Understanding Data Warehousing in AWS

AWS data warehousing has gotten complicated with all the services, architectures, and migration strategies flying around. As someone who has built and managed data warehouses on AWS for multiple organizations over the past several years, I learned everything there is to know about this critical piece of cloud infrastructure. Today, I will share it all with you.

If you’re dealing with large datasets and need analytical capabilities that go beyond what a regular database can handle, you’re in the right place. Data warehousing on AWS is one of those areas where getting the architecture right from the start saves you months of pain down the road. I’ve learned that lesson the hard way more than once, and I’m hoping to save you from making the same mistakes I did early in my career.

What is a Data Warehouse?

Probably should have led with this section, honestly. A data warehouse is a centralized repository where you pull in data from multiple different sources — your transactional databases, your CRM, your application logs, third-party APIs, marketing platforms, whatever — and store it in a format optimized for analysis and reporting. Unlike your regular operational databases that are tuned for fast reads and writes of individual records, a data warehouse is built to crunch through massive analytical queries spanning millions or billions of rows.

The key difference that took me a while to internalize: your operational database asks “what happened with order #12345?” while your data warehouse asks “what were the total sales by region for Q3, broken down by product category and customer segment?” Very different workloads, very different architectures. Trying to run analytical queries on your production database is a recipe for performance problems, and I’ve watched that scenario play out at multiple companies before they finally invested in a proper warehouse.

Data warehouses use techniques like columnar storage, data compression, and massively parallel processing to handle these analytical workloads efficiently. They also typically store historical data going back months or years, giving you the ability to analyze trends over time.

AWS Data Warehousing Solutions

AWS gives you several paths for building a data warehouse. The right choice depends on your data volume, query patterns, budget, team expertise, and how much operational overhead you want to manage. Let me walk through the main options and share my honest take on each.

Amazon Redshift

Redshift is the flagship data warehouse service on AWS, and it’s where most teams start their journey. It’s a fully managed, petabyte-scale data warehouse that uses columnar storage and massively parallel processing to deliver fast query performance on large datasets. I’ve been working with Redshift since its early days, and the service has matured dramatically.

I’ve run Redshift clusters handling terabytes of data with query times that would make our old on-premises data warehouse weep with jealousy. The columnar storage format is key — when your query only needs three columns out of a table with fifty, Redshift only reads those three columns from disk. That alone can make queries 10x faster than a traditional row-based database. Combined with automatic compression encoding and zone maps, Redshift is seriously efficient at scanning large tables.

Redshift Serverless is worth mentioning too. If you don’t want to manage cluster sizes and node types, serverless mode automatically scales compute up and down based on your workload. I’ve been recommending this for teams that are just starting out or have unpredictable query patterns. You pay per query rather than per hour, which can save real money for intermittent workloads. The flip side is that for heavy, continuous workloads, provisioned clusters are usually more cost-effective.

One thing I always tell teams new to Redshift: distribution keys and sort keys matter enormously. Getting them right can be the difference between a query taking 3 seconds and 3 minutes. Spend time understanding your query patterns before you design your table schemas.

Amazon S3 and Data Lakes

S3 isn’t a data warehouse by itself, but it’s become the foundation of the modern data lakehouse architecture. You dump your raw data into S3, process it with tools like Glue or EMR, and then either load it into Redshift or query it directly with Athena or Redshift Spectrum.

That’s what makes the data lakehouse approach endearing to us data engineers — you get the flexibility of a data lake with the performance of a structured warehouse. I’ve worked on architectures where 80% of the data lives in S3 as Parquet files and only the most frequently queried datasets are loaded into Redshift. The cost savings are massive because S3 storage is pennies per gigabyte compared to Redshift’s per-node pricing.

The trick is organizing your S3 data lake properly. Use consistent naming conventions, partition your data by date or other relevant dimensions, store everything in columnar formats like Parquet or ORC, and maintain a Glue Data Catalog so all your tools can discover and query the data. A messy data lake quickly becomes a data swamp, and nobody wants that.

AWS Glue

Glue is the ETL (Extract, Transform, Load) service that moves data between your sources and your warehouse. It handles the unglamorous but absolutely critical work of cleaning data, transforming formats, deduplicating records, and loading everything into the right place.

The Glue Data Catalog is particularly useful — it acts as a central metadata repository that both Athena and Redshift Spectrum can use. Define your table schemas once, and multiple services can query the same data. I use Glue crawlers to automatically discover new data as it lands in S3, which saves a lot of manual schema management. Crawlers inspect your data, infer the schema, and register it in the catalog. Not perfect every time, but a huge time-saver for most use cases.

Glue ETL jobs run on Apache Spark under the hood, so you get distributed processing without managing a Spark cluster. For simpler transformations, Glue Studio provides a visual interface that non-engineers can use. I’ve seen analytics teams build their own basic ETL pipelines through Studio, which freed up my engineering time for more complex work.

Amazon Athena

Athena lets you run SQL queries directly against data stored in S3 without loading it into a database first. It’s serverless, so there’s no infrastructure to manage, and you pay per query based on the amount of data scanned. At $5 per terabyte scanned, it’s remarkably affordable for ad-hoc analysis.

I use Athena heavily for ad-hoc analysis and exploratory queries. When a stakeholder comes to me with a one-off question about historical data, I’d rather fire up an Athena query than spin up a Redshift cluster or wait for an ETL job to load the data. For recurring analytical workloads with strict performance requirements, Redshift is usually the better choice, but for flexibility and speed to insight, Athena is genuinely hard to beat.

Pro tip: compress and partition your data before querying with Athena. A well-partitioned Parquet dataset can reduce your Athena costs by 90% or more compared to scanning uncompressed CSV files. I learned this the expensive way when a team member ran a query against 2TB of raw JSON logs and our Athena bill for that single query was noticeable.

Amazon Kinesis for Real-Time Data

If your data warehouse needs near-real-time data, Kinesis is how you get it there. Kinesis Data Streams captures data in real-time from sources like application logs, IoT devices, and clickstreams. Kinesis Data Firehose then delivers that data to destinations like S3, Redshift, or Elasticsearch.

I’ve used Kinesis Firehose to stream clickstream data from our web applications directly into S3, where Glue picks it up, transforms it, and loads it into Redshift. The entire pipeline from user click to queryable warehouse row takes about 5-10 minutes. For most analytical use cases, that’s plenty fast.

Best Practices I’ve Learned

After building and migrating several data warehouses on AWS, here are the practices that have served me well over the years:

• Partition your data: Whether you’re using Redshift or S3/Athena, partitioning by date or another high-cardinality column dramatically reduces query costs and improves performance. This is probably the single highest-impact optimization you can make.
• Use columnar formats: Parquet and ORC for S3 data, and let Redshift handle its own columnar storage. The compression and read efficiency gains are enormous — typically 60-90% less data scanned per query.
• Implement data quality checks: Build validation into your ETL pipelines. Catching bad data early saves hours of debugging downstream. I use Great Expectations integrated with Glue jobs for automated data quality monitoring.
• Monitor costs religiously: Redshift reserved instances save 40-60% over on-demand pricing. Athena costs drop dramatically when you use partitioned, compressed data. Set up billing alerts so surprises don’t happen.
• Design for evolution: Your data model will change. Schema-on-read approaches give you flexibility, and keeping raw data in S3 means you can always reprocess if your transformation logic changes.
• Document everything: Schema definitions, ETL job logic, data lineage, refresh schedules. Future you (and your teammates) will thank present you.

Conclusion

Building a data warehouse on AWS is a journey, not a one-time project. Start with clear requirements, pick the right combination of services, and iterate as your needs evolve. The tooling has matured significantly over the past few years, and there’s really no reason to run your own data warehouse hardware anymore. Whether you go with Redshift, a data lakehouse on S3, or some hybrid approach, AWS has the building blocks to handle it. Just make sure you invest time in the ETL layer — that’s where most data warehouse projects succeed or fail. And partition your data. Seriously. Partition your data.