The AWS Certified Data Analytics Speciality Exam is a certification that tests a person’s ability to use AWS tools that are meant for collecting, storing, processing, analyzing, visualizing, and securing data. It not only tests your ability in those tools individually but also using them together. It’s all about how efficiently you can design data architecture in the cloud according to the requirements.
This exam is helpful for data engineers and data analysts. It’s recommended to have a foundational or associate level certification and two years of hands-on experience to pass the exam, which makes me an exception in this case. I learned everything from scratch and was able to take the exam in two months. So, I can conclude that it’s not mandatory to have certain experience and any other certifications to pass the exam.
My journey toward getting certified for the AWS Data Analytics Speciality Exam
As I’ve mentioned, I didn’t have much experience with AWS aside from working on a few college assignments. So, I started from scratch and decided to learn everything covered in the AWS Certified Solutions Architect Associate Certification Exam even though it wasn’t the exam I was going to write. I recommend the Simplilearn YouTube tutorial for this course. It features detailed explanations on all the topics covered in AWS Certified Solutions Architect Associate Certification Exam like:
- Storage services
- Data encryption
- Cross-region replication
… and provides insights into how everything works.
After gaining confidence about how AWS works, I started preparing for the AWS Certified Data Analytics Speciality Exam. Initially, I enrolled in a course on Udemy. This course by Stephane and Frank was useful for me to gain core knowledge on all the tools involved in AWS data analytics. I was able to complete hands-on practice through this course. This course also mentions the budget for AWS, allowing you to practice hands-on. Most of the practice is free through the free tier when a new account is created. It even comes with a free practice exam. Do try to take it even if you know everything about all the tools.
Areas to focus on
The figure below shows the areas that are included as part of data analytics and how important they are:
It’s important to plan your learning before you take your exam. It might be overwhelming to understand all the tools in a short time. So, remember that if you’re a newbie like me, spend a dedicated amount of time every day and plan the contents of the exam according to when you would like to take the exam. Before starting to plan, read the AWS Certified Data Analytics – Specialty Exam Guide. It provides updated information if there are any changes to the exam or if any new tools are added. With this guide, you’ll have a clear understanding of what to expect from the exam.
Tools you need to understand for the exam:
- Kinesis Data Streams, Kinesis Firehose, and Kinesis Analytics for streaming data
- Amazon Managed Streaming for Kafka (MSK)
- Simple Queue Service (SQS) for queuing messages
- Internet of Things (IoT)
- AWS Database Migration Service (DMS)
- Simple Storage Service (S3)
- DynamoDB for optimizing transactional queries
- AWS Lambda
- AWS Glue
- Processing data at unlimited scale with Elastic MapReduce, including Apache Spark, Hive, HBase, Presto, Zeppelin, Splunk, and Flume
- Amazon Elasticsearch Service, for searching and analyzing petabyte-scale data and Kibana
- Athena for querying S3 data lakes
- Redshift and Redshift Spectrum
- Relational Database Service (RDS) and Aurora
- QuickSight for visualizing your data interactively
- Keeping your data secure with encryption, KMS, HSM, IAM, Cognito, STS, and more
It’s important to understand how each and every tool works individually, and what tools it is restricted to interact with. After understanding how each tool works try to build real-time data architecture with limited tools.
- Always avoid using EMR solutions when the question asks for “cost-effective” and “easy to manage” solutions
- QuickSight can’t visualize data in real-time or near real-time; use OpenSearch and Kibana to achieve this
- Kinesis data streams can’t write to S3 or Redshift directly; use Kinesis Firehose instead
- The copy command is used to copy data to Redshift; the Unload command is used to copy data from Redshift
- Athena can’t query S3 Glacier you need to use Glacier select
- The recommended file format is always ORC or parquet
I would recommend assigning some time only for taking sample exams. It’s important to take as many exams as possible and try to understand the reason behind all the false options it helps eliminate options in different architectural questions. I enrolled in a course on the website which included around 10 exams, each of them with detailed explanations for every option of every question.
Attempting these exams also helps in real- data solutions to decide on what kind of tools one would like to choose. Apart from taking exams, take advantage of all the resources provided by the AWS website. The AWS data analytics whitepaper provides a high-level review of all the tools covered in the exam. AWS also provides sample and practice questions.
Finally, it’s not as hard as you think. Keep practicing and you will definitely pass the exam.
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