9 Alternatives to the AWS Data Analytics Certification

If you've been researching the AWS Certified Data Analytics – Specialty credential, there's an important update you need to know before you go any further: AWS retired this certification in 2024, and it's no longer possible to earn it. The good news is that AWS and other major providers have built several strong data analytics certificates that cover similar ground, some even more directly aligned to today's data engineering and data analytics job roles than the original exam was.

This article walks through exactly what happened to the original certification, what AWS officially recommends in its place, and nine alternatives worth considering depending on your specific career goals and preferred cloud platform. Whether you were partway through preparing for the retired exam or just starting your research, you'll find a clear path forward here.

What Happened to the AWS Data Analytics Certification?

AWS Certified Data Analytics – Specialty, formerly known as AWS Certified Big Data – Specialty, was officially retired on April 9, 2024, with the last date to sit the exam falling on April 8, 2024. AWS retired this certification alongside two other specialty-level credentials, the Database Specialty and SAP on AWS Specialty exams, as part of a broader strategy shift toward fewer specialty certifications and more role-focused credentials at the foundational, associate, and professional levels.

If you already held the certification before its retirement, your credential remains valid through its standard three-year validity period, though you can no longer recertify by retaking the same exam once that period ends. AWS positioned its new AWS Certified Data Engineer – Associate certification as the direct successor, designed to align more closely with the modern data engineer job role and cover the ingestion, transformation, storage, and management of data on AWS. The shift reflects a broader industry trend, since AWS has continued consolidating other specialty certifications, including its Machine Learning Specialty exam, into similarly role-focused associate-level credentials since then.

Best Alternatives to the AWS Data Analytics Certification

Each of these nine certifications offers a legitimate path forward, whether you want to stay within the AWS ecosystem or explore other cloud and data platforms entirely. Compare the scope and cost of each closely, since the right replacement depends heavily on which specific skills your target employers actually value.

1. AWS Certified Data Engineer – Associate

The AWS Certified Data Engineer – Associate certification is AWS's official, direct replacement for the retired Data Analytics Specialty exam, and it's the most logical starting point for anyone who was specifically targeting the original credential. The exam covers data ingestion, transformation, storage, and the ongoing management of data pipelines on AWS, with a somewhat narrower focus on the data engineer role compared with the broader analytics scope the original specialty exam covered.

At $150, the exam costs meaningfully less than the retired specialty-level credential typically did, reflecting AWS's shift toward more accessible associate-level pricing across its newer certifications. Because this is the certification AWS itself points to as the successor, it carries the clearest continuity with the original credential in the eyes of employers already familiar with AWS's certification track.

2. AWS Certified Machine Learning Engineer – Associate

The AWS Certified Machine Learning Engineer – Associate certification, launched in October 2024, validates the ability to build, deploy, and maintain machine learning solutions on AWS, with a strong emphasis on production deployment, monitoring, and security rather than pure algorithm theory. This credential itself replaced the AWS Certified Machine Learning – Specialty exam, which followed the Data Analytics Specialty into retirement in March 2026, continuing the same consolidation pattern.

At $150, the exam costs the same as the Data Engineer Associate certification, and it's a strong complementary option for data professionals whose work increasingly overlaps with machine learning pipelines rather than pure data analytics. This path makes the most sense if your target roles involve deploying models into production alongside traditional data engineering work.

3. Google Cloud Professional Data Engineer

Google Cloud's Professional Data Engineer certification validates the ability to design, build, and manage data processing systems on Google Cloud Platform, covering data pipeline design, machine learning model deployment, and data governance considerations. At $200, the exam costs somewhat more than AWS's associate-level options, reflecting its broader scope and the three to four months of preparation most candidates typically need.

This certification makes the most sense if your target employers use Google Cloud rather than AWS, since the underlying skills, while conceptually similar to AWS's data engineering competencies, are tested specifically against Google's own tools and services. Professionals open to working across multiple cloud platforms sometimes pursue both an AWS and a Google Cloud credential to broaden their job market appeal.

4. Microsoft Certified: Azure Data Engineer Associate

The Microsoft Certified: Azure Data Engineer Associate certification, earned through the DP-203 exam, validates skills in designing and implementing data storage, processing, and security solutions within Microsoft's Azure ecosystem. This credential serves as the closest Azure-specific equivalent to what the AWS Data Analytics Specialty once covered, making it a natural alternative for professionals working at organizations built around Microsoft's cloud platform rather than AWS.

The exam covers data ingestion, transformation, and monitoring using Azure-specific tools, giving it real value for data engineers and analysts whose employers have standardized on Microsoft's ecosystem. Because Azure remains one of the most widely adopted enterprise cloud platforms, this certification carries meaningful weight in corporate environments outside the AWS-dominated startup and tech sectors.

5. Microsoft Certified: Power BI Data Analyst Associate

The Microsoft Certified: Power BI Data Analyst Associate credential, earned through the PL-300 exam, takes a different angle than the more infrastructure-focused alternatives on this list, validating hands-on skills in building data models, dashboards, and reports specifically within Power BI. At around $165, the exam costs less than most cloud-platform certifications, and preparation typically takes two to four months for someone with some existing data background.

This certification is a better fit than the more engineering-focused alternatives if your work centers on business intelligence, reporting, and dashboard creation rather than building and managing data pipelines. Employers evaluating candidates for reporting-heavy or business intelligence roles often specifically look for this certification as a signal of practical, tool-specific competence.

6. Databricks Certified Data Engineer Associate

The Databricks Certified Data Engineer Associate certification validates skills in building data pipelines using Databricks's lakehouse architecture, covering Delta Lake, Spark DataFrames, structured streaming, and job orchestration on the platform. At $200, the exam is priced comparably to other major platform certifications, and Databricks recommends at least six months of hands-on platform experience before attempting it, even though there's no formal prerequisite.

This certification makes particular sense if your target organizations use Spark and lakehouse-style architectures rather than a specific cloud provider's native tools, since Databricks operates across multiple cloud platforms rather than being tied to AWS, Azure, or Google Cloud exclusively. Data engineers who want a credential with strong recognition specifically in Spark-heavy environments tend to gravitate toward this option over a single cloud provider's certification.

7. Snowflake SnowPro Core Certification

The Snowflake SnowPro Core Certification serves as the entry point into Snowflake's certification ecosystem, validating foundational knowledge of Snowflake's architecture, SQL usage, data loading, and performance considerations. At $175, the exam is one of the more affordable options on this list, and Snowflake recommends at least six months of hands-on platform experience before attempting it, even though no prerequisites are formally required.

Because Snowflake remains particularly popular in enterprise environments, including financial services and healthcare organizations that prioritize governance and reliable data sharing, this certification carries real weight for professionals targeting those specific industries. Data professionals who want to go deeper can pursue Snowflake's Advanced-level certifications afterward, though the Core credential alone already signals meaningful platform competence to employers.

8. CompTIA Data+

CompTIA Data+ takes a vendor-neutral approach, covering data mining, analysis, visualization, and governance without tying candidates to any single cloud provider or platform's specific tools. The certification costs between roughly $225 and $340 depending on current pricing, and most candidates prepare for one to six months depending on their existing familiarity with data concepts.

This certification is a strong choice if you want a portable credential that doesn't signal allegiance to a specific cloud ecosystem, which can be valuable if you're still deciding which cloud platform to specialize in or if your career involves working across multiple client environments. Pairing CompTIA Data+ with a platform-specific certification, such as one of the AWS or Azure options above, can offer a well-rounded combination of broad literacy and specific technical depth.

9. Certified Analytics Professional (CAP)

The Certified Analytics Professional credential, awarded by the Institute for Operations Research and the Management Sciences, takes a notably different approach from the more technical alternatives on this list, validating the ability to frame business problems and apply analytics strategically rather than testing knowledge of a specific platform or cloud service. Candidates typically need either a master's degree with several years of relevant experience or a bachelor's degree with five or more years of experience to qualify, and the exam costs between $495 and $695 depending on membership status.

This certification makes the most sense for experienced analytics professionals moving into leadership or consulting roles, rather than as a direct technical replacement for the platform-specific skills the original AWS certification covered. Professionals who already hold a technical, platform-specific credential from elsewhere on this list sometimes pursue CAP later in their careers as a complementary, strategy-focused credential.

How We Rank These Alternatives

To create this list, we evaluate how closely each certification's scope aligns with what the original AWS Data Analytics Specialty covered, alongside cost, format, and standing among employers. The criteria below reflect what matters most when comparing a retired credential's replacements.

• Relevance to the retired exam's scope: Certifications are evaluated on how directly they cover the data ingestion, transformation, and analytics skills the original AWS credential once tested.
• Affordability: Programs are evaluated on exam cost relative to the depth and career value of the skills validated.
• Provider reputation: Certifications from major cloud providers and established industry organizations are weighed based on employer recognition across different sectors and platforms.

What You'll Learn

Across these nine alternatives, expect coverage of data pipeline design, cloud-based data storage and processing, and the tools used to transform raw data into usable business insights. Skills commonly covered across these programs include:

• Data ingestion, transformation, and pipeline design
• Cloud-based data storage and processing architecture
• Data governance, security, and access management
• Dashboard and report creation using tools like Power BI or platform-native visualization tools
• SQL and platform-specific query languages
• Machine learning integration within modern data pipelines, depending on the specific certification

How Much Do These Certifications Cost?

Exam costs across this list range from $150 for AWS's associate-level certifications up to $695 for the most experienced-focused option, the Certified Analytics Professional credential. Most platform-specific certifications from AWS, Google Cloud, Databricks, and Snowflake fall in a fairly tight range between $150 and $200, reflecting a broader industry shift toward more accessible associate-level pricing compared with the specialty-level exams these newer options often replaced.

Beyond the exam fee itself, budget for practice exams, official study materials, and in some cases instructor-led prep courses, particularly for more technically demanding certifications like the Google Cloud Professional Data Engineer exam. Most of these certifications also require renewal every two to three years, so factor ongoing recertification costs into your long-term budget rather than evaluating the initial exam fee in isolation.

Which Alternative Is Right for You?

If you were specifically preparing for the original AWS Data Analytics Specialty exam and want the closest possible continuation of that path, the AWS Certified Data Engineer – Associate certification is the clearest choice, since it's the credential AWS itself designed as the successor. Professionals whose work has shifted toward machine learning pipelines alongside traditional data work may find the AWS Machine Learning Engineer Associate certification a more relevant complement or alternative.

If your organization uses a different cloud platform entirely, choosing the certification native to that ecosystem, whether that's Google Cloud, Azure, Databricks, or Snowflake, will generally serve you better than forcing an AWS-specific credential onto a non-AWS environment. Professionals who work primarily in business intelligence and reporting, rather than data pipeline engineering, should strongly consider the Power BI Data Analyst Associate certification instead of any of the more infrastructure-focused options on this list.

Career Outlook for Data Analytics and Data Engineering Professionals

The skills covered across these certifications remain in strong demand as organizations continue investing in cloud-based data infrastructure and analytics capabilities. According to the Bureau of Labor Statistics, data scientists, an occupation closely related to the data engineering and analytics roles these certifications target, earn a median annual wage of $112,590, with employment projected to grow 34% through 2034, much faster than the average for all occupations.

That demand extends across nearly every major cloud platform rather than concentrating narrowly around AWS, which is part of why so many credible alternatives to the retired AWS certification now exist. Professionals who choose a certification aligned with their actual target employers' technology stack, rather than defaulting to whichever platform is most talked about, tend to see the strongest return on the time and cost invested in earning it.

FAQs About Alternatives to the AWS Data Analytics Certification

These are some of the most common questions people ask after learning that the AWS Data Analytics Certification has been retired. Review them alongside the alternatives above as you decide on your next step.

Is My AWS Certified Data Analytics – Specialty Still Valid?

Yes, if you earned the certification before its April 9, 2024 retirement date, it remains valid through its standard three-year validity period from when you earned or last recertified it. You won't be able to recertify by retaking the same exam once that period ends, since the exam itself no longer exists, so you'll need to pursue a different current certification, such as the AWS Certified Data Engineer – Associate, to maintain active AWS certification status afterward. Your resume and LinkedIn profile can still reference the credential accurately as long as it remains within its valid period.

Why Did AWS Retire the Data Analytics Specialty Certification?

AWS retired this certification, along with its Database Specialty and SAP on AWS Specialty credentials, as part of a broader strategy shift toward fewer specialty-level certifications and more certifications aligned closely with specific, in-demand job roles like data engineer. AWS has continued this consolidation pattern since then, also retiring its Machine Learning Specialty certification in 2026 in favor of a more role-focused associate-level credential. The shift reflects AWS's stated goal of better matching its certification offerings to how employers actually describe and hire for cloud data roles today.

Which Alternative Is Closest to the Original AWS Data Analytics Certification?

The AWS Certified Data Engineer – Associate certification is the closest direct alternative, since AWS itself designed and positioned it as the official successor to the retired specialty exam. That said, the newer certification has a somewhat narrower focus specifically on data engineering tasks like ingestion, transformation, and pipeline management, compared with the broader analytics scope the original specialty-level exam covered. If your work spans a wider range of analytics responsibilities, you may want to supplement this certification with an additional credential like Power BI's Data Analyst Associate certification to cover reporting and visualization skills more directly.

Do Employers Still Recognize the Retired AWS Data Analytics Certification?

Generally, yes, since a certification's retirement affects only whether new candidates can earn it, not whether existing holders' credentials remain a legitimate signal of skill and effort. Employers familiar with AWS's certification ecosystem typically understand that certifications get retired and replaced periodically, and a retired-but-still-valid credential on your resume shouldn't raise concerns on its own. That said, actively pursuing one of the current alternatives shows employers you're keeping your skills current with today's certification landscape rather than relying solely on a credential from an exam that no longer exists.

Should I Choose an Associate-Level or Specialty-Level Certification as My AWS Replacement?

For most professionals, starting with an associate-level certification like AWS Certified Data Engineer – Associate makes sense, both because it's the direct successor to the retired specialty exam and because AWS has generally shifted its most current, actively maintained certifications toward this level. Specialty-level certifications still exist in some areas, but AWS's broader consolidation trend suggests associate-level credentials aligned to specific job roles are likely to remain the more actively supported and recognized path going forward. If you already have significant hands-on experience and want to validate deeper expertise, moving on to a professional-level AWS certification after earning the associate credential is a reasonable next step.

Discover Data Analytics Certification Programs

The retirement of the AWS Data Analytics Certification doesn't mean your options have narrowed, since several strong alternatives now cover similar ground, often with more direct alignment to today's job roles. Browse programs through Learn.org and connect directly with certification providers to get personalized information on exam requirements, cost, and preparation resources.