Detailed Review Of Preparation

Quite recently I took both the AWS Big Data exam (now retired) and its new version the Data Analytics. I passed both. I would rate the difficulty level for both exam as moderate.

Exam guide for the certification can be found here and Sample Questions.

Differences between Big Data & Data Analytics Exam

1. Amazon MSK is featured on the new exam

2. More storage questions on the Data Analytics exam

3. A lot more questions on processing

4. The Big Data exam used to have a number of Machine Learning questions. You may still see few Machine Leaning questions on the exam

5. Relatively fewer questions on Analysis and Visualization

Preparation Tips:

1. If you have passed at least an associate exam, your focus should be on Collection, Processing and Analytics and Visualization domains

2. Know the use cases for each big data analytics service(listed in the next section)

3. Know the anti-patterns for each service

4. Data Analytics certification focuses on OLAP while the Database is OLTP biased. NB: There are OLTP questions on the Data Analytics exam

5. You should understand how to secure the services. Know which ones use fine grained IAM permissions and which ones do not

6. Understand how encryption at rest and in-transit works and which types are supported for each service

7. You should have experience with Redshift and know the best practices in designing tables and loading data for optimum performance

8. Services used for Batch Processing and Streaming

9. Know the license models for QuickSight and security features supported per model  

10. How to load data into QuickSight and how to refresh data

11. When to use Glue and where to use EMR. Know the limitations of Glue

12. S3 store tiers and lifecycle policies

13. Data transformations/conversions – Kinesis Firehose, Lambda, Kinesis Data Analytics, Glue

14. Data formats that improve data processing e.g. Avro, ORC, Parquet

15. Take the exam readiness course from AWS to assess your readiness

Services to know for the exam

1. Glue & Glue Data Catalog

2. S3 

3. DynamoDB

4. Kinesis Data Streams

5. KInesis Firehose

6. Redshift and Spectrum

7. EMR and Hadoop Ecosystem

8. Lambda

9. Athena

10. Quicksight

11. Kinesis Data Analytics

12. ElasticSearch and Kibana

13. RDS & Aurora

14. Amazon MSK

15. IOT Core

16. Data Pipeline

17. DMS

18. Snowball and Direct Connect

19. SageMaker

20. Security considerations for all above services

 Resources Used

Certification Preparation course

Udemy

Linux Academy

AWS Free Digital Courses

Exam Readiness: AWS Certified Data Analytics – Specialty

Best Practices for Data Warehousing with Amazon Redshift

Practice Test

 Whizlabs

White Papers

Big Data Analytics Options on AWS

Lambda Architecture for Batch and Stream Processing

Use Amazon ES to Log and Monitor Almost Everything

Streaming Data Solutions on AWS with Amazon Kinesis