If you’re planning to build a career in data engineering, getting comfortable with AWS is a strong first step. It’s packed with tools that deal with how data is stored, processed, and protected, which makes it a good starting point for anyone thinking about a future in this technology.
If you’re ready to build a future in tech, an AWS data engineering course in Bhubaneswar can set you on the right track with real-world tools and job-focused training. We’ve outlined a few important AWS tools for data engineers and the topics you’ll want to dig into while you build your skills.
Core AWS Services for Data Engineering
- Amazon S3
Amazon S3 acts as a flexible cloud storage system, where data—both raw and processed—is saved as “objects” inside digital containers called buckets. With secure URL-based access and strong durability, it’s ideal for data lakes and supports both structured and unstructured data.
- Amazon EC2
EC2 provides scalable virtual servers in the cloud. ETL jobs, app hosting, and compute-heavy processing are all part of a data engineer’s routine. The good part? With scalable instance choices, they can adjust resources as needed and keep costs in check.
- IAM (AWS Identity and Access Management)
IAM ensures fine-grained access control, allowing engineers to enforce the principle of least privilege when managing user and service permissions. It also lets you set up trusted links between services like Lambda, S3, and Glue to keep things running securely and within compliance.
Networking Foundations
- Amazon VPC (Virtual Private Cloud)
The VPC serves as the core of AWS network security, separating compute and database services into secure private zones. It’s the data engineer’s job to manage access and maintain strong network separation, particularly for systems running in production.
- AWS Direct Connect
Direct Connect establishes a dedicated fibre connection between your on-premises network and AWS. This lowers latency, increases throughput, and can reduce bandwidth costs for hybrid or large-scale data workloads.
Database and Storage Services
Each of these—Amazon RDS, DynamoDB, and Redshift—brings something unique to the table. RDS handles relational data, DynamoDB is great for fast NoSQL tasks, and Redshift takes care of heavy analytics. They all integrate smoothly with AWS services such as S3.
Data Integration and ETL
- AWS Glue
AWS Glue makes it easier to manage ETL without worrying about servers. It can scan your data, figure out the structure, and build a catalogue for you. With Python or Scala, you can write jobs to clean, shape, and prep your data for analysis.
- AWS Data Pipeline
This orchestration service moves and transforms data between services or on‑premise sources. You define sources, destinations, and transformation tasks, then schedule them to automate workflows efficiently.
Don’t Miss: Is AWS Data Engineering Right for You?
Big Data & Real-Time Analytics
- Amazon EMR
Batch processing at scale becomes easier with managed clusters. EMR integrates with S3 to retrieve data, perform advanced analytics or machine learning, and auto-scales to optimise both cost and performance.
- Amazon Athena
Amazon Athena is a serverless query tool that uses Presto to run SQL queries directly on data stored in S3. It lets you use SQL to query structured or semi-structured data stored in S3—no servers to manage, and you pay only per query and the amount of data scanned.
- Amazon Kinesis
It’s a suite of tools designed for real-time data ingestion, processing, and analysis: - Data streams for streaming ingestion
- Firehose provides fully managed streaming data delivery to destinations like S3, Redshift, and Elasticsearch
- Data Analytics for SQL or Flink-based real-time analytics
- Video Streams is used to ingest video data for ML applications like computer vision and real-time analytics
Data Lake Governance
- AWS Lake Formation
Lake Formation makes it easier to build and manage secure data lakes on S3. It automates tasks like access control, data cataloguing, and ingestion, reducing the need for manual setup.
Application/Workflow Integration & Orchestration
- AWS Step Functions
You can build smooth workflows across AWS services—linking Glue crawlers, ETL tasks, and even hands-on steps all in one flow.
- Migration and On‑Prem Movement
AWS DMS allows seamless data migration from on-premise databases to AWS services like RDS, Redshift, and DynamoDB, with minimal downtime and real-time sync. If transferring large files over the internet is too slow, AWS Snowball lets you shift big chunks of data offline and safely upload them to AWS later.
Conclusion
Learning how to work with AWS isn’t just another skill. This could truly shift your entire career path for the better. Most companies depend on data to make decisions, so they are always looking for people who know how to handle it properly in the cloud. Whether it’s building real-time data pipelines, managing data lakes, or setting up machine learning tasks, AWS gives you the tools to do it right.
If you are thinking about a role that actually has a future, an AWS data engineering course in Bhubaneswar could be a great place to begin.
With AVD Group, you get hands-on learning, practical labs, expert guidance, and help preparing for your certification. So why wait? Join today!
