Choose your role, experience and technology. Compare relevant certifications, check exam details and request pricing for your shortlist.
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Your experience and technology stack make a difference.
An earlier-career route into working with data on Google Cloud before moving to professional specialisations.
Practise Google Cloud data services and basic data preparation before attempting the exam.
A starting point for understanding data workloads before specialising in Fabric, databases or engineering.
Beginner-friendly; follow with SQL practice and a small data project.
A direct fit for engineers building AWS data pipelines. Shows platform-specific knowledge that supports dependable analytics and AI data foundations.
Build SQL/Python pipelines and practise AWS data services before booking.
Useful when Spark programming is central to the role, including data engineering work beyond one specific cloud.
Become comfortable writing and debugging Spark DataFrame transformations.
Connects data engineering with agent systems by focusing on assembling and governing useful context for AI.
Build familiarity with Databricks data, retrieval and agent workflows.
A strong platform choice when your team engineers data in Databricks, whether hosted on AWS, Azure or Google Cloud.
Build practical SQL, Spark and Databricks pipeline experience.
Useful when your role connects engineering with enterprise analytics and semantic models in Microsoft Fabric.
Have experience creating and managing analytical assets and querying data.
A direct fit for Microsoft Fabric data engineers building, orchestrating and managing analytical data solutions.
Practise Fabric ingestion and orchestration, SQL and PySpark.
For consultants implementing Salesforce enterprise data solutions.
Practise the relevant Salesforce product in a hands-on environment before booking.
A foundation for working with Snowflake before choosing advanced engineering, analytics or AI specialisations.
Practise Snowflake loading, querying, access controls and warehouse management.
Fits engineers responsible for advanced, dependable data workloads on Databricks.
Gain production Databricks experience, including troubleshooting and optimising pipelines.
A direct fit for engineers designing and managing Google Cloud data systems. Most useful when supported by real platform delivery experience.
Google recommends 3+ years of industry experience, including 1+ year designing and managing Google Cloud solutions.
These are Global Certs IT editorial fit ratings, not official provider rankings or learner reviews. A 5/5 role match is directly aligned with the role; 4/5 adds supporting skills. Experience adjusts the score to help you distinguish a practical next step from a stretch goal.
With “Any experience”, ratings show role relevance alone. A credential supports your profile; practical skills, projects and your target employer’s technology still matter.
Official pages were checked on 2026-09-13. Bookable betas are labelled. Known retired exams are excluded from recommendations. Confirm exam versions, regional availability, eligibility and fees on the linked official page before paying.
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Build a batch-to-streaming pipeline with quality checks, orchestration and a documented recovery plan.