AWS Databases, EMR, SageMaker, IoT, Redshift, Glue, QuickSight, RDS, Aurora, DynamoDB, Kinesis, Rekognition & much more
Created by: Siddharth Mehta
Produced in 2022
What you will learn
- Confidently architect AWS solutions for Ingestion, Migration, Streaming, Storage, Big Data, Analytics, Machine Learning, Cognitive Solutions and more
- Learn the use-cases, integration and cost of 40+ AWS Services to design cost-economic and efficient solutions for a variety of requirements
- Answer detailed technical questions of your design and development teams regarding implementation and build
- Practice hands-on labs on complex AWS services like IoT, EMR, SageMaker, Redshift, Glue, Comprehend and many more
Overall Score : 78 / 100
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- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- AWS ElastiCache ( Memcached ) - After covering most of the mechanisms of data ingestion, we will shift focus on caching data before moving on the databases. We will start learning about caching with Memcached flavor of this service which offers powerful caching capabilities for simpler data types.
- AWS ElastiCache ( Redis ) - We will learn comparative difference between Memcached and Redis for caching, and learn how to use Redis flavor of caching which can build cache clusters and can host complex data types.
- AWS S3 (Advanced) - AWS S3 is the basis of data s
Udemy's Top 10% of most engaging instructors
My name is Siddharth Mehta. I have career experience of more than 15 years in the IT Industry and am presently working as Enterprise Cloud Architect. I am published author on many online and print-media publications. I have taught thousands of students on Udemy and have number of courses on Data and Analytics.
Would you consider learning from just any hobbyist who knows programming or someone who just teaches programming without practically using it in the real world, or someone who has experience of using the technology in real world on multi-million dollar large-scale projects globally ? I will teach you everything I know about the subject, from my years of practical experience in the field of BI, Data, Analytics, Cloud and Data Science.
If you are interested in learning more about me, below are some of my career highlights:
I have career experience of more than 15+ years and am presently working in New York Metro region as Enterprise Architect for a life-sciences proprietary multi-tenant product technology portfolio, managing an ecosystem of ISVs and tenants. Below are some of my career highlights:
-|- International experience of working across geographies (US, UK, Singapore) for multi-national clients in Banking, Logistics, Government, Media Entertainment, Products, Life Sciences and other domains
-|- Lead architecture of multi-million dollar portfolios containing apps in Cloud, web, mobi