Data engineering is one of the fastest-growing specialisations in Indian IT, driven by analytics and AI projects that all depend on clean, well-organised data. Each major cloud now has a data engineering certification. This guide compares the three most common choices and helps you pick based on the platform you work with.
Details checked on the official Microsoft, AWS and Google Cloud certification pages in October 2026.
At a glance
| Microsoft Fabric Data Engineer Associate | AWS Certified Data Engineer – Associate | Google Cloud Professional Data Engineer | |
|---|---|---|---|
| Exam | DP-700 | DEA-C01 | Professional Data Engineer |
| Level | Intermediate | Associate | Professional |
| Fee | Country-based pricing (Microsoft associate-level price in India) | US$150 | US$200 plus tax |
| Core tools | Microsoft Fabric, lakehouses, data pipelines, SQL, PySpark, KQL | AWS Glue, Redshift, Kinesis, Athena, S3, Lake Formation | BigQuery, Dataflow, Pub/Sub, Dataproc, Cloud Storage |
| Renewal | Yearly, free online assessment | Every 3 years | Every 2 years |
Microsoft Fabric Data Engineer Associate (DP-700)
Microsoft Fabric brings data engineering, data warehousing, real-time analytics and Power BI into one platform. DP-700 tests three skill areas: implementing and managing an analytics solution; ingesting and transforming data; and monitoring and optimising an analytics solution. You need to be comfortable with SQL, PySpark and Kusto Query Language (KQL). The exam lasts 100 minutes. Microsoft has noted an update to the English version from 19 October 2026, so check the study guide for changes before you prepare.
Best for: engineers in organisations that use Power BI and the Microsoft data stack. This is common in Indian enterprises and in IT services firms serving Microsoft-heavy clients.
AWS Certified Data Engineer – Associate
The AWS data engineering certification covers data ingestion and transformation, data store management, data operations and support, and data security and governance on AWS. Expect questions on choosing between batch and streaming, designing data lakes on S3, cataloguing with Glue, and querying with Athena and Redshift.
Best for: data engineers at AWS-based product companies and startups, or cloud engineers moving into data.
Google Cloud Professional Data Engineer
This is the most senior of the three, and Google Cloud’s data tools, BigQuery especially, are highly regarded. The exam tests designing data processing systems, ingesting and processing data, storing data, preparing data for analysis, and maintaining and automating workloads. Like other Google Professional certifications, it is valid for two years.
Best for: experienced data engineers, analytics teams using BigQuery, and anyone targeting companies known for data-heavy work on Google Cloud.
Platform-independent skills that matter more than any certificate
- SQL: strong, fluent SQL is the most important data engineering skill on every platform.
- Python and Spark: for transformations at scale.
- Data modelling: star schemas, slowly changing dimensions, and lakehouse layers (often called bronze, silver and gold).
- Orchestration: scheduling and monitoring pipelines, for example with Apache Airflow or the cloud’s native tools.
- Data quality and governance: testing data, tracking lineage and controlling access, especially important under India’s data protection law.
How to choose
- Pick the certification for the cloud your current or target employer uses.
- If you are new to data engineering, start with an associate-level exam (DP-700 or AWS Data Engineer) rather than the Google Professional exam.
- Build a portfolio project alongside: for example, ingest a public dataset, transform it, load it into a warehouse, and build a dashboard. This is what interviewers will ask about.
Leave a Reply