Why You Should Ditch Redshift for Athena When Querying Cold Data on S3

Cold data…you know, that old batch of records or files chilling in your Amazon S3 bucket like that pair of jeans you swore you’d fit back into. It’s there, it’s valuable, but you don’t touch it very often. Now and then, though, you have to pull something out from that forgotten treasure trove and run some analytics. When that happens, you have two options: fire up a massive Redshift cluster, which is like dragging out a bulldozer to plant flowers, or take the smarter approach—AWS Athena.

If you’re not using Athena for your cold data queries yet, let’s talk about why you might want to change that ASAP.

Athena: The Data Query Ninja (No Clusters, No Hassle)

First off, what even is Athena? Think of it as the quiet genius in the room. Athena doesn’t demand attention or constant maintenance. It’s serverless, so it doesn’t sit there burning a hole in your wallet while idle. Instead, it waits patiently in the shadows until you need it, then swoops in to query your data directly from S3. You don’t need to provision any hardware or worry about scaling—it just works when you ask it to.

Unlike Redshift, which requires clusters, nodes, and a fair bit of babysitting, Athena just taps into your S3 data like it’s been sitting there waiting to be useful. And let’s be real—S3 is dirt cheap for storage, so if your data is cold and only used occasionally, this is the perfect setup.

Now, the best part? You only pay when you use it. No constant costs. No provisioning nightmares. It’s like that pay-as-you-go phone plan you wished you had in high school instead of that monster family plan.

Why Redshift Might Be Overkill for Cold Data

Redshift is cool, don’t get me wrong. It’s powerful, and if you’re doing real-time, heavy-hitting analytics with a ton of concurrent queries, it might even be the better tool for the job. But if we’re talking cold data—data you barely touch except for the occasional “hey, what were sales numbers in Q1 2017” or “how did we perform in that random market?”—then Redshift can be total overkill.

Here’s why Athena is the perfect underdog for cold data queries:

1. Cost Efficiency—Athena Saves You $$$

Redshift requires clusters, and clusters ain’t cheap. You’re paying for nodes, storage, and compute power whether you’re using it or not. Cold data doesn’t need that. Why pay for an engine when you only need a bicycle to get you where you’re going?

With Athena, you’re only charged for the data you query, which means if you run one query per month, you pay for one query per month. Imagine saving hundreds (if not thousands) in Redshift maintenance costs just by switching to Athena. For most businesses that don’t need constant querying, that’s a game-changer.

2. Skip the Infrastructure Drama

No clusters. No nodes. No scaling issues. No waking up at 2 a.m. because your Redshift cluster decided to crash right before a big presentation. Athena is serverless, which means Amazon takes care of the heavy lifting while you sleep soundly. Need more compute power? Athena scales up without you lifting a finger. Done querying? Athena goes back to sleep, and you don’t pay for idle time.

3. Easy Integration with S3

This is where Athena shines—S3 is its best friend. Your cold data is likely already in S3 (because who’s paying premium storage for cold data?), and Athena reads that data right where it sits. No data movement, no redundancy, no wasting time copying to another service. Athena reads directly from S3, performs your query, and sends back the result without any drama.

4. Athena’s Pay-Per-Query Model

Here’s a fun fact: You pay based on the amount of data Athena scans. You don’t have to worry about upfront costs, monthly fees, or paying for hardware you’re not using. Every time you write a query, Athena tells you how much data it scanned and charges accordingly. Plus, if you optimize your queries (we’ll get into that), you can keep your costs crazy low.

5. No Need to Maintain Data Warehousing

Here’s a dirty secret: Maintaining a Redshift cluster for cold data is like paying for a storage unit filled with things you’ll never touch. With Athena, you leave your cold data in S3, query it whenever needed, and then walk away. No additional data warehousing, no cluster management—just a clean, simple approach to analytics.

How to Set Up Athena with S3 (Step-by-Step Without Tearing Your Hair Out)

The setup process for Athena is so simple, you’ll kick yourself for not doing it sooner. Here’s the quick version:

1. Data in S3: First things first—make sure your cold data is already in Amazon S3. If it’s not, upload it there. Simple.
2. Partition Your Data: If your data is large, you’ll want to partition it by attributes like date or region. This keeps your query efficient and makes sure Athena isn’t scanning your entire dataset, which could run up costs. Partitioning allows Athena to only read the relevant chunks of data.
3. Create a Table in Athena: Head to the AWS Athena console. You’ll need to define a schema for your data, which is basically telling Athena how to interpret the data structure in S3 (think columns and data types). AWS Glue can help you automate this process if your data is complex or massive.
4. Run Your Queries: Now the fun begins. You can write standard SQL queries in Athena. It’s straightforward and feels just like querying any other relational database.

Here’s a bonus: Athena supports CSV, JSON, Parquet, and ORC. So whatever format your cold data is in, Athena’s got you covered.

Examples of When to Use Athena

Still not convinced? Let’s throw in some real-world examples to show where Athena kills it for cold data analytics:

1. Data Archiving: You have terabytes of archived sales data in S3, and once every fiscal year, someone asks for a report. Spinning up Redshift for that annual query feels like using a cannon to kill a fly. Athena can query it directly in S3 for just pennies.
2. Compliance: Keeping financial or transaction logs for audit purposes? No need to maintain an active Redshift cluster just in case someone requests access. Athena can perform those compliance queries on demand without keeping a massive infrastructure alive for months or years.
3. Marketing Campaign Data: You run a marketing campaign every few years, and now it’s time to compare current results to campaigns from 2015. That data’s been sitting in S3, but it’s cold. Athena can give you a year-by-year comparison without spinning up Redshift.

Pitfalls to Watch Out For (Yes, They Exist)

Before you think Athena’s your data savior, there are some common pitfalls to avoid:

1. Beware of Unpartitioned Data

Partitioning is your best friend. Without it, Athena scans your entire dataset, and your bill will go through the roof. You’ll want to organize your cold data into manageable sections—by date, region, or other relevant columns.

2. File Size Matters

Athena doesn’t love dealing with a ton of small files. S3 might have those in abundance, especially if data was stored via real-time streams. Consider merging small files into larger ones using AWS Glue or EMR. This’ll make your queries faster and cheaper since Athena won’t have to scan hundreds of files to get what it needs.

3. Watch Out for Schema Changes

If your dataset schema changes over time (e.g., adding new columns or changing data types), Athena might not handle it smoothly. This can result in incomplete or inaccurate query results. Stay vigilant about how your data is structured over time.

4. Query Cost Surprises

Just because Athena is “pay-per-query” doesn’t mean it can’t rack up costs. Be mindful of how much data is being scanned by your queries. Use filters and projections to limit how much data Athena has to scan. For instance, avoid SELECT * when you only need a few columns.

Real Life Scenario: How Much You Can Save

Let’s paint a picture: Your company has 10 TB of cold data stored in S3, and you run about 5 queries a month on that data. With Redshift, you’re looking at costs for storage and compute, even when you’re not actively querying. Depending on the size of your cluster, this could be anywhere from $2,000 to $4,000 per month just for keeping Redshift alive.

With Athena, you’re charged based on the amount of data scanned per query. Assuming each query scans around 200 GB of data, at $5 per TB scanned, you’re paying $1 per query. That’s $5 per month versus thousands. Do the math. Athena wins, hands down.

When Redshift Might Still Be Better

I’m not saying Redshift should be tossed aside like yesterday’s leftovers. There are situations where Redshift is the better tool for the job:

• Complex Analytics: If you’re doing high-frequency, concurrent queries or need real-time data analysis, Redshift is built for that. Athena isn’t optimized for situations where you need to constantly run queries every few seconds.
• Heavy Data Transformation: If your job requires heavy joins, aggregations, and transformations across multiple large tables, Redshift handles these better at scale. Athena works well for simpler SQL queries, but when things get complex, Redshift’s optimized infrastructure performs better.
• Data Warehousing: If you’re managing a data warehouse that needs to be accessible for hundreds of users or applications, Redshift’s architecture is built for that. Athena is more for ad hoc queries and quick insights.

Wrapping It Up: Athena for the Win

If you’re running infrequent queries on cold data stored in S3, then Athena is the no-brainer solution. It’s cheap, scalable, and keeps things simple without the need for clusters, nodes, or constant babysitting. By paying only for what you use, you keep costs low, especially when dealing with data that just sits around waiting for the rare report or compliance check.

So go ahead and query your cold data with Athena. Your wallet—and your future self—will thank you for not overpaying on Redshift when you didn’t need to. Happy querying! 😊