Top 5 Cloud Data Engineering Tools You Must Learn in 2025

Data engineering is the backbone of today’s businesses, allowing organizations to process, store, and analyze large volumes of data in an efficient manner. With cloud computing on the rise, learning the correct cloud-based tools is essential for any aspiring or seasoned data engineer. If you want to establish a solid career in this domain, here are the top 5 cloud data engineering tools you need to learn in 2025.

1. Apache Spark
Apache Spark is among the strongest open-source big data processing frameworks. Because of its capacity to process vast amounts of data in real-time, Spark is being used for ETL (Extract, Transform, Load) processes, real-time analytics, and machine learning pipelines on a large scale.
Why Apache Spark?

In-memory computation for rapid data processing

Easy integration with cloud platforms such as AWS, Azure, and GCP

Multiple language support with Python, Java, and Scala

2. Google BigQuery
Google BigQuery is a completely managed data warehouse where organizations can execute fast SQL queries against enormous datasets. It’s most commonly used for business intelligence, reporting, and real-time analytics.
Why Learn Google BigQuery?
Serverless and highly scalable data warehousing solution
Incorporated AI and ML capabilities
Has support for integrations with tools such as Looker, Tableau, and Data Studio

3. AWS Glue
AWS Glue is a managed ETL service that makes data preparation and transformation easy. It eliminates the need to manually discover, catalog, and evolve schemas, making it a must-have tool for cloud data engineers.
Why Learn AWS Glue?
Serverless ETL tool that minimizes infrastructure management
Supports Apache Spark and Python for data transformations
Easily integrates with AWS ecosystem (S3, Redshift, Athena, etc.)

4. Databricks
Databricks is a cloud-based data analytics platform based on Apache Spark. It offers a shared workspace for data engineers, scientists, and analysts to process and analyze data effectively.
Why Learn Databricks?
Unified data processing with AI/ML support
Optimized performance for large-scale analytics workloads
Integrated with leading cloud platforms (AWS, Azure, GCP)

5. Apache Airflow
Apache Airflow is an open-source workflow automation software for orchestrating intricate data pipelines. It enables data engineers to schedule, monitor, and manage workflows effectively.
Why Learn Apache Airflow?
Facilitates workflow automation and orchestration
Supports DAG (Directed Acyclic Graph) scheduling
Integrates seamlessly with cloud-based services such as AWS, GCP, and Azure
Final Thoughts
As cloud computing continues to evolve, mastering these cloud data engineering tools will be a game-changer for professionals in the field. Whether you’re a beginner or an experienced engineer, investing time in learning Apache Spark, Google BigQuery, AWS Glue, Databricks, and Apache Airflow will open new career opportunities and enhance your expertise in cloud data engineering.
Like to get your hands dirty with these tools? Join our Cloud Data Engineering Course and bring your skills up to date!
Begin your journey today!