8 top cloud analytics certifications

Benefits for individuals

Certifications objectively recognize professional knowledge, demonstrating a practitioner's ability to meet industry best practices to employers and peers. For many, a certification is a stepping stone to a promotion or a new role.

Strong certification programs emphasize practical learning through labs, projects and real-world case studies. Unless they already have experience, many professionals find that the certification process provides broader, more practical hands-on learning than their current role affords. This is especially true when obtaining a certification in cloud data analysis before migrating to the cloud. By the time candidates earn the credential, they have solved problems in simulated cloud environments, preparing them to handle similar challenges on the job from day one.

However, more advanced certifications often require experience that can only be gained on the job. For example, AWS recommends at least two years of real-world experience before taking its Data Analytics Specialty exam, emphasizing the practical skills involved.

Benefits for businesses

Organizations with certified cloud data analytics professionals are better equipped to make informed, data-driven decisions.

Skilled analysts can use cloud tools to derive insights from large data sets so that leaders can base decisions on facts instead of guesswork. In practice, certified experts don't just help the business run better; they offer opportunities to rethink business strategies to become more data-driven and increasingly AI-ready.

An essential component of cloud analytics is managing data workflows and cloud resources efficiently. Certified staff can optimize data pipelines, automate processes and use cloud services more cost-effectively. Cloud computing costs are often a concern for CFOs, so training and certification are likely worthwhile if they lead to more efficient processes.

In short, investing in certifications helps organizations do more with their data while reducing waste and errors. If CFOs are concerned with rising costs, IT leaders are equally focused on security, governance and compliance demands when data resides in the cloud.

Employees with strong credentials, especially from cloud vendors, will be up to date with best practices for protecting sensitive information and complying with regulatory standards. This expertise reduces the risk of data breaches and helps organizations comply with frameworks like GDPR and HIPAA.

Improved governance is a common reason for moving data to the cloud. Certified professionals help organizations confidently adopt cloud analytics tools, knowing security best practices and compliance measures are in place from day one. Without strong cloud knowledge, analysts might be tempted to download data for on-premises analysis, undermining the advantages of a well-governed cloud deployment.

Some popular credentials

The following certifications and professional training programs are among the most popular. They are delivered by well-recognized cloud vendors and focus on practical skills. These programs blend cloud-specific tools, industry-standard platforms and advanced analytics to support roles ranging from entry-level analysts to senior data engineers. Each one builds proficiency in modern cloud workflows.

When choosing a course, professionals often look for certification in the technology they are currently using. However, when the goal is broader career growth and strategic skills development, there are several features to look for:

• Comprehensive curriculum. The certification should cover a wide range of topics essential for cloud data analytics, such as the following:

• Data management and transformation.
• Data visualization and storytelling.
• Cloud-native tools.
• Integration of generative AI.

• Hands-on experience. The program should include hands-on labs and, ideally, a capstone project to provide practical, job-ready experience.
• Cloud-specific focus. Unlike a general data analytics certification, your program should be tailored to cloud ecosystems.
• Accessibility. To make it accessible to working professionals, look for a course designed to be completed in about three months at no more than 10 hours per week.
• Up-to-date content. The program should be regularly updated to include the latest trends and technologies in cloud data analytics.

Comparison table

The level of each certification is estimated from entry-level to advanced based on the key skills involved and the prior experience required:

AWS Certified Data Analytics -- Specialty

• Skills taught:
  • AWS services (Redshift, Kinesis, Glue) for big data pipelines.
  • Data lake architecture, real-time analytics and cost optimization.
• Why it's valuable:
  • High demand for AWS expertise in cloud-native analytics roles.
  • Focuses on scalable tools and machine learning integration.
• Study materials:
  • Free course: Exam Readiness: AWS Certified Data Analytics -- Specialty.
• Investment:
  • Time: 2-3 months (prior AWS experience recommended).
  • Cost: $300 exam fee.

Microsoft Azure Data Scientist Associate

• Skills taught:
  • Machine learning on Azure (ML Studio, Databricks).
  • Experimentation, model deployment and MLOps.
• Why it's valuable:
  • Validates end-to-end AI/ML workflows in Azure's cloud ecosystem.
  • Ideal for roles blending data science and cloud engineering.
• Study materials:
  • Numerous third-party options based on Microsoft's curriculum.
• Investment:
  • Time: 2-3 months.
  • Cost: $165 exam fee.

Databricks Certified Associate Developer for Apache Spark

• Skills taught:
  • Distributed data processing, Spark SQL and Delta Lake.
  • Optimization for cloud platforms (AWS/Azure).
• Why it's valuable:
  • Critical for big data roles using Databricks' unified analytics platform.
  • High demand in enterprises using Spark for ETL and ML.
• Study materials:
  • Instructor-led and self-paced courses.
• Investment:
  • Time: 1-2 months.
  • Cost: $200 exam fee.

Google Cloud Data Analytics Professional Certificate

• Skills taught:
  • Data management (BigQuery, Cloud Storage), transformation and visualization (Looker).
  • Generative AI integration, cloud-native tools and storytelling with data.
• Why it's valuable:
  • Directly aligned with Google Cloud's ecosystem, ideal for roles in cloud analytics.
  • Hands-on labs and a capstone project, providing job-ready experience.
• Study materials:
  • Google Cloud Skills Boost, 100 hours of online training.
• Investment:
  • Time: ~3 months (10 hrs/week).
  • Cost: $49/month on Coursera.

IBM Data Analyst Professional Certificate

• Skills taught:
  • Python, SQL, Excel, and IBM Cognos Analytics.
  • Data wrangling, visualization, and cloud database integration.
• Why it's valuable:
  • Covers end-to-end analytics workflows, including AI applications.
  • ACE®-recommended for college credit.
• Study materials:
  • Online course, hosted by Coursera.
• Investment:
  • Time: ~4 months (10 hrs/week).
  • Cost: $39/month on Coursera.

Microsoft Certified: Power BI Data Analyst Associate (PL-300)

• Skills taught:
  • Data preparation, modeling (DAX), visualization and dashboard deployment.
  • Integration with Azure services and AI-driven insights.
• Why it's valuable:
  • This is the industry standard for Power BI and is critical for roles in BI.
  • Validates cloud-based reporting and security management.
• Study materials:
  • Numerous third-party options based on Microsoft's curriculum.
• Investment:
  • Time: 1-2 months (exam prep).
  • Cost: $165 exam fee; $39/month for Coursera training.

SAS Certified Advanced Analytics Professional

• Skills taught:
  • Predictive modeling, machine learning and statistical analysis.
  • Deployment of models in cloud and hybrid environments.
• Why it's valuable:
  • Recognized in industries like finance and healthcare for advanced analytics.
  • Combines SAS tools with open source frameworks.
• Study materials:
  • Online courses using SAS learning subscriptions, instructor-led courses and practice exams.
• Investment:
  • Time: 6-12 months (multiple exams required).
  • Cost: $180-$250 per exam.

SnowPro Advanced: Data Analyst Certification

• Skills taught:
  • Data preparation and loading.
  • Advanced SQL query building and troubleshooting.
  • Utilization of Snowflake's built-in functions and creation of UDFs.
  • Descriptive and diagnostic data analysis, including forecasting.
  • Data presentation and visualization aligned with business needs.
• Why it's valuable:
  • Snowflake's Data Cloud Academy enables the skills to deploy several analytical workloads across different clouds and regions.
  • For this certification, four domains of knowledge are covered:
    • Data ingestion and data preparation.
    • Data transformation and data modeling.
    • Data analysis.
    • Data presentation and data visualization.
• Study materials:
  • Self-study and instructor-led courses with practice exams.
• Investment:
  • Time: There's no specific time investment, but a Core certification is needed to qualify for this advanced certificate.
  • Cost: $375.

Donald Farmer is a data strategist with 30-plus years of experience, including as a product team leader at Microsoft and Qlik. He advises global clients on data, analytics, AI and innovation strategy, with expertise spanning from tech giants to startups. He lives in an experimental woodland home near Seattle.