AWS Data Architect Bootcamp - 43 Services 500 FAQs 20+ Tools (Udemy.com)

Hi! Welcome to the AWS Data Architect Bootcamp course, the only course you need to learn everything about data architecture on AWS and play the role of an Enterprise Data Architect. This is the most-comprehensive AWS course related to AWS data architecture on the market. Here's why:

• This is the only online course taught by an Enterprise Cloud Architect, who leads large teams of junior architects in the real world, who has an industry experience of close to two decades in the IT industry, who is a published author, and leads technology architecture of XXX million dollar projects on cloud for multi-national clients. Data Architects draw a salary in the range of $150K - $250K on an average. This course trains you for that job! This is my 10th course on Udemy, 3rd on AWS topics (previous 2 are best-sellers).

  
  

• Typical AWS classroom trainings on data architecture which contains a fraction of the topics covered in this course, costs $3000 - $5000. And this course teaches you 5 to 7 times more topics than AWS Training (40+ AWS Services) in the fraction of the cost.

  
  

• Everything covered in this course is kept latest. Services which are in Beta and launched in Re-invent (last Nov) are already covered in the course . AWS innovates and adds features to their stack very fast, and I keep my course constantly updated with those changes. Think of this course as a Architecture Updates subscription.

  
  

• Developers have questions, Architect's have questions, Clients have questions - All technical curious minds have questions. And this course also has 500+ questions and answers (FAQs) curated from AWS FAQs, to equip you with as many ready-to-use answers as you would need in your architect role.

  
  

The entire course is formed of 40+ services. Every service is composed of the below listed sections, with their proportion in each section / service.

• Architecture (12%) – Diagrams, Integration, Terminology

• Use-Cases (6%) – Whether and When to use the AWS Service

• Pricing (2%) – Cost estimation methods to assess overall solution cost

• Labs (75%) – To-the-point labs for architectural understanding covering all major and important features

• Frequently Asked Questions (5%) – Selected question from AWS FAQs explained concisely. (Total 500+)

Apart from AWS Services, we will use a number of client tools to operate on AWS Services, Databases and other technology stack. Here is a list of the tools that we would be using:

1. EC2          2. Putty          3. Cloud9,         4. HeidiSQL        5. MySQL Workbench         6. Pgadmin      7. SSMS

8. Oracle SQL Developer         9. Aginity Workbench for Redshift            10. SQL Workbench / J

11. WinSCP          12. AWS CLI              13. FoxyProxy            14. Oracle Virtualbox              15. Linux Shell Commands

16. FastGlacier              17. Rstudio             18. Redis Client               19. Telnet                20. S3 Browser

21. Juypter Notebooks

Below is a detailed description of the curriculum as AWS Services we will be learning to understand how they fit in the overall cloud data architecture on AWS and address various use-cases. If you have any questions, please don't hesitate to contact me.

1. AWS Transfer for SFTP (Nov 2018 Release) - We will start our journey in this course with this service and learn how to ingest files in self-service manner using an sFTP server on AWS and sFTP tools on-premise to ingest file based data on AWS.

   
   

2. AWS Snowball - Large data volumes spanning hundreds of TBs are not ideal for ingestion via network. Using this service, we will learn how to ingest mega volume data using device based offline data transport mechanism to AWS cloud.

   
   

3. AWS Kinesis Data Firehose - One of the data ingestion mechanism is streaming. We will learn how to channel streamed data from Kinesis Data Streams to AWS Data Storage & Analytics Repositories like S3, Redshift, ElasticSearch and more using this service.

   
   

4. AWS Kinesis Data Streams - Clients can have streaming infrastructure or even devices (IoT) which may stream data continuously. Using this service we will learn how to collect streaming data and store it on AWS.

   
   

5. AWS Managed Streaming for Kafka (MSK) (Nov 2018 Release) - AWS recently added Kafka to their technology stack, which has lot of similarities with Kinesis. Learn comparative features as well as the method of standing up Kafka cluster on AWS to accept streaming data in AWS.

   
   

6. AWS Schema Conversion Tool - Database migration is a complex process and can be homogeneous (for ex. SQL Server on-premise to SQL Server on AWS) or heterogeneous ( for ex. MySQL to PostgreSQL). We will use this offline tool to learn about assessing migration complexities, generate migration assessment reports, and even perform schema migration.

   
   

7. AWS Database Migration Service (DMS) - Database Migration / Replication is a very common need for any federated data solution. We will use this service to learn how to migrate and/or replicate on-premise data from databases to AWS hosted relational databases on AWS RDS.

   
   

8. AWS Data Sync (Nov 2018 Release) - Continuous synchronization of data from on-premise to cloud hosted data repositories becomes a key requirement in environments where data is generated or changes very fast. We will use to service to learn how it can solve this requirement.

   
   

9. AWS Storage Gateway - This service has striking resemblance with AWS Data Sync, and is one of the alternatives for standing cached volumes and stored volumes on AWS to build a bridge between on-premise data storage and AWS. We will briefly learn similarities between AWS Data Sync and AWS Storage Gateway.