AWS Certified Data Engineer - Associate - Hands On + Exams from Udemy

What's inside

Learning objectives

Deep dive into aws data analytics services: gain comprehensive knowledge of the various aws services tailored for data analytics, including amazon redshift, kin
Architect data solutions on aws: understand best practices and strategies to design, deploy, and maintain scalable and reliable data analytics solutions on the

Data security and management in aws: learn about aws's robust data security measures, governance tools, and best practices to manage, store, and secure data eff
Prepare for the aws certification: equip themselves with the necessary knowledge and confidence to take and pass the aws certified data analytics - specialty ex

Deep dive into aws data analytics services: gain comprehensive knowledge of the various aws services tailored for data analytics, including amazon redshift, kin
Architect data solutions on aws: understand best practices and strategies to design, deploy, and maintain scalable and reliable data analytics solutions on the
Data security and management in aws: learn about aws's robust data security measures, governance tools, and best practices to manage, store, and secure data eff
Prepare for the aws certification: equip themselves with the necessary knowledge and confidence to take and pass the aws certified data analytics - specialty ex

Syllabus

Data Engineering Concepts

Roles & Responsibilities of a Data Engineer

Types of Data Storage Systems

ACID vs BASE

OLTP vs OLAP

4 V's of Big Data

Data Engineering Concepts - Part 1

Data Engineering Concepts - Part 2

Big Data Technologies

Big Data Technologies - Part 1

Big Data Technologies - Part 2

Big Data Technologies - Part 3

Big Data Technologies - Part 4

Big Data Technologies - Part 5

How to apply for the Exam + 30 minutes extra for ESL + 50% Discount for 2nd time

Domain 1: Collection

Kinesis Data Streams

Amazon Kinesis Data Streams - Hands On Demo

Kinesis Data Firehose

Kinesis Data Analytics

Amazon Managed Streaming for Apache Kafka (MSK)

Amazon Managed Streaming for Apache Kafka (MSK) - Hands On Demo

AWS Data Migration Service (DMS)

Amazon Simple Queue Service (SQS)

Amazon Simple Queue Service (SQS) - Hands On Demo

Amazon MQ

Amazon Simple Notification Service (SNS)

Amazon Simple Notification Service (SNS) - Hands On Demo

AWS Direct Connect

AWS Snow Family

Domain 2: Storage and Data Management

Amazon Simple Storage Service (S3) - Part 1

Amazon Simple Storage Service (S3) - Part 2

Amazon Simple Storage Service (S3) - Part 3

Amazon Simple Storage Service (S3) - Part 4

Amazon Simple Storage Service (S3) Hands On Demo

Amazon Elastic Block Store (EBS)

Amazon Elastic File System (EFS)

AWS Backup

Amazon DynamoDB

DynamoDB - Local Secondary Indexes (LSI) and Global Secondary Indexes (GSI)

DynamoDB - Hands On Demo

DynamoDB Accelerator (DAX)

DynamoDB Read Capacity Units (RCUs) and Write Capacity Units (WCUs)

Amazon RDS

Amazon RDS - Hands On Demo

Amazon Elasticache

Amazon Elasticache - Hands On Demo

Amazon Keyspaces

Amazon Keyspaces - Part 2

Amazon Keyspaces - Hands On Demo

Domain 3: Processing

Amazon EC2

AWS Glue - Part 1

AWS Glue - Part 2

AWS Glue - Part 3

AWS Glue - Hands On Demo

Elastic MapReduce (EMR)

Elastic MapReduce (EMR) - Hands On Demo

AWS Lambda

AWS Lambda - Hands On Demo

AWS Step Functions

AWS Step Functions - Hands On Demo

Clean Up

AWS Lake Formation

Domain 4: Analysis and Visualization

Amazon Athena - Part 1

Amazon Athena - Part 2

Amazon Athena - Hands On Demo

Redshift

Amazon Redshift - Hands On Demo

Amazon OpenSearch

Amazon OpenSearch - Hands On Demo

Amazon QuickSight

Amazon Neptune

Amazon Neptune - Hands On Demo

Amazon DataZone

Amazon Timestream

Amazon Virtual Private Cloud (VPC)

Amazon Virtual Private Cloud (VPC) - Part 1

Amazon Virtual Private Cloud (VPC) - Part 2

Domain 5: Security

AWS Security - Part 1

AWS Security - Part 2

AWS Data Analytics Reference Architecture

Practice Questions and Answers with Explanations

Part 1 - 10 Questions

Part 2 - 10 Questions

Part 3 - 10 Questions

Part 4 - 10 Questions

Part 5 - 10 Questions

Part 6 - 10 Questions

Part 7 - 10 Questions

Part 8 - 10 Questions

Part 9 - 10 Questions

Part 10 - 10 Questions

Part 11 - 10 Questions

Part 12 - 10 Questions

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides hands-on demos for services like Kinesis, MSK, SQS, S3, DynamoDB, RDS, Elasticache, Keyspaces, Glue, EMR, Lambda, Step Functions, Athena, Redshift, OpenSearch, and Neptune, which reinforces practical skills

Covers a wide range of AWS services, including Kinesis, MSK, DMS, SQS, SNS, S3, EBS, EFS, DynamoDB, RDS, Elasticache, Keyspaces, EC2, Glue, EMR, Lambda, Step Functions, Athena, Redshift, OpenSearch, Neptune, DataZone, and Timestream

Includes practice questions and answers with explanations, which helps learners prepare for the AWS Certified Data Engineer - Associate certification exam

Explores data engineering concepts like ACID vs BASE, OLTP vs OLAP, and the 4 V's of Big Data, which are fundamental to understanding data processing and storage

Includes information on how to apply for the exam, including extra time for ESL learners and a discount for a second attempt, which is helpful for test-takers

Requires learners to have an AWS account and familiarity with the AWS Management Console, which may pose a barrier to entry for some beginners

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in AWS Certified Data Engineer - Associate - Hands On + Exams with these activities:

Review Data Warehousing Concepts

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Reinforce your understanding of data warehousing principles, which are fundamental to using services like Redshift effectively.

Browse courses on Data Warehousing

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Review the differences between OLTP and OLAP systems.
Study common data warehouse architectures.
Practice designing star and snowflake schemas.

Read 'Designing Data-Intensive Applications'

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Gain a deeper understanding of the underlying principles of data systems to make informed decisions about AWS service selection and architecture.

View Secret Colors on Amazon

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Read the chapters on data storage and retrieval.
Study the sections on distributed systems and fault tolerance.
Relate the concepts to AWS services like Redshift and DynamoDB.

Practice SQL Queries on Sample Datasets

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Sharpen your SQL skills, which are essential for querying and manipulating data in services like Redshift, Athena, and DynamoDB.

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Set up a local database environment (e.g., PostgreSQL).
Download sample datasets (e.g., from Kaggle).
Write SQL queries to perform data analysis and transformations.
Practice optimizing queries for performance.

Four other activities

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Write a Blog Post on AWS Data Engineering Best Practices

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Solidify your understanding by explaining AWS data engineering concepts and best practices in a clear and concise manner.

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Choose a specific topic related to AWS data engineering.
Research the topic thoroughly.
Write a blog post explaining the concepts and best practices.
Include code examples and diagrams to illustrate your points.

Build a Data Pipeline with AWS Services

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Apply your knowledge by building an end-to-end data pipeline using various AWS services covered in the course.

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Design a data pipeline architecture.
Ingest data using Kinesis or SQS.
Store data in S3 and DynamoDB.
Process data with Glue or EMR.
Visualize data with QuickSight.

Review 'AWS Certified Data Analytics Study Guide'

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Prepare for the AWS certification exam by reviewing a dedicated study guide.

View AWS Certified Cloud Practitioner Study Guide... on Amazon

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Read each chapter carefully.
Complete the practice questions at the end of each chapter.
Take the sample exams to assess your readiness.

Contribute to an Open-Source Data Engineering Project

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Gain practical experience and contribute to the data engineering community by working on an open-source project.

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Find an open-source data engineering project on GitHub.
Review the project's documentation and code.
Identify an area where you can contribute (e.g., bug fix, new feature).
Submit a pull request with your changes.

Career center

Learners who complete AWS Certified Data Engineer - Associate - Hands On + Exams will develop knowledge and skills that may be useful to these careers:

Cloud Data Engineer

A Cloud Data Engineer is responsible for designing, building, and maintaining data infrastructure in the cloud. This involves using cloud services to collect, store, and process data for analysis and reporting. As this course covers core AWS data analytics services like Amazon Redshift, Kinesis, and Athena, it provides a strong foundation for a career as a cloud data engineer. It delves into data security, management, and architectural best practices on AWS, essential for efficient data handling. The course gives hands-on experience, which is valuable for those looking to step into this role. It could be particularly useful to learn about database technologies like DynamoDB, along with data migration and processing tools like AWS Glue.

See salaries and explore the career path for Cloud Data Engineer

Data Warehouse Engineer

A Data Warehouse Engineer designs, develops, and maintains data warehouses. Data warehouses are the backbone of business intelligence, and this role builds a system that is efficient, reliable, and scalable. This course covers several AWS services, specifically Amazon Redshift, that are used to build data warehouses in the cloud. The focus on data storage solutions, practical experience with data transformation using AWS Glue, and security practices on AWS, are particularly important. This course can help build a career in this area. It allows an engineer to gain proficiency in creating a modern data warehouse.

See salaries and explore the career path for Data Warehouse Engineer

ETL Developer

An ETL Developer is responsible for building data pipelines to extract, transform, and load data from various sources into a data warehouse or data lake. This course, with its focus on AWS services like AWS Glue, Kinesis, and S3, is a great starting point to become an ETL developer. The course allows the learner to work with the kinds of tools used to build and manage these pipelines. This course also covers data security and management on AWS, important for any ETL developer who must ensure data integrity. It provides the hands-on experience that is valuable to those looking for this role.

See salaries and explore the career path for ETL Developer

Data Architect

A Data Architect designs and oversees the implementation of an organization's data management systems. They must have strong understanding of data storage, processing, and retrieval, often focusing on the big picture of how data flows through an organization. This course provides a solid foundation in AWS data analytics services. It includes best practices for designing, deploying, and maintaining data solutions on the AWS cloud. Knowing these AWS services, including Redshift and Athena, helps a data architect make informed decisions about data infrastructure. The course also covers security and data management, which are important for a data architect who must ensure data integrity and compliance.

See salaries and explore the career path for Data Architect

Cloud Solutions Architect

Cloud Solutions Architects design and implement cloud computing solutions for businesses. They must understand cloud platforms like AWS, and be able to develop strategies that meet business needs. This course helps build proficiency in AWS analytics services, such as Redshift, Kinesis and Athena, which are used to build robust cloud-based solutions. The course gives hands-on experience and also teaches best practices of data security and management on AWS. These are important for solutions architects. The course's focus on AWS data analytics provides tools and knowledge a Cloud Solutions Architect needs.

See salaries and explore the career path for Cloud Solutions Architect

Analytics Engineer

An Analytics Engineer focuses on transforming raw data into formats that are suitable for analysis. This role combines elements of data engineering and data analysis. This course is a great place to start a journey as an Analytics Engineer. It provides a solid understanding of AWS services for data storage, processing, and analytics. The course's focus on services, such as AWS Glue, along with database technologies like DynamoDB, can help an engineer handle data transformation and management. The practical experience with AWS services is very useful for anyone wishing to work in the field.

See salaries and explore the career path for Analytics Engineer

Cloud Architect

A Cloud Architect is responsible for designing and overseeing the implementation of cloud computing strategies. It requires a broad knowledge of cloud services and security best practices. This course, with its deep dive into AWS data analytics services, including Redshift, Kinesis, and Athena, can be very useful for a Cloud Architect. An understanding of data storage, management, and security on AWS is important to build scalable and reliable cloud solutions. The course is also useful as it covers architectural concepts, which are key for cloud architects who wish to create well designed and integrated systems.

See salaries and explore the career path for Cloud Architect

Cloud Consultant

A Cloud Consultant helps organizations adopt and optimize cloud technologies. This involves a thorough understanding of cloud platforms and their services, along with best practices in cloud security and data management. This course is helpful, as it covers numerous AWS data services. These include Redshift, Kinesis, and Athena. As a cloud consultant, deep knowledge of these services can allow you to provide useful guidance to clients. This course is also useful to consultants that wish to focus specifically on AWS as it provides a roadmap into the specific services offered.

See salaries and explore the career path for Cloud Consultant

Database Administrator

A Database Administrator is responsible for the performance, integrity, and security of a database. This may involve setting up, maintaining, and supporting database systems. This course, with its deep dives into Amazon RDS, DynamoDB, and other database services on AWS, is a great start to a career as a database administrator. Understanding database technologies, such as Amazon Keyspaces, and how they fit into the AWS ecosystem are key features of the course. The course content on data migration, security, and management are all important for a database administrator, and would be useful to someone in this role.

See salaries and explore the career path for Database Administrator

Data Operations Engineer

A Data Operations Engineer is responsible for managing and monitoring data systems and ensuring data quality, availability, and reliability. This course may be particularly helpful because it covers AWS services for data storage, processing, and analytics. The course covers data security, management, and architecture. These topics are important for any data operations engineer who must make sure data systems function smoothly and that data is consistently reliable and secure. The course gives hands-on experience, which may be useful to data operations engineers.

See salaries and explore the career path for Data Operations Engineer

Business Intelligence Developer

Business Intelligence Developers create and manage business intelligence solutions. This requires an understanding of databases, data warehousing, and data visualization tools. This course's detailed exploration of AWS data services like Redshift, Athena, and QuickSight can help a business intelligence developer create effective dashboards and reports. Understanding how to access data from cloud platforms is often key in such a role. This course is useful to BI developers who wish to integrate cloud-based data sources into their solutions as it provides them hands-on experience working with AWS analytics services.

See salaries and explore the career path for Business Intelligence Developer

Data Analyst

A Data Analyst examines and interprets data to help businesses make better decisions. While this course focuses on data engineering, the knowledge of AWS data services like Athena and Quicksight can be very useful to a data analyst. The course helps develop the skills needed to access data, analyze it, and visualize it. It helps you become more familiar with data processing and management on AWS cloud platforms. The course's exploration of data visualization with QuickSight may be useful. This course may be helpful in understanding how data is stored in cloud systems, allowing the analyst to more effectively work with their data.

See salaries and explore the career path for Data Analyst

Solutions Engineer

A Solutions Engineer collaborates with clients to understand their business challenges. They then design and implement solutions that meet their specific requirements. This role often requires a combination of technical expertise and client-facing skills. This course is useful for a solutions engineer who wishes to work with clients that use AWS. The course covers AWS data analytics services like Redshift and Athena. It also provides hands-on knowledge of data management and security. The course helps one understand how a system is designed using AWS services, which is useful to recommend these solutions to clients.

See salaries and explore the career path for Solutions Engineer

Machine Learning Engineer

A Machine Learning Engineer builds and maintains machine learning models. This includes the data pipelines that feed into these models. This course, with its focus on AWS data services like Kinesis, Amazon S3, and AWS Glue, may be helpful for machine learning engineers. Knowledge of these services may help with building data ingestion, transformation, and storage pipelines, key prerequisites for an effective machine learning system. This course also may provide the opportunity to understand how data is processed in the cloud and can help those who wish to work with machine learning on the cloud.

See salaries and explore the career path for Machine Learning Engineer

Data Science Consultant

A Data Science Consultant helps organizations solve their business problems using data. This role demands expertise in data analysis, data modeling, and often requires cloud expertise. This course may be helpful to a data science consultant, as it covers AWS data analytics services like Redshift and Athena, along with best practices in data management and security on AWS. A consultant needs to have an understanding of cloud technologies as clients increasingly use cloud services. The course may help those who wish to advise clients who use AWS to store and analyze their data.

See salaries and explore the career path for Data Science Consultant

AWS Certified Data Engineer - Associate - Hands On + Exams

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