The AWS data analytics certification program validates a deep understanding of AWS data analytics services and their integration with each other to derive insights from data, making it suitable for individuals pursuing a role focused on data analytics.
The global big data analytics market is expected to reach $346.24 billion by 2030, with a growth rate of 30.7 percent.¹ With the rise of Generative AI, this growth is creating more opportunities for professionals in the field, such as data engineers, business analysts, and data analysts. According to Foote Partners, 141 non-certified big data skills have seen an average increase of 2 percent in market value, with skills like big data analytics, bioinformatics, and data engineering commanding pay premiums of 18 to 20 percent. The analysis report also identifies top certifications in the field, such as SAS Certified Professional: AI and Machine Learning and AWS Certified Data Analytics—Specialty, which are currently earning pay premiums of 10% above base pay.²
The business world is currently inundated with data, and companies require practitioners who possess specialized skills to work with advanced data analytics technologies to extract insights. With AWS currently being the largest cloud service provider in the world with a 33% share of the market³, the AWS Certified Data Analytics specialist is an ideal candidate to meet the growing demand.
It helps if you have an analytical frame of mind and professional experience. You should posses the ability to evaluate business requirements for big data applications and have functional knowledge to build, secure, maintain, and design data analytics solutions on AWS.
This exam readiness guide aims to provide valuable insights into the exam domains with quality resources to help you prepare effectively. For information on prerequisites, we recommend that you read our pillar article on the AWS Certification path.
AWS Data Analytics Certification
The AWS data analytics certification exam validates your expertise in building data analytics solutions using AWS data lakes and managing the entire data lifecycle. It is designed to test your knowledge of data security, compliance, and ability to design scalable and cost-effective solutions.
The exam is a comprehensive assessment of your knowledge and skills in data analytics on AWS. You need proficiency in Python and SQL, as well as familiarity with the common machine learning concepts.
Exam Resources
The most practical way to build technical expertise is through exam readiness resources offered by AWS partners, training courses, whitepapers, blogs, and updated practice tests. These resources will equip you and teach you about the new big data services and analytics tools available on the AWS platform.
Courses
- AWS Certified Data Analytics Specialty — Whizlabs
- AWS Exam Readiness – Data Analytics Specialty — AWS Skill Builder
- AWS Certified Data Analytics Specialty (DAS-C01) — Pluralsight
- AWS Certified Data Analytics Specialty — Udemy
- Practice Exams | AWS Certified Data Analytics Specialty — Udemy
White-papers
- Architectural Patterns to Build End-to-End Data Driven Applications on AWS
- Storage Best Practices for Data and Analytics Applications
- Derive Insights from AWS Modern Data Architecture
- Big Data Analytics Options on AWS
- Build Modern Data Streaming Architectures on AWS
- AWS Glue building a secure data pipeline
Workshops
- Amazon Quicksight
- Transform Data with AWS Glue DataBrew
- Modern Data Architecture Immersion Day Workshop
- Athena Workgroups
- Modern Data Architecture Immersion Day Workshop
Resources
- Service Guides: AWS Documentation
- Service FAQs: AWS FAQs
- Sample Questions: Data Analytics Specialty Sample Questions
- Exam Guide: AWS Certified Data Analytics – Specialty (DAS-C01)
Exam Domains (SAA-C02)
The ideal candidate should have sound knowledge of the five domains that are essential for clearing the AWS Certified Data Analytics - Specialty exam.
Domain 1: Data Collection
This domain assesses your data collection skills and includes three task statements, which account for 18% of the total exam score.
1.1 Determine the operational characteristics of the collection system
The first task in this domain assesses skills related to evaluating data loss, assessing costs, identifying failure scenarios, determining data persistence, and identifying latency characteristics of a collection system.
You should have a strong understanding of data management and technical knowledge of networking, ETL, and data migration.
1.2 Select a collection system that handles the frequency, volume, and the source of data
This task validates skills in data collection system selection, including the ability to describe and characterize incoming data, match data flow characteristics to potential solutions, assess tradeoffs between ingestion services, explain throughput capability, and choose a solution that satisfies connectivity constraints.
These scenario require a strong understanding of data management and technical knowledge of various collection systems.
1.3 Select a collection system that addresses the key properties of data, such as order, format, and compression
The last task in this domain examines your knowledge in data collection and management, including the ability to capture data changes at the source, understand data structure and format, apply compression and encryption, manage out-of-order and duplicate data delivery, and transform and filter data during the collection process.
Understand the tradeoffs between different processing approaches, such as at-most-once, exactly-once, and at-least-once.
Reading whitepapers and case studies can help you develop a strong understanding of data management principles and the ability to apply them in a practical setting.
Domain 2: Storage and Data Management
In the storage and data management domain, there are three distinct task statements that make up a significant 22% of the overall exam score.
2.1 Determine the operational characteristics of the storage solution for analytics
This task validates skills in selecting and evaluating storage solutions based on cost, performance, durability, reliability, and latency. It is designed to examine your ability to understand consistency requirements and data freshness needs to determine the most appropriate storage solution for analytics.
2.2 Determine data access and retrieval patterns
This task requires a strong understanding of data storage solutions and their capabilities. It validates your skills in determining appropriate storage solutions based on various factors such as update patterns, access patterns, change characteristics of data, long-term vs. transient storage needs, structured vs. semi-structured data, and query latency requirements.
Your ability to analyze and evaluate these factors is crucial in determining the most effective data access and retrieval patterns.
2.3 Select appropriate data layout, schema, structure, and format
This task requires skills in selecting appropriate data layout, schema, structure, and format. You should be able to determine mechanisms to address schema evolution requirements, select the storage format, compression/encoding strategies, data sorting and distribution strategies, and storage layout for efficient data access.
The scenario also examines your ability to explain the cost and performance implications of different data distributions, layouts, and formats and implement data formatting and partitioning schemes for data-optimized analysis.
2.4 Define data lifecycle based on usage patterns and business requirements
This problem statement examines your strategic thinking and policy application in the context of data management. It validates your functional knowledge of analyzing usage patterns and business requirements to determine the appropriate data lifecycle strategy and applying that strategy to different storage solutions through the implementation of data retention policies.
2.5 Determine the appropriate system for cataloging data and managing metadata
The final task in this domain validates your skills in data management and organization, specifically in evaluating and selecting appropriate systems for cataloguing data and managing metadata.
You will also need to demonstrate your expertise in evaluating and explaining mechanisms for discovering, creating, updating, searching, retrieving, tagging, and classifying data.
Domain 3: Processing
The processing domain examines your knowledge and skills in three task statements, which account for a significant 24% of the overall exam.
3.1 Determine appropriate data processing solution requirements
The first task examines your knowledge of data processing solution requirements, including understanding data preparation and usage requirements, different types of data sources and targets, evaluating performance and orchestration needs, and evaluating appropriate services for cost, scalability, and availability.
3.2 Design a solution for transforming and preparing data for analysis
This task statement is designed to assess your data engineering skills, specifically in designing and implementing ETL/ELT processes for both batch and real-time workloads, implementing failover, scaling, and replication mechanisms, addressing concurrency needs, optimizing costs, applying orchestration workflows, and aggregating and enriching data for downstream consumption.
3.3 Automate and operationalize data processing solutions
The final task in this domain validates your technical skills in implementing automated techniques for repeatable workflows, applying methods to identify and recover from processing failures, and deploying logging and monitoring solutions to enable auditing and traceability.
Domain 4: Analysis and Visualization
The Analysis and Visualization domain examines your skills in three scenarios, which comprise 18% of the total exam.
4.1 Determine the operational characteristics of the analysis and visualization solution
The first task statement is designed to confirm your skills in analyzing and evaluating the operational characteristics of an analysis and visualization solution. This includes determining costs, scalability, failover recovery, fault tolerance, availability, and performance requirements.
You should be able to evaluate different presentation approaches for data, including dynamic, interactive, and static options, and to translate performance requirements into an appropriate visualization approach.
Learn to translate performance requirements into an appropriate visualization approach.
4.2 Select the appropriate data analysis solution for a given scenario
This scenario is designed to evaluate your skills in analyzing and comparing solutions and selecting the appropriate type of analysis based on the customer use case. This includes streaming, interactive, collaborative, and operational analysis.
4.3 Select the appropriate data visualization solution for a given scenario
This final scenario validates your data visualization skills in evaluating output capabilities, choosing appropriate data delivery methods, defining data refresh schedules, selecting appropriate tools for different data freshness requirements, understanding visualization tool capabilities, implementing data access mechanisms, and integrating solutions from multiple heterogeneous data sources.
Domain 5: Security
The security domain assesses your skills in three task statements, which account for 18% of the total exam.
5.1 Select appropriate authentication and authorization mechanisms
This task validates the skills of an individual in selecting and implementing appropriate authentication and authorization mechanisms, including federated access, SSO, IAM, policies, ACL, table/column level permissions, security groups, and role-based control.
You need a deep understanding of access control and security principles to ensure that the right users have access to the right resources at the right time.
5.2 Apply data protection and encryption techniques
This task validates your knowledge of data protection and encryption. It examines your ability to determine encryption and masking needs, apply different encryption approaches, such as server-side and client-side encryption, AWS KMS, and AWS CloudHSM.
You should have a sound knowledge of implementing at-rest and in-transit encryption mechanisms, implementing data obfuscation and masking techniques, and applying basic principles of key rotation and secrets management.
5.3 Apply data governance and compliance controls
This final task examines your knowledge and skills in data governance and compliance, access and audit logging configuration, and the implementation of compliance controls.
Plotting Your Path to AWS Data Analytics Certification
The AWS Data Analytics Certification exam is exceedingly helpful for reinforcing your understanding of any technology.
If you have not yet started, sign up for an AWS training account and thoroughly acquaint yourself with the practice resources, including third-party courses, practice exams, whitepapers, blogs, virtual/in-person instructor-led classroom training, virtual webinars, and an exam-readiness course. The official AWS Certified Data Analytics – Specialty exam page can also help you build a plan to prepare.
Here, we answer a few of the most frequently asked questions according to the search data.
Is AWS Certified data analytics specialty valuable?
Organizations across all industries are seeking to expedite decision-making in today's complex and disrupted business landscape. This has created a demand for technology professionals who possess the expertise to leverage AWS' elastic data processing services to support these business outcomes. The AWS Data Analytics Specialty certification is specifically designed to validate an individual's proficiency in designing and implementing AWS services to extract value from data. This program holds significant value and is well worth the investment.
What is the salary for an AWS Certified data analytics professional?
The AWS Certified Data Analytics Specialty certification is among the top certifications that earn pay premiums of 10% above base pay, according to Foote Partners. Furthermore, non-certified big data skills have seen an average increase of 2% in market value, with skills such as big data analytics, bioinformatics, and data engineering commanding pay premiums of 18 to 20%. Earning this specialty credential can, therefore, prove to be financially rewarding for individuals who wish to pursue a career in data analytics.
Can I get a job with AWS Data Analytics Certification?
This specialty certification can demonstrate to potential employers or clients that the certified individual possesses the necessary skills and knowledge to work with data analytics on AWS. The AWS data analytics certification program is considered the most valuable in the industry for those who wish to pursue a data-focused job role, along with the SAS Certified Professional: AI and Machine Learning certification.
Footnotes
- Data Analytics Market Worth To Be USD 346.24 Billion at a CAGR of 30.7% by 2030 - Report by Market Research Future (MRFR)
- Tech’s Winning (and Losing) Jobs in 2023 - Dice Media
- AWS still lead dog in cloud market share and for Amazon