In the ever-evolving world of AWS certifications, the AWS Certified Big Data Specialty certification will be retired on July 1, 2020, while the AWS Certified Data Analytics Specialty exam, launched on March 17, 2020, is set to replace it. This article aims to outline the key differences between the two exams, helping you decide which certification best aligns with your career goals and expertise.

Understanding the distinctions between AWS certifications is crucial for making an informed decision about which path aligns with your career goals. Both the AWS Certified Big Data Specialty and the AWS Certified Data Analytics Specialty exams are designed to validate your expertise in data management and analytics within the AWS ecosystem, but they target slightly different audiences and focus on different aspects of big data solutions.

Exam Availability and Retirement: Key Timeline Insights

One of the most significant aspects of these two certifications is their availability timeline. The AWS Certified Big Data Specialty exam officially retired on July 1, 2020. If you were planning to take this exam, you had to complete your preparations and take the test before this cutoff date. On the other hand, the AWS Certified Data Analytics Specialty exam was introduced as a replacement, accepting registrations beginning on March 17, 2020. This new exam, which replaced the Big Data Specialty certification, officially took place for the first time on April 13, 2020.

For those who have not yet had the opportunity to take the AWS Certified Big Data Specialty exam, it’s essential to understand that this certification is no longer available. However, the AWS Certified Data Analytics Specialty exam continues to provide a fresh and modern approach to testing proficiency in AWS analytics services, with a clear focus on data lakes and analytics solutions that meet contemporary industry needs.

Who Should Take These Exams? Tailoring Your Certification Path

The next logical step in deciding which certification is right for you is understanding your career goals and professional experience. Both exams cater to professionals working in data analytics and big data environments, but each certification focuses on slightly different areas of expertise.

AWS Certified Big Data Specialty: A Path for Advanced Big Data Professionals

The AWS Certified Big Data Specialty exam was created for professionals who are already involved in large-scale data processing, analysis, and complex analytics within the AWS ecosystem. If you work with massive data sets, big data tools, and advanced analytics pipelines, this certification was tailored to help you validate your expertise in managing, analyzing, and extracting insights from vast data sets using AWS services.

Key services you would have worked with in this certification include Amazon Kinesis, AWS Lambda, DynamoDB, Redshift, S3, and EMR. You would have been expected to have a deep understanding of how to architect and implement scalable data processing pipelines, and apply data modeling techniques that meet the needs of complex data systems. Additionally, this certification required proficiency in managing real-time data streams, utilizing services like Kinesis Firehose and Kinesis Streams, and designing security measures to protect sensitive data across AWS environments.

As the AWS Certified Big Data Specialty exam is no longer available, its role has now shifted to the Data Analytics Specialty exam, which covers a more contemporary approach to big data services in AWS. If you previously worked with AWS big data tools and are looking to upgrade your skills to meet newer industry trends, this certification was your perfect fit.

AWS Certified Data Analytics Specialty: Targeting Modern Data Solutions and Insights

In contrast to the AWS Certified Big Data Specialty exam, the AWS Certified Data Analytics Specialty exam is tailored for professionals looking to deepen their expertise in modern data analytics solutions on AWS. This certification focuses heavily on the tools and services needed to design, deploy, and manage end-to-end data analytics solutions, with a significant emphasis on data lakes, data pipelines, and visualization techniques.

If your goal is to demonstrate your proficiency in the analysis of data using AWS analytics services like Amazon Redshift, AWS Glue, Amazon Athena, and Amazon Quicksight, the AWS Certified Data Analytics Specialty exam is the right choice for you. This certification targets professionals who are engaged in extracting insights from data and delivering business intelligence by leveraging AWS tools.

With a deeper focus on data processing, data storage, visualization, and security, the Data Analytics Specialty exam provides a more holistic view of how organizations can manage large amounts of data in real-time and utilize data to derive meaningful insights that can drive decision-making processes. You will need to understand how to securely store data, analyze it effectively using AWS services, and ensure that all aspects of the data management lifecycle are handled correctly.

Unlike the Big Data Specialty exam, which was more focused on handling and processing vast datasets, the Data Analytics Specialty exam has shifted towards practical, insight-driven analytics that can be used to generate valuable business outcomes. This exam is ideal for professionals working in roles such as Data Analysts, Data Architects, and Data Engineers who are responsible for designing and implementing data solutions that support real-time analysis and decision-making.

Key Differences Between the Big Data and Data Analytics Specialties

Understanding the nuances between the two certifications can help you make a well-informed decision about which exam to pursue based on your skills, career aspirations, and current industry trends. Below are the primary distinctions:

1. Scope and Focus:
   • The AWS Certified Big Data Specialty exam was centered around large-scale data processing and complex analytics on AWS, whereas the AWS Certified Data Analytics Specialty exam focuses more on analytics services and data lakes.
2. Target Audience:
   • The Big Data Specialty was ideal for those dealing with large datasets and advanced analytics, while the Data Analytics Specialty is aimed at professionals who work with data storage, visualization, and generating insights from analytics data.
3. Key Services:
   • Big Data Specialty emphasized tools like Kinesis, EMR, DynamoDB, and Redshift, while the Data Analytics Specialty focuses on services like Athena, Quicksight, Glue, and Redshift Spectrum, with greater emphasis on building and managing data lakes.
4. Certifications Evolution:
   • The Big Data Specialty was officially retired in favor of the Data Analytics Specialty, making the newer certification more aligned with the current trends in data analytics and cloud computing.

Which Exam is Right for You?

Both the AWS Certified Big Data Specialty and the AWS Certified Data Analytics Specialty exams offer valuable insights into the world of big data and cloud analytics. However, with the retirement of the Big Data Specialty exam, the AWS Certified Data Analytics Specialty has emerged as the modern certification for professionals looking to prove their expertise in AWS’s analytics services.

If you are a professional aiming to demonstrate your ability to work with AWS analytics tools and design end-to-end data solutions, the Data Analytics Specialty is your best bet. For those who have already earned the Big Data Specialty certification or have experience in advanced big data analytics, the Data Analytics Specialty certification offers a natural progression that aligns with current industry demands.

Ultimately, both certifications provide immense value, and the choice between them depends on your specific career trajectory, the skills you wish to develop, and the services you want to specialize in. Regardless of which certification you pursue, AWS offers world-class resources and training programs to help you succeed in your exam preparation, and platforms like ExamLabs provide comprehensive practice exams to ensure you’re fully prepared for success.

Understanding Core Competencies and Knowledge Requirements for the AWS Certified Big Data and Data Analytics Specialty Exams

When preparing for any AWS certification exam, it is essential to understand the specific competencies and prerequisites that are assessed. Both the AWS Certified Big Data Specialty and the AWS Certified Data Analytics Specialty exams require candidates to demonstrate their expertise in handling and analyzing large data sets using AWS technologies. However, the two exams focus on different aspects of data management, processing, and analytics. Let’s dive deeper into the core competencies tested and the knowledge and experience prerequisites for each certification.

Core Competencies Assessed in the AWS Certified Big Data Specialty Exam

The AWS Certified Big Data Specialty exam primarily tests candidates’ ability to manage and implement Big Data solutions on AWS. It emphasizes technical expertise in utilizing AWS Big Data services, designing scalable systems, and automating data analysis processes. Below are the key competencies tested in the exam:

1. Designing and Maintaining Big Data Systems on AWS: One of the core components of the Big Data Specialty exam is the ability to design and implement scalable, efficient, and secure Big Data systems. Candidates are required to demonstrate their knowledge of how to architect systems that can process and analyze large volumes of data. This involves the ability to utilize AWS services like Amazon EMR, AWS Lambda, and Amazon Kinesis to build robust data architectures that can scale according to business needs.
2. Utilizing AWS Big Data Services Effectively: A significant part of the exam involves demonstrating proficiency in AWS’s Big Data services. Candidates are tested on their understanding of tools like Amazon Redshift, Amazon DynamoDB, and Amazon S3. Each of these services plays a vital role in Big Data analytics, from data storage to real-time data processing. Having a thorough understanding of how to integrate these services into a cohesive data management solution is critical for success in the exam.
3. Automating Data Analysis Processes: Another essential competency tested in the AWS Certified Big Data Specialty exam is the ability to automate data analysis tasks. This involves utilizing services like AWS Lambda for serverless data processing, Amazon Kinesis for real-time data streaming, and Amazon EMR for large-scale data analysis. Candidates need to demonstrate their ability to build data pipelines that can automate the collection, transformation, and analysis of data efficiently.

Core Competencies Assessed in the AWS Certified Data Analytics Specialty Exam

While the AWS Certified Big Data Specialty exam focuses on Big Data system design and management, the AWS Certified Data Analytics Specialty exam takes a more comprehensive approach, testing candidates’ ability to work with AWS services to derive actionable insights from data. Here are the key competencies tested in the Data Analytics Specialty exam:

1. Defining and Integrating AWS Data Analytics Services: The ability to define and integrate AWS data analytics services is a fundamental competency tested in this exam. Candidates are expected to have a strong understanding of AWS analytics services, including Amazon Athena, Amazon Redshift, AWS Glue, and Amazon Kinesis. They need to demonstrate their ability to design and implement data analytics solutions that integrate these services to meet business objectives. This involves not only knowledge of how each service works individually but also how to combine them into an effective analytics pipeline.
2. Aligning AWS Services with the Data Lifecycle Phases: One of the major competencies tested in the Data Analytics Specialty exam is the ability to align AWS services with different stages of the data lifecycle. Candidates must demonstrate expertise in each phase: data collection, processing, storage, and visualization. Understanding how to use tools like AWS Glue for data extraction and transformation, Amazon Redshift for data warehousing, and Amazon Quicksight for data visualization is crucial. Candidates will also need to show their ability to manage real-time and batch data processing efficiently.
3. Data Security and Access Control: Data security is another crucial competency tested in the Data Analytics Specialty exam. Since the exam focuses on building and maintaining data analytics solutions, candidates are expected to understand AWS’s security measures for protecting sensitive data across various stages of the data lifecycle. This includes managing data access with IAM (Identity and Access Management) roles, securing data storage with encryption methods in services like S3 and Redshift, and ensuring data privacy and compliance with regulatory standards.

Knowledge and Experience Prerequisites for the AWS Certified Big Data Specialty Exam

For the AWS Certified Big Data Specialty exam, candidates are expected to have substantial experience in both IT and Big Data technologies. The specific experience prerequisites for this exam include:

1. Two Years of Direct Experience with AWS: Candidates must have at least two years of hands-on experience working with AWS technologies. This experience should be focused on deploying and managing Big Data solutions on AWS, including proficiency in services like Amazon Kinesis, EMR, and Redshift. The exam assesses the ability to work with AWS infrastructure to process and analyze large data sets efficiently.
2. Experience with Big Data Technologies: In addition to AWS experience, candidates are also expected to have a deep understanding of Big Data technologies. This includes experience in working with Hadoop, Spark, and other distributed computing frameworks. The exam tests candidates’ ability to leverage these technologies to process large datasets and implement complex analytics solutions on AWS.
3. Strong Knowledge of Data Processing and Analysis: Another essential prerequisite is a strong understanding of data processing and analysis techniques. Candidates should be proficient in designing and building data pipelines that can handle batch and real-time processing, as well as performing large-scale data analysis.

Knowledge and Experience Prerequisites for the AWS Certified Data Analytics Specialty Exam

The prerequisites for the AWS Certified Data Analytics Specialty exam are slightly more lenient but still require candidates to have a strong foundation in data analytics technologies, particularly in the AWS ecosystem. Here’s what you need:

1. Five Years of Experience with Data Analytics Technologies: Candidates should have at least five years of experience working in data analytics roles. This includes experience in designing, developing, and implementing data analytics solutions using a variety of tools and techniques. While direct experience with AWS is not required, familiarity with AWS data services will be essential for passing the exam.
2. Two Years of Hands-On AWS Experience: While candidates don’t need as much experience with AWS specifically as for the Big Data Specialty exam, two years of hands-on experience with AWS services are recommended. This includes familiarity with services like AWS Lambda, Redshift, Amazon Athena, and AWS Glue for data transformation and integration.
3. Knowledge of Data Security: Given the exam’s emphasis on data security and privacy, candidates should have a solid understanding of AWS’s security best practices for managing data. This includes configuring access control mechanisms, encrypting data at rest and in transit, and ensuring compliance with regulatory standards in the data analytics domain.

Both the AWS Certified Big Data Specialty and the AWS Certified Data Analytics Specialty exams provide valuable opportunities to validate your expertise in managing and analyzing large-scale data sets on AWS. However, each exam focuses on different aspects of data management, with the Big Data Specialty exam being more centered around Big Data system design and management, and the Data Analytics Specialty exam emphasizing the ability to derive insights from data through analytics services. Understanding the core competencies and experience prerequisites for each exam is critical in making an informed decision on which certification to pursue.

With the right knowledge, hands-on experience, and a solid preparation strategy, you can successfully pass either of these exams and open the door to advanced career opportunities in the world of Big Data and analytics. Use resources such as AWS documentation, sample questions, and practice tests on platforms like ExamLabs to enhance your exam readiness and improve your chances of success.

Exam Structure, Duration, and Domain Comparison for the AWS Certified Big Data and Data Analytics Specialty Exams

When it comes to AWS certifications, the AWS Certified Big Data Specialty and the AWS Certified Data Analytics Specialty exams are both critical credentials for professionals working with large data sets. These two certifications share some common features but also exhibit distinct differences, particularly in exam structure, duration, and content domains. Understanding these differences is essential for tailoring your preparation strategy and ensuring that you are fully prepared to tackle either exam.

Exam Structure & Duration: Key Differences Between Big Data and Data Analytics

Both the AWS Certified Big Data Specialty and the AWS Certified Data Analytics Specialty exams share similar formats. They both consist of multiple-choice and multiple-response questions that assess your proficiency in handling AWS services for large-scale data management and analytics. However, there are important differences in the duration of the exams that may impact your preparation and approach on the day of the test.

• AWS Big Data Specialty Exam: This exam is designed to assess the in-depth knowledge and hands-on experience of candidates in implementing Big Data solutions on AWS. The duration for the AWS Big Data Specialty exam is 170 minutes, which allows you a significant amount of time to go through the multiple-choice and multiple-response questions. Given the complexity of Big Data systems and the need for detailed understanding, this time frame ensures that you have enough time to carefully analyze the questions and select the most accurate responses.
• AWS Data Analytics Specialty Exam: The Data Analytics Specialty exam, while similar in format, provides slightly more time for candidates to complete the exam. With a duration of 180 minutes, this exam gives you an additional 10 minutes compared to the Big Data Specialty exam. This extra time can be beneficial, especially considering the exam’s broader focus on data analytics services and workflows, which may require more detailed analysis and thoughtful responses.

Despite the differences in duration, both exams share a registration fee of $300, which is standard across AWS certification exams. The availability of the exams in four languages — English, Japanese, Simplified Chinese, and Korean — ensures that candidates from various linguistic backgrounds can also take the exam in their preferred language, making it more accessible to a global audience.

Domain Comparison: How the Content Varies Between the Big Data and Data Analytics Specialty Exams

While both exams cover similar core concepts in Big Data and data analytics, the domain focus and weightings differ significantly between the two certifications. Understanding the weight assigned to each domain in both exams will help you prioritize your study time and focus on the areas that are most likely to appear on the exam.

• Collection Domain:
  • Big Data Specialty: The Collection domain holds an 18% weight in the AWS Certified Big Data Specialty exam. This domain assesses your understanding of how to collect data using various AWS services, including Kinesis, DynamoDB, and others. It emphasizes the importance of handling large volumes of incoming data efficiently and ensuring that it is ready for further processing.
  • Data Analytics Specialty: In the Data Analytics Specialty exam, the Collection domain carries a slightly lower weight of 17%. This reflects the reduced emphasis on data collection when compared to other stages of the data analytics lifecycle. Notably, this domain in the Data Analytics Specialty exam does not include subtopics like durability and availability characteristics, which are featured in the Big Data Specialty exam.
• Storage Domain:
  • Big Data Specialty: The Storage domain in the AWS Certified Big Data Specialty exam carries a weight of 17%. This section evaluates your understanding of storage solutions in AWS, particularly for handling large-scale data sets. Topics include S3 storage classes, data lifecycle management, and ensuring the integrity and availability of data.
  • Data Analytics Specialty: In contrast, the Storage domain in the Data Analytics Specialty exam is given a more significant emphasis, accounting for 22% of the exam weight. This increase reflects the more prominent role that data storage plays in analytics workflows, particularly in terms of organizing data lakes, managing storage for real-time data processing, and ensuring efficient access for data analytics.
• Processing Domain:
  • Big Data Specialty: The Processing domain holds a weight of 17% in the Big Data Specialty exam. Candidates are assessed on their ability to implement data processing pipelines, work with tools like Amazon EMR, and process vast amounts of data at scale. The focus is on designing systems that can handle high throughput, batch processing, and real-time data processing.
  • Data Analytics Specialty: The Processing domain in the Data Analytics Specialty exam is more heavily weighted at 24%. This reflects the domain’s significance in the data analytics lifecycle. Data analysis workflows often require more intensive processing, which could include batch processing, stream processing, and advanced analytics techniques. The increased weight highlights the importance of AWS services such as Amazon Kinesis, AWS Glue, and Amazon Redshift in processing complex data sets.
• Analysis & Visualization Domain:
  • Big Data Specialty: In the AWS Certified Big Data Specialty exam, the combined weight for both Analysis and Visualization domains is 29%. These areas are split into two distinct sections. The Analysis portion focuses on how to extract meaningful insights from Big Data sets, while the Visualization domain assesses your ability to communicate findings through tools like Amazon Quicksight or other AWS visualization services.
  • Data Analytics Specialty: For the Data Analytics Specialty exam, Analysis and Visualization are merged into a single domain with a total weight of 18%. While still significant, the combined weight of these domains is slightly lower than in the Big Data Specialty exam. This reflects the broader focus of the Data Analytics exam on the entire analytics workflow, including collection, processing, and storage.
• Security Domain:
  • Big Data Specialty: Security holds a weight of 20% in the Big Data Specialty exam, emphasizing the importance of protecting large-scale data systems. This domain covers critical areas such as encryption, data access control, and secure data storage and processing. Candidates must demonstrate proficiency in AWS security services, including IAM roles and policies, to ensure that data remains secure throughout its lifecycle.
  • Data Analytics Specialty: In the Data Analytics Specialty exam, the Security domain has a slightly reduced weight of 18%. While security is still a crucial aspect of the exam, the focus is more on securing data analytics workflows, including managing access control for data lakes and securing the pipeline for data transformation and analysis.

Understanding the differences in exam structure, duration, and domain weightings is essential for effective preparation. Both the AWS Certified Big Data Specialty and the AWS Certified Data Analytics Specialty exams require a deep understanding of AWS services, data management practices, and security protocols. However, each exam emphasizes different aspects of the data lifecycle.

• Big Data Specialty: Focus on system design, large-scale data processing, and automating data analysis processes. Ensure that you are comfortable with Big Data services like Amazon EMR, Kinesis, and Redshift.
• Data Analytics Specialty: Emphasize data analytics workflows, including data collection, processing, storage, and visualization. Pay special attention to services such as Amazon Quicksight, AWS Glue, and Amazon Athena.

To maximize your chances of success, utilize resources such as AWS documentation, sample questions, and practice exams. Platforms like ExamLabs offer a variety of practice tests that simulate the actual exam environment, providing valuable insights into your preparedness.

By focusing your study efforts based on the domain weightings and ensuring that you cover all relevant services and concepts, you will be well-equipped to succeed in either certification exam.

As cloud technologies continue to evolve, so too do the certification offerings from Amazon Web Services (AWS). For professionals aiming to enhance their credentials in data management and analytics, AWS provides two valuable certifications that were once considered complementary: the AWS Certified Big Data Specialty exam and the AWS Certified Data Analytics Specialty exam. However, with the retirement of the Big Data Specialty exam, the Data Analytics Specialty is now the primary certification path for those focused on AWS analytics services.

This decision-making process—whether to pursue the retired Big Data Specialty exam or the evolving Data Analytics Specialty certification—requires careful consideration of your professional experience, goals, and the unique skills you wish to develop. Both exams are essential for professionals in the field of cloud data analytics, but understanding their differences and aligning them with your expertise can set you on the right path toward success.

Understanding the AWS Certified Big Data Specialty Exam’s Retirement

The AWS Certified Big Data Specialty exam has been a recognized certification for professionals skilled in managing large-scale data systems within the AWS ecosystem. For individuals already working with big data processing and complex analytics on AWS, the Big Data Specialty exam has served as a valuable credential for establishing proficiency in key AWS services such as Amazon EMR, Redshift, and Kinesis.

However, as cloud computing technologies have advanced and the demand for more sophisticated data analytics tools has increased, AWS decided to retire the Big Data Specialty exam and replace it with the Data Analytics Specialty certification. This change reflects the growing need for professionals who are not only adept at managing large data sets but also skilled in deriving meaningful insights from them using AWS’s evolving suite of analytics tools.

For those who are considering taking the AWS Certified Big Data Specialty exam, it’s important to note that the certification will officially retire soon. Therefore, if you are aiming to pursue this certification, you need to complete your preparation and take the exam before the official retirement date. After that, the Data Analytics Specialty will be the recommended certification for anyone working with AWS’s data services.

Why the AWS Certified Data Analytics Specialty Is the Best Option for Modern Professionals

With the retirement of the Big Data Specialty exam, the AWS Certified Data Analytics Specialty exam is the new gold standard for data professionals looking to showcase their AWS expertise. This certification is focused on a broader range of AWS analytics services, including data lakes, data warehouses, and various tools for visualizing and interpreting data.

The Data Analytics Specialty certification tests your ability to work with AWS services such as Amazon Redshift, AWS Glue, Amazon QuickSight, and Amazon Kinesis. It’s designed for professionals who want to prove their ability to design, secure, and manage data analytics solutions on AWS, particularly those focused on gaining insights from complex data sets.

If your work revolves around developing data pipelines, building data lakes, or working with analytics tools to derive business intelligence, the Data Analytics Specialty exam is the ideal choice. It’s a more comprehensive exam that takes a deep dive into the entire data lifecycle, from collection and processing to storage, analysis, and visualization.

This certification will be valuable if you are looking to advance in roles such as data analysts, data engineers, or cloud data architects, where analytics is a critical function of the business. By earning this certification, you will demonstrate your ability to make data-driven decisions using AWS tools, which can significantly enhance your career prospects in the ever-evolving field of data analytics.

Big Data Specialty: Still a Valid Option for Certain Professionals

While the AWS Certified Big Data Specialty exam is retiring, it still holds value for certain professionals, particularly those with a deep background in traditional Big Data analysis. This certification is tailored for individuals who have a strong foundation in managing vast, complex datasets and are skilled in data engineering roles, such as Big Data architects and specialists.

If you’re someone who has significant experience with AWS Big Data tools and focuses on managing data infrastructure at scale, you may find the Big Data Specialty certification useful for your career. Although the exam will no longer be available after its retirement date, having this certification on your resume could still provide credibility for your expertise, especially if you’ve already completed it or are in the process of preparing for the exam.

However, professionals who are considering taking the Big Data Specialty exam before its retirement should recognize that the field of data analytics has moved toward a more holistic approach, one that goes beyond just managing large datasets to providing actionable insights from those datasets. Therefore, transitioning to the Data Analytics Specialty exam may provide more future-proof skills that align with current industry trends.

Choosing Between the Two: Aligning the Exam with Your Career Goals

To decide which exam is the best fit for you, consider the specific skills you want to develop and the career path you envision. Here are a few key points to help guide your decision:

• If you’re focused on Big Data infrastructure: The Big Data Specialty certification is the go-to credential for those managing data pipelines, data lakes, and large-scale data storage solutions within the AWS ecosystem. This certification focuses on the technical implementation of Big Data systems, including the management and optimization of massive datasets. If you work primarily as a Big Data engineer or architect, this certification has been valuable in the past and can still serve you well if you are already familiar with its content.
• If you’re interested in data analytics and insights: The Data Analytics Specialty exam is ideal for professionals who aim to work with data analytics services and tools to turn raw data into actionable insights. This exam covers a broader range of services, including Amazon Kinesis for real-time analytics, AWS Glue for data integration, and Amazon QuickSight for visualization. If your focus is on processing data for analysis, visualization, and decision-making, then the Data Analytics Specialty exam is the perfect fit.
• If you’re seeking career flexibility and growth: The Data Analytics Specialty exam is likely to be more beneficial in the long run. As businesses increasingly rely on cloud-based data analytics to drive decision-making, proficiency in AWS analytics services will become increasingly valuable. Even though the Big Data Specialty exam is retiring, the tools and concepts it covers are still critical. However, the Data Analytics Specialty exam addresses the evolving needs of the industry, with an emphasis on deriving insights from data, making it a more future-proof certification.

Start Your Preparation with the Right Resources

Regardless of which exam you pursue, starting your preparation with the right resources is key to success. Platforms like ExamLabs offer comprehensive practice exams that simulate real test scenarios, providing a valuable tool for assessing your knowledge and preparing for the actual exam.

In addition to practice tests, hands-on experience with AWS services is crucial for developing a deep understanding of how to design and manage data systems. AWS provides a range of training courses, whitepapers, and documentation that can help you become familiar with the platform’s tools and services.

Conclusion: 

In conclusion, while the AWS Certified Big Data Specialty exam is retiring, it remains a valuable certification for professionals with a deep understanding of Big Data infrastructure on AWS. However, for those looking to stay current with the latest trends in data analytics and gain a more holistic view of data management, the AWS Certified Data Analytics Specialty exam is the ideal certification.

As you embark on your preparation journey, make sure to evaluate your career goals and focus on the skills that will best support your aspirations. Whether you choose the retired Big Data Specialty or the evolving Data Analytics Specialty, both certifications offer excellent opportunities for career growth and can help you stay competitive in the rapidly growing field of cloud-based data analytics. Good luck with your exam preparations!