Data Impostor

·11 min read

Study notes: AWS Data Engineering

11 min read

So, I’ve done my AWS Data Engineering certification on the 25th October. To help me prepare for the exam I’ve written down some notes. Hopefully they’ll be useful to someone else too.

Note: This is planned to be a long post and not something to be read like a normal post. I’m writing this as a way to better organize my thoughts.

Courses done

Going through both the official preparation and the Udemy courses, I’d say to go for the official course if you want to get the best exam test experience. However Udemy is cheaper, provides way more courses and has a lot of exams to test out (like this) although the questions quality may vary. You can check the recommendations on this PDF .****

My timeline

I’ve set out to do this exam initially at the end of July but sadly I had an accident and had vacations after recovery. I did one Udemy course in July but to prepare for the exam I took to the AWS course during October. If you are confident on your skills one month to study is more than enough with a bit of focus. If doing this less intensively I’d say 2/3 months is a good timeline.

Notes by services

With my previous experience there’s a base knowledge that I think I’ve gathered. In this section I’ll be focusing on writing about the services and/or features I don’t know about.

S3

What is S3 object lock?

Source

S3 tiers:

S3 tiers

How can I replicate objects within and across regions?

Source

S3 batch replication allows to:

Redshift

Amazon Comprehend

Amazon Macie

DMS

Kinesis

Is divided in these services:

Furthermore we have Amazon Managed Service for Apache Flink

Kinesis Agent is a pre-built java library to processs files into Kinesis Data Streams.

MSK is a fully managed Kafka service.

AppFlow

AWS Transfer Family

DataSync

Aws Snow

API Gateway

We can use it’s cache features to cache responses from our API. This can be useful to reduce the number of requests to our backend.

Elasticache

Step Functions

OpenSearch

How do make sure the Kinesis data stream is scalable writing to opensearch?

Cloudtrail

AWS Resource Access Manager (RAM)

Glue

Glue databrew

Kinesis

Kinda basic but I keep forgetting as I don’t use it that much, the possible shardIteratorTypes are:

AWS Serverless Application Model (SAM)

Networking

This is one of the my weakest areas.

gateway endpoints

interface endpoints

IAM

EMR

Quicksight

QuickSight’s Generative Business Intelligence (BI) feature leverages machine learning to automate data analysis tasks, including identifying patterns, trends, and anomalies in the data, and generating relevant visualizations and insights.

Athena

Session Manager

AWS Application Discovery Service

Using Application Discovery Service APIs, you can export the system performance and utilization data for your discovered servers. Input this data into your cost model to compute the cost of running those servers in AWS. Additionally, you can export data about the network connections that exist between servers. This information helps you determine the network dependencies between servers and group them into applications for migration planning.

It has two components:

AWS Application Migration Service

AWS Storage Gateway

AWS Shield

AWS GuardDuty

MWAA

DynamoDB

Final thoughts

It has been interesting to get the AWS Certification. I’ve gotten to learn quite a bit on specifics of the aws services which I’ll surely be using them in the future. It provides a good baseline or even update on the available services (i.e. redshidt materialized views, datashares and even lake formation) but if you are already working with these services experiences beats by far this. Or just write an article on the services you don’t know about (which is what I have planned for the next couple of months).

Not listening