Welcome to the AWS Data Engineering Associate Certification Study Guide! If you’re new to data engineering and want to become certified in AWS, you’re in the right place. This guide is designed for beginners with no prior knowledge and should get you started and ready to get certified with AWS!
What is Data Engineering?
Data engineering is a field within data management that focuses on the practical application of data collection and data processing. Data engineers are responsible for designing, building, and maintaining the architecture (often referred to as the data pipeline) that enables an organization to collect, store, and analyze data efficiently and effectively. In other words, Data engineering is the powerhouse behind your favorite apps, services, and online experiences. It’s what ensures data flows seamlessly, enabling companies to make smart decisions, create personalized user experiences, and even predict your next online purchase. Let’s take a closer look at how data engineering works and why it’s essential, with real-world examples:
Data Collection - The Netflix Example
Imagine you’re watching Netflix. While you’re binging on your favorite show, data engineers at Netflix are collecting valuable information about your viewing habits. They use tools and processes to capture what you watch, how long you watch it, and whether you binge-watch on weekends or savor an episode each day.
Data Transformation - Amazon's Product Recommendations
Ever noticed how Amazon seems to know exactly what you want to buy next? Data engineers at Amazon work behind the scenes to transform and analyze massive amounts of data. They take your past purchases, your browsing history, and even reviews from other customers to create those uncannily accurate product recommendations.
Data Storage - Airbnb's User Profiles
Airbnb, the global home-sharing platform, relies on data engineering to store and manage user profiles, property details, and booking information. Data engineers ensure this information is securely stored and can be quickly retrieved when you’re looking for that perfect getaway.
Data Processing - Uber's Real-Time Rides
When you book an Uber ride, data engineering springs into action. It processes real-time data from both drivers and riders, calculating routes and pricing on the fly. This dynamic data processing ensures you get a ride quickly and at a fair price.
Data Integration - Spotify's Playlists
Spotify is all about personalized music experiences. Data engineers at Spotify integrate data from various sources, like your listening history, your friends’ playlists, and music charts, to create customized playlists and music recommendations.
Data Quality - Banking and Fraud Detection
In the world of finance, data engineers play a crucial role in maintaining data quality. They implement rigorous data validation and monitoring processes to detect unusual transactions that could signal fraud. Banks use these data engineering techniques to protect your hard-earned money.
Scalability and Performance - Twitter's Real-Time Feeds
Twitter’s data engineering team ensures that tweets flow in real-time to millions of users. They’ve built a scalable infrastructure that handles the enormous volume of data generated by users worldwide, all while maintaining performance and reliability.
Data Governance and Compliance - Healthcare and HIPAA
In the healthcare industry, data engineers adhere to strict data governance rules, such as HIPAA. They implement data security measures to protect sensitive patient information, ensuring that only authorized personnel can access it.
Why become a Data Engineer?
Data Engineering forms the bedrock upon which Data Science is constructed. Think of it as the solid foundation that supports the entire data-driven ecosystem. Just as quality ingredients are vital to a great meal, reliable data is essential for meaningful insights. Without Data Engineering, there can be no Data Science. It’s the starting point for logging, storing, and analyzing data, paving the way for the exciting world of machine learning, AI, and deep learning. Furthermore, Data Engineering provides solid job security, boasting an expected growth rate of 17.6%1 and an average salary of $103,000 in the U.S.2, a staggering 42% higher than the U.S. average salary. So, why not start your journey with Data Engineering by taking up the AWS Certification and build a secure and promising future in the data domain?
Pre-requisites for an AWS Data Engineer Associate Certification
Before you dive into your journey to become an AWS Certified Data Engineer – Associate, there are a few things to consider. While there are no strict pre-requisites for this certification, it’s recommended that candidates have the equivalent of 2-3 years of experience in data engineering or data architecture, coupled with at least 1-2 years of hands-on experience with AWS services. This combination of knowledge and practical experience will help you navigate the certification process more effectively.
Exam Details
Exam Format
Understanding the exam format is crucial for effective preparation. The AWS Data Engineer Associate Certification exam consists of 85 questions. These questions come in two primary formats: multiple choice and multiple response. It’s important to note that unanswered questions are scored as incorrect, but there’s no penalty for guessing. The exam duration is 170 minutes, with 50 questions contributing to your final score. Additionally, there are 15 unscored questions, used to gather performance data for future exams.
Exam Content and Essential AWS Services
To excel in the AWS Data Engineer Associate Certification, a solid foundation in data engineering principles and a deep knowledge of AWS services are indispensable. The exam content spans four crucial domains, each requiring proficiency in various aspects of data engineering. Let’s delve into these domains:
Data Ingestion and Transformation (34%):
This domain underlines the candidate’s capacity to proficiently ingest and transform data, orchestrate data pipelines, and apply programming concepts effectively. It encompasses a wide spectrum of data transformation techniques, making it a critical skill for a data engineer.
Data Store Management (26%):
Choosing optimal data stores, designing efficient data models, cataloging data schemas, and managing data lifecycles are the core elements of this domain. It demands a comprehensive understanding of how data should be structured and managed, aligning it with your organization’s requirements.
Data Operations and Support (22%):
Operationalizing, maintaining, and monitoring data pipelines, along with ensuring data quality and consistency, fall within this domain. An AWS Data Engineer must possess the skills to troubleshoot issues, optimize performance, and facilitate data operations seamlessly.
Data Security and Governance (18%):
The final domain addresses crucial aspects like authentication, authorization, data encryption, privacy, and governance. It also focuses on enabling effective logging, ensuring data is handled securely and according to compliance standards.
While mastering these domains is essential, practical experience in previous data engineering frameworks is just the beginning. You must also harness the power of AWS services to apply these concepts in the AWS ecosystem effectively. Here are some of the key AWS services to get you started:
1. Amazon S3: (Simple Storage Service): At the core of AWS data storage, Amazon S3 plays a pivotal role in data engineering. Candidates must understand how to use S3 for data storage, ingestion, and distribution. Proficiency in setting up S3 buckets, managing permissions, and configuring data lifecycle policies is vital.
2. AWS Glue: AWS Glue is a fully managed ETL service, simplifying the preparation and loading of data. Candidates should be skilled in using AWS Glue to create ETL jobs, data catalogs, and data transformation scripts. Automating data pipelines and orchestrating ETL processes is a must.
3. Amazon Redshift: A robust data warehousing service for analytics, Amazon Redshift demands knowledge in data modeling, query optimization, and working with large datasets. Understanding schema design, query optimization, and data loading best practices is essential.
4. AWS EMR (Elastic MapReduce): As a cloud-native big data platform, AWS EMR is designed for processing vast data volumes. Candidates should be familiar with EMR clusters, Hadoop, Spark, and other big data technologies. The ability to create and manage EMR clusters, work with S3-stored data, and optimize EMR job performance is vital.
5. Amazon Kinesis: Amazon Kinesis offers real-time data streaming and analytics services. A strong grasp of Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics is necessary. The knowledge to ingest, process, and analyze streaming data is crucial for data engineering.
6. AWS Lambda: AWS Lambda, a serverless compute service, is ideal for event-driven processing. Proficiency in using Lambda functions to automate data processing, trigger events, and perform serverless data transformations is vital. This includes configuring Lambda functions, working with event sources, and handling errors.
7. Amazon Athena: Amazon Athena is an interactive query service for analyzing data in Amazon S3 using standard SQL. Candidates should be proficient in writing SQL queries for data analysis, creating views, and data catalogs within Athena. Understanding how to use Athena for ad-hoc queries and reporting is important.
8. AWS Step Functions: AWS Step Functions is a serverless orchestration service for building workflows. Candidates should understand how to create and manage serverless workflows to orchestrate data pipelines, manage dependencies, and ensure reliable data processing.
These services form the core toolkit for AWS Data Engineers. Mastering them is essential to excel in the AWS Data Engineer Associate Certification exam and to thrive in data engineering roles
Exam Resources
To embark on a successful journey towards AWS Data Engineer Associate certification, it’s crucial to have the right resources at your disposal. AWS offers a comprehensive selection of recommended materials to aid in your preparation, ensuring that you’re well-equipped to excel in the certification exam. Among the most important resources are:
1. AWS Certified Data Engineer Associate Exam Page: This official AWS certification exam page serves as your gateway to a wealth of information and relevant links. Here, you’ll find essential details about the exam, including registration, pricing, and any updates or announcements. It’s your starting point for accessing all other pertinent resources.
2. AWS Certified Data Engineer – Associate (DEA-C01) Exam Guide by AWS: The official AWS exam guide is an invaluable resource. It outlines the service requirements, typical knowledge areas that need to be demonstrated, and the skills you must master to succeed in the exam. It provides a structured overview of the topics you’ll encounter, making it an essential reference during your preparation.
3. Official AWS Practice Question Set for DEA-C01: This official practice question set is a valuable resource for self-assessment. It includes sample questions designed to test your knowledge and readiness for the certification exam. Practicing with these questions will help you gauge your understanding of the exam topics and identify areas that may need further study.
4. Udemy AWS Data Engineer Course: If you’re looking for a comprehensive and hands-on preparation course, consider enrolling in the Udemy AWS Data Engineer course. Led by best-selling Udemy instructors Frank Kane and Stéphane Maarek, this course is a collaboration between two experts who have collectively taught over 2 million people worldwide. Frank Kane, with his extensive experience in wrangling massive data sets during his nine-year tenure at Amazon, brings a unique perspective to the course. It already boasts a remarkable 4.8-star review rating at the time of writing. This course combines Stéphane’s depth on AWS with Frank’s expertise, making it an excellent choice for in-depth certification preparation, and for me, Stéphane has helped me tremendously in passing all my AWS Certification Exams!
These resources collectively provide a well-rounded preparation package, ensuring you’re equipped with the knowledge and skills needed to excel in the AWS Data Engineer Associate certification exam. Utilize these materials to enhance your understanding of the exam topics and boost your confidence for the actual test.
Registration
Registering for the AWS Data Engineering Associate Certification exam is a straightforward process. By following AWS’s guidelines, which include scheduling and fee information, you can take this significant step towards enhancing your career. To ensure a smooth and hassle-free registration, it’s advisable to plan your exam date and time well in advance.
Here’s what you need to know when registering:
Exam Availability: The registration period for the AWS Data Engineering Associate Certification exam commences on October 31, 2023. During this time frame, candidates can take the exam between November 27, 2023, and January 12, 2024. This flexibility in scheduling allows you to choose a convenient time to showcase your skills and knowledge. It’s important to note that during this period, the exam is in its Beta stage. AWS Certification employs beta exams to evaluate the performance of exam items before integrating them into live exams. If the beta proves successful, candidates who pass will be among the first to earn the new certification. Beta exam results will be available 90 days from the close of the beta exam. Afterward, the official exam will be administered starting from March 2024.
Exam Duration and Format: The AWS Data Engineering Associate Certification exam is a comprehensive test, with a total duration of 170 minutes. It features 85 questions, which can be either multiple-choice or multiple-response. This format ensures a thorough assessment of your understanding of the material.
Cost: Registering for the exam requires a fee of 75 USD*. For any additional cost-related information, it’s recommended to visit the “Exam pricing” section.
Testing Options: As an aspiring candidate, you have the flexibility to select your preferred testing mode. You can either opt for an in-person exam conducted at a nearby Pearson VUE testing center or choose the convenience of an online proctored exam. This choice allows you to align your examination experience with your preferences.
Languages Offered: The exam is available exclusively in English.
Conclusion
Your journey to becoming an AWS Data Engineer Associate is an exciting adventure into the world of data engineering. This certification not only validates your knowledge and skills but also opens doors to a realm of opportunities in the ever-expanding field of data management. Whether you’re new to data engineering or aiming to take your existing expertise to new heights, this certification sets you on a path to success.
Best of luck in your studies and on your journey to become AWS certified!
