions

Hands On: Introduction to AWS Step Functions

  • Accessing the AWS Management Console
  • Exploring AWS Step Functions
  • Building a Game with AWS Step Functions
  • Configuring Parallel and Conditional Step Types
  • Logging Results to Amazon CloudWatch
  • Creating and Executing a State Machine

24.1: Introduction to Developer Tools (DEA-C01)

24.2: Overview of AWS Command Line Interface (CLI)

24.3: Installing and Configuring AWS CLI

24.4: Managing Credentials and Profiles

24.5: Understanding Command Structure and Output Control

24.6: Exploring Input Features for Ease of Use

24.7: Introduction to AWS Cloud9

Hands On: Creating a Cloud9 Environment

  • Accessing the AWS Management Console
  • Setting Up a Cloud9 Environment
  • Navigating the Cloud9 IDE
  • Introduction to AWS CodeCommit

Hands On: Connecting to an AWS CodeCommit Repository

  • Accessing the AWS Management Console
  • Connecting to a CodeCommit Repository
  • Working with Code in the Repository
  • Introduction to AWS CodeBuild
  • Introduction to AWS CodeDeploy
  • Introduction to AWS CodePipeline
  • Introduction to AWS Cloud Development Kit (CDK)

25.1: Introduction to Machine Learning Concepts

25.1: Overview of Amazon SageMaker

25.1: Utilizing SageMaker Feature Store

25.2: Tracking ML Lineage with SageMaker

25.3: Data Preparation with SageMaker Data Wrangler

 

26.1 Snowflake Overview and Architecture
26.2 Connecting to Snowflake
26.3 Data Protection Features
26.4 SQL Support in Snowflake
26.5 Caching in Snowflake
Query Performance
26.6 Data Loading and Unloading
26.7 Functions and Procedures
Using Tasks
26.8 Managing Security
Access Control and User Management
26.9 Semi-Structured Data
26.10 Introduction to Data Sharing
26.11 Virtual Warehouse Scaling
26.12 Account and Resource Management

27.1: CV Preperation

27.2: Interview Preperation

27.3: LinkedIn Profile Update

27.4: Expert Tips & Tricks

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    Enrolling in the Cloud Master Course by Prepzee was a game-changer for me. The comprehensive curriculum covering AWS, DevOps, Azure, and Python gave me a holistic understanding of cloud technologies. The hands-on labs were invaluable, and I now feel confident navigating these platforms in my career. Prepzee truly delivers excellence in cloud education. The balance between theory and practical application was perfect, allowing me to grasp complex concepts with ease. I'm grateful for the opportunity to have learned from industry experts through this course.

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  • Prepzee's Cloud Master Course exceeded my expectations. The course materials were detailed and insightful, providing a clear roadmap to mastering cloud technologies. The instructors' expertise shone through their teachings, and the interactive elements made learning enjoyable. This course has undoubtedly been a valuable asset to my professional growth.

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    I wanted a comprehensive cloud education, and the Cloud Master Course at Prepzee delivered exactly that. The course content was rich and well-presented, catering to both beginners and those with prior knowledge. The instructors' passion for the subject was evident, and the hands-on labs helped solidify my understanding. Thank you, Prepzee!

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    Prepzee has been a great partner for us and is committed towards upskilling our employee.Their catalog of training content covers a wide range of digital domains and functions, which we appreciate.The best part was there LMS on which videos were posted online for you to review if you missed anything during the class.I would recommend Prepzee to all to boost his/her learning.The trainer was also very knowledgeable and ready to answer individual questions.

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Frequently Asked Questions

Enroll in our Data Engineer Job-Oriented Program and embark on a dynamic journey towards a thriving career in data engineering. This comprehensive program is designed to equip you with the skills and knowledge necessary to excel in the ever-evolving field of data engineering. Throughout this program, you'll delve into a diverse array of tools and technologies that are crucial for data engineers, including popular platforms like PySpark, AWS, and AWS Glue Analytics, Kafka , Airflow among many more.

Prepzee offers 24/7 support to resolve queries. You raise the issue with the support team at any time. You can also opt for email assistance for all your requests. If not, a one-on-one session can also be arranged with the team. This session is, however, only provided for six months starting from your course date.

All instructors at Prepzee are Amazon certified experts with over twelve years of experience relevant to the industry. They are rightfully the experts on the subject matter, given that they have been actively working in the domain as consultants. You can check out the sample videos to ease your doubts.

Prepzee provides active assistance for job placement to all candidates who have completed the training successfully. Additionally, we help candidates prepare for résumé and job interviews.

Projects included in the training program are updated and hold high relevance and value in the real world. Projects help you apply the acquired learning in real-world industry structures. Training involves several projects that test practical knowledge, understanding, and skills. High-tech domains like e-commerce, networking, marketing, insurance, banking, sales, etc., make for the subjects of the projects you will work on. After completing the Projects, your skills will be synonymous with months of meticulous industry experience.

Prepzee's Course Completion Certificate is awarded once the training program is completed, along with working on assignments, real-world projects, and quizzes, with a least 60 percent score in the qualifying exam.

Actually, no. Our job assistance program intends to help you land the job of your dreams. The program offers opportunities to explore competitive vacancies in the corporates and look for a job that pays well and matches your profile and skill set. The final hiring decision will always be based on how you perform in the interview and the recruiter's requirements.

You can enroll for AWS Data Engineer certification DEA C01 certification.

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