AWS Athena: Nowadays, practically every organization needs a data query tool to access and manipulate vast amounts of data stored in on-premise or cloud-based locations.
A query tool is important because it eliminates the time you would have spent manually going into hundreds of different tables to find the information you need.
There are loads of query tools out there, and finding the right one can be tricky. However, if your organization makes heavy use of AWS resources, your quest for the right tool might not be as difficult as all that. Why? Because Amazon has its own query tool: Athena.
If you’re already using Amazon for data storage needs, machine learning, data analytics, or all of these things, adding Athena to that suite of data tools might make a lot of sense.
Athena is also a great option for those who want a highly flexible, scalable query tool that is easy to set up.
Interested in learning more about AWS Athena? Read on, and we’ll give you everything you need to know about this query tool, including all of its features, the product’s highs and lows, and even some tips on how to use it successfully.
6 Must-know Facts about AWS Athena
- AWS Athena is a serverless query tool managed by Amazon that gives users an easy and reliable way to pull data from S3 or other data sources.
- Athena uses standard SQL, so learning how to query with it is fairly straightforward.
- Though most will probably store their data in S3, there are 30 other possible sources from which you can query data—including both cloud-based and on-premise data warehouses.
- With Athena, there’s no need to provision data or manage updates—Athena does that all automatically.
- When used in conjunction with Amazon IAM, managing team members’ data access and other privileges is a cinch.
What is AWS Athena: Explained
AWS Athena is a proprietary Amazon tool that lets users engage in serverless, interactive analytics with petabytes of data—wherever that data happens to be. It’s a relatively easy way of querying and analyzing data or building applications for Amazon S3 or 30 other data sources.
The key advantages of Athena are scalability and flexibility. As we dive further into the details of the product, it’ll be easy for you to understand why.
Most people will probably often use Athena to submit queries to various data sources powered by Amazon S3, but it’s also possible to do so with on-premise data warehouses—or even multi-cloud environments.
Athena also allows you to prep data for use in Machine Learning models, which in turn gives users the power to predict sales, detect anomalies, automate various tasks, and so on.
Like any good query tool, Athena also makes it easy to build business intelligence and analytics applications so your teams can visualize and otherwise keep track of recent—or even minute by minute—operational trends.
Let’s go over AWS Athena’s main features in detail so that you can get a full sense of its flexibility and many use cases.
Many query tools will require active management of data infrastructure. Things like managing updates, configuring tables, implementing data warehouses, and manually scaling up and down.
But AWS Athena handles all of that on its own.
All you have to do is load your data, whether structured or unstructured, and Athena takes care of the rest.
Due to its serverless nature, even teams that lack a data engineer can leverage the benefits of Big Data. Just fire up the console and get querying. It’s that simple.
Easy and Flexible
To be successful with Athena, all you need is the ability to work with standard SQL. Expect Athena to be able to handle basically any query you’d encounter in other major tools: including joins, window functions, and arrays, to name a few.
On top of ease of use, Athena also strives to meet a variety of user preferences when it comes to working with data. For instance, Athena supports a huge spectrum of data sources, including CSV, JSON, ORC, Avro, and Parquet. It’s also compatible with other BI and SQL development applications outside of the Athena console.
When it comes to speed, availability, and durability, Athena is no slouch either. By powering Athena with Amazon’s robust S3 engine, you’re sure to get queries returned in seconds—even if querying large datasets.
Thanks to Athena’s employment of S3, you can be sure of maximal availability and durability. In large part, that’s because S3 duplicates and stores data in multiple facilities. By doing so, you’ll still be able to access data even if one facility is unavailable.
In terms of durability, S3 provides 99.999999999% object durability. Also known among data scientists as “11 9s” of durability, that means it is damn near impossible you will ever experience data loss while using Athena.
AWS Athena also makes it easy to ensure your organization’s data is protected from unwanted interference. It works with Amazon Identity and Access Management (IAM) to set fine-grained policies around who can access data and under what parameters. It also supports both server-side and client-side encryption so as to prevent exposure to malicious actors.
How to Use AWS Athena
Like all AWS tools, having an Amazon account is a must before you can use Athena. You also need to be sure to set up a regionally appropriate S3 bucket to hold your query results.
Once you meet these prerequisites, you’re ready to start exploring the query editor and changing settings as you like—or just skip straight to creating your first database!
After creating a database, you then need to set up some tables so that you can actually query your data. Once you organize your data into sensible tables with columns and rows, you can query to your heart’s content.
Just keep in mind that unless you’re on a fixed-rate plan, Amazon charges per query.
How to Learn AWS Athena
As always with AWS resources, Amazon’s documentation ought to be considered your Bible. If you’re already quite AWS-savvy, the documentation is probably all you need. Athena’s user guide contains tons of useful explanations and best practices for getting started with Athena, creating tables, running queries, setting up security, and so on.
If the documentation is too much reading for you, you can also check out this 30-minute intro course offered by Cloud Academy. It’s a good primer for new users, though you will probably need to consult the documentation at a certain point—at the very least to troubleshoot any issues you run into.
Here’s the bottom line: don’t let yourself get lost in all the possible avenues to explore. Define what your individual or organizational goals are for using Athena, and what your role is going to be within Athena.
Are you an analyst or a data scientist? The former will need to be more concerned with learning the querying, whereas the latter will need to understand more about provisioning, creating S3 buckets, databases, and tables.
AWS Athena: When is it Not The Best Choice?
What do people like most about Athena? It’s simple, easy to use, and flexible. Just launch the tool, and start querying. It’s basically that easy. And thanks to its pay-as-you go pricing, you don’t have to worry about long-term commitments if you decide the tool is not right for you.
This is good news, because Athena is not always the right fit for your needs. Don’t be discouraged—there’s plenty to love about Athena. But we have to be realistic and understand that it’s not going to be the perfect query tool for everyone.
The main challenge appears to be that because Athena is easy to use, easy to launch, and serverless, it also lacks some fine-grained functionality. Ironically, because it’s designed to be hassle-free, managing Athena within your organization’s data infrastructure turns out to be tricky if your needs are complex. Ditto if your querying needs are complex.
Plus, because data provisioning is meant to be automated, Athena does sometimes throw up bugs that’ll slow down the return speed of your queries.
If any of these sound like deal-breaking disadvantages to you, perhaps you should consider the below alternatives.
MongoDB is a widely popular data management software. Not only does it provide an easy way to query data, but it also handles the entire suite of data infrastructure that your enterprise could ever need. It’s also a very flexible tool to use, letting you integrate other data tools within MongoDB as needed.
The user interface is, frankly, probably more streamlined than that of Amazon Athena. Even so, if you ever run into issues using MongoDB’s tools, you can rely on a robust customer support team to lend you a hand.
Oracle is an equally competitive option, here. Large-scale enterprises are likely to especially appreciate Oracle’s reputation for transparency when it comes to working out fixed-rate contracts.
Because honestly, the biggest companies out there will probably find Athena’s querying rates quite expensive, and they might be able to work out a better deal with this competitor.
But Oracle does more than just deliver on pricing. It’s easy to integrate with your other tools, and easy to get up and running. Oracle also has robust security features and backup measures in place, so you’re likely to never experience any data loss or security breaches.
AWS Athena: Release History
Amazon introduced Athena in November 2016 as a fully managed querying service. It entered a market crowded with plenty of other querying tools, but Amazon seemed to hope that people who were already using Amazon for other data management needs would be grateful to be able to leverage the company’s proprietary querying tool as well.
Since Athena’s initial release, Amazon has added a number of other features and improvements to keep Athena competitive vis-à-vis other querying services out there. In 2017, Amazon added greater complexity to Athena’s data capabilities and a table creation wizard to help users start using the software quickly.
Over the next few years, Amazon also added support for both nested and federated querying, data encryption, and query monitoring. As the big data ecosystem has continued to expand, Amazon has also striven to build upon Athena’s diverse number of integrations.