Databricks vs AWS Certifications: Which Should You Take First?

"Should I take a Databricks certification or an AWS one first?" Many data and ML engineers run into this question when planning their next career move. The short answer: the right choice depends on the data platform your organization runs and the career path you're aiming for.

This article compares all 7 Databricks exams against the 6 main AWS data-track exams across cost, difficulty, language availability, and market value — then lays out the optimal exam order for each career path.

All 7 Databricks Exams vs 6 AWS Data Exams: Spec Comparison

First, let's line up the exam specs side by side. Databricks is uniform — every exam is $200 with a 70% pass score — while AWS exam cost and pass score vary by level.

Every AWS exam is available in Japanese, and Specialty exams are long — $300 for 180 minutes. Databricks is cheaper at a flat $200, but Spark Developer and GenAI Engineer are English-only. Per-question time is roughly 2 minutes in both Associate-level exams, with AWS giving slightly more breathing room.

Overall Comparison: Cost, Materials, and Brand Recognition

Beyond the raw exam specs, here's how the two platforms differ.

The gap in Japanese learning resources is a major factor. AWS certifications have a rich ecosystem of books, Udemy courses, blog posts, and official Japanese training — you can cover everything you need to pass in Japanese alone. Databricks has limited Japanese material, so you need to be comfortable reading the official English documentation. The good news: since 2025, Databricks Academy has added Japanese courses and more exams support Japanese, so the situation is improving.

Market Value Comparison: Job Postings and Salary Impact

In the Japanese job market, AWS brand recognition is overwhelming. "AWS SAA holder" is a preferred qualification in many cloud-related postings, and it's valued at SIers, consultancies, and end-user companies alike.

Databricks certifications, on the other hand, are positioned as specialist credentials focused on data engineering and ML. Job posting volume is lower than AWS, but they're a powerful differentiator at companies that have adopted or are evaluating Databricks. They are directly valued at companies that:

• Run Databricks on AWS or Azure Databricks in production
• Are migrating to a data lakehouse architecture
• Use MLflow and Unity Catalog as their MLOps foundation
• Operate large-scale Spark-based data processing pipelines

On salary, AWS SAA / SAP holders typically command 500k-1M JPY more than non-holders. Databricks DEA / DEP holders, when targeting data engineering roles specifically, tend to be preferred for postings in the 8M-12M JPY salary band. When you hold both, you can cover "infrastructure + data platform" end to end, which boosts your evaluation in architect and tech lead roles further.

Recommended Cert Combinations by Career Path

To answer "which exam should I take first?", here are the recommended routes by career path. The core strategy is "build cloud foundations with AWS, then sharpen your specialty with Databricks".

For data engineers, the standard route is AWS SAA → AWS DEA → Databricks DEA → DEP. Lock in cloud foundations like S3, IAM, VPC, and Glue with AWS SAA, learn data services like Kinesis, Redshift, and Athena with AWS DEA, then layer Delta Lake, Unity Catalog, and Spark SQL expertise on top with Databricks DEA / DEP.

For ML engineers, deciding between AWS MLA (SageMaker-centric) and Databricks MLA (MLflow-centric) first comes down to a simple rule: choose based on whether your org's ML platform is SageMaker or Databricks. Getting both eventually lets you contribute to SageMaker → Databricks ML migration projects too.

Which Cert Enterprises Value: By Platform

What enterprises want is a certification that directly maps to the platform they've adopted. Certs that don't match the in-house data stack get less weight, so check the tech stack of your target employer before deciding what to study.

At companies running Databricks on AWS, the most highly valued pattern is prove your infrastructure understanding with AWS certs, and prove your specialty with Databricks certs. Conversely, at companies built around AWS EMR / Glue / Redshift, the direct value of Databricks certs is limited. Research the target tech stack via LinkedIn job postings and engineering blogs before you build your exam plan.

The Optimal Order When Pursuing Both

If you plan to take both AWS and Databricks certifications, the following order is the most efficient.

Phase 1: AWS SAA (1-2 months)

Build your cloud foundations. Learn AWS core services like S3, IAM, VPC, Lambda, and RDS. This knowledge is also a prerequisite for studying Databricks on AWS, so locking it in first is efficient. Japanese Udemy courses plus the official Skill Builder are enough to pass.

Phase 2: Databricks DEA (1-2 months)

Take on Databricks Data Engineer Associate now that you have AWS foundations. Concepts like Delta Lake with S3 as the storage layer, or the relationship between IAM and Unity Catalog, click much faster when you already know AWS.

Phase 3: AWS DEA or MLA (1-2 months)

Data engineers pick up AWS DEA (Glue, Kinesis, Redshift, Athena). ML candidates pick up AWS MLA (SageMaker-centric). Think of this as adding data / ML specialization on top of the SAA foundation from Phase 1.

Phase 4: Advanced Databricks certs (2-4 months)

Finally, finish off with advanced Databricks certs like DEP (Professional) or MLA / MLP. By this stage, both AWS and Databricks foundations are solid, so you can handle advanced architecture-design questions with confidence.

Study Cost: Free Resources and Japanese Materials

AWS: Overwhelmingly rich Japanese resources

• AWS Skill Builder: Official free online courses. SAA and DEA learning paths are available in Japanese.
• Japanese books: Multiple SAA / SAP prep books are on the market. DEA Japanese books arrived from 2025.
• Udemy: Rich selection of Japanese SAA / SAP / DEA courses. 1,500-2,000 JPY during sales.
• AWS official practice exam: Free official Practice Exams are available inside Skill Builder.
• Hands-on: AWS Free Tier gives 12 months of free usage, keeping the cost of hands-on validation low.

Databricks: English-centric resources, but improving

• Databricks Academy: Official free learning paths. Some courses are now available in Japanese.
• Community Edition: Use a Databricks Workspace for free. Perfect for hands-on practice with notebooks and Delta Lake operations.
• Official Practice Exam: A free mock exam is provided for each cert. Great for getting a feel for question format and difficulty.
• Japanese books: Only 1-2 books target DEA, and Japanese books for Professional or ML exams are essentially nonexistent.
• Udemy: Mostly English courses. Japanese coverage is limited.

On study cost, AWS is far cheaper and easier to get started with. Databricks assumes you can read English documentation, so if English makes you uneasy, start with AWS to get comfortable with the study workflow, then move on to Databricks.

Key Exam Prep Tips

Tips for Databricks Exams

• Check the Exam Guide weighting first: For DEA, "ELT with Spark SQL & Python" is the largest domain at 29%. Concentrate your study time there.
• Prepare for both PySpark and SQL code questions: Expect both DataFrame API (.filter, .groupBy, .withColumn) and Spark SQL (MERGE INTO, COPY INTO).
• Memorize Delta Lake-specific features precisely: Time Travel, VACUUM, OPTIMIZE, Z-ORDER, Liquid Clustering, Change Data Feed — Delta Lake-specific features show up constantly.
• Understand the Unity Catalog permission model: How GRANT / REVOKE, the 3-level namespace (catalog.schema.table), External Locations, and Storage Credentials fit together.

Tips for AWS Exams

• Reason via the 5 pillars of the Well-Architected Framework: Evaluate answer choices against security, reliability, performance efficiency, cost optimization, and operational excellence.
• Internalize "most cost efficient" and "least operational overhead" tiebreakers: AWS exams heavily favor best-practice choices. Technically correct but cost-inefficient answers are still wrong.
• Prefer managed services: Glue, Athena, Kinesis, and other serverless options are usually correct over rolling your own on EC2.
• For DEA, concentrate on 5 services: Glue, Kinesis, Redshift, Athena, Lake Formation: These five cover the majority of the exam.

Sample Questions to Check Your Level

Sample questions that test your knowledge across Databricks and AWS. Try them while keeping the differences between the two platforms in mind.

Frequently Asked Questions