7 AWS Services Every Data Engineer Should Master

If you work in data engineering, you know the job is a constant balance between speed, reliability, and maintainability. It is not enough to just move data from one system to another. You must make sure the process will still work next month, that it can handle more volume next year, and that someone else can understand it without asking you a hundred questions.

I have been through years of building ETL jobs, chasing down broken pipelines, rewriting transformations after schema changes, and tuning performance when reports were too slow to be useful. The biggest lesson is that the tools you choose matter more than you think. The wrong choice can lead to constant firefighting. The right one can make the system almost invisible because it just works.

AWS offers dozens of services for working with data. Most production architectures only rely on a small set of them. Whether you are processing nightly batches or building real-time streams, the same few tools keep showing up. Here are the seven AWS services that have consistently proved useful in real-world data projects.

1. Amazon S3: The Data Lake Foundation

S3 is where most AWS-based data platforms begin. It is durable, cheap, and can store any type of file. I have used it for raw logs, intermediate transformations, and curated datasets ready for analytics.

A good folder structure is critical. On one project, we set up separate zones for raw, staging, and curated data. That simple choice made pipelines more predictable and kept us from mixing half-processed files with production-ready data. S3 is also more than storage. You can trigger events when a file arrives, which means you can start downstream processes instantly without running constant checks.

2. AWS Glue: Managed ETL for Batch Jobs

Glue runs Apache Spark without you having to manage a cluster. It is perfect for scheduled transformations and data cleaning. I have used it to convert CSV files into Parquet, remove bad records, and populate the Glue Data Catalog so other services could query the data without extra setup.

One thing I learned is that Glue’s default settings are not always the best. Tuning the number of DPUs and enabling bookmarks to avoid reprocessing old files can save both time and cost. Glue works well with S3, Athena, and Redshift, which means you can connect these pieces without extra engineering.

3. AWS Lambda: Small, Fast, Event-Driven Jobs

Lambda is a good fit for quick tasks that need to run when something happens. I have used it to validate new files in S3, start Glue jobs, and send alerts when certain conditions are met.

It is not meant for heavy processing. There are limits on how long a Lambda can run and how big the payload can be. In one system, we broke a larger job into smaller steps, each handled by a separate Lambda, with SQS queues between them. That way, no single function became a bottleneck and everything stayed responsive.

4. Amazon Athena: SQL Without a Warehouse

Athena lets you run SQL directly on data in S3. It is the fastest way I know to answer questions like “Did yesterday’s job drop a column?” or “How many records have a null customer ID?” without loading the data into a warehouse first.

Costs can climb if you scan too much. Partitioning your data and storing it in formats like Parquet or ORC will reduce the amount read per query. I once reduced a scan from hundreds of gigabytes to under ten simply by adding partitions. That also made the queries much faster.

5. Amazon Redshift: High-Performance Analytics

When you need to query billions of rows and get results quickly, Redshift is a solid choice. I have used it for BI dashboards, scheduled reporting, and large-scale analytics.

Performance depends heavily on how you design your tables. Choosing the right sort keys and distribution styles can make the difference between seconds and minutes. Redshift Spectrum is also useful because it allows queries on S3 data without loading it into the warehouse, which means you can combine your data lake and warehouse in one query.

6. Amazon EMR: Big Data With More Control

Glue is great for most batch jobs, but sometimes you need full control over the environment. EMR is a managed Hadoop and Spark cluster where you can install exactly what you need.

I once had to process a machine learning dataset that required specific Python libraries not supported by Glue. EMR allowed us to set up the environment, run the job at scale, and shut it down when we were done. It takes more effort to configure than Glue, but you get flexibility in return. 

7. Amazon Kinesis: Handling Data in Motion

Kinesis is built for streaming data. I have used it for clickstream analytics, real-time log processing, and powering live dashboards. It can deliver data to S3, Redshift, or even process it in-flight with Lambda.

When working with Kinesis, plan for scaling early. Shards determine how much data you can handle, and changing them during peak traffic can get tricky. For simpler cases, Kinesis Data Firehose is easier because it handles batching and retries automatically before loading into storage or a warehouse.

Putting It Together

Most production systems use a mix of these services. A typical pattern looks like this:

• Data lands in S3 from an external source
• A Lambda function checks the file and starts a Glue job
• The Glue job transforms the data and writes it back to S3
• Athena queries are run for validation or ad hoc analysis
• Cleaned data is loaded into Redshift for analytics and BI reporting
• Kinesis handles any streaming sources feeding the same pipeline
• EMR is used for special cases where custom processing is needed

This combination covers storage, transformation, event handling, interactive queries, heavy analytics, big data processing, and real-time streams. Once you are comfortable with these tools, you can connect them in different ways depending on the problem.

Conclusion

The AWS ecosystem can seem overwhelming at first. There are many overlapping tools, and each has its own learning curve. But in practice, most data engineers only use a core set of services.

S3, Glue, Lambda, Athena, Redshift, EMR, and Kinesis have proved themselves again in real production environments. You do not need to master them all at once. Start with the one that solves your immediate problem, then learn the others as your architecture grows.

Over time, you will know exactly which service to reach for in each situation. That is when your pipelines become faster to build, easier to maintain, and more reliable in the long run.