In the ever-evolving landscape of data management, AWS Athena has emerged as a powerful ally for businesses looking to harness the capabilities of serverless data querying. I recently had the pleasure of sitting down with Marco Esposito, a seasoned data analyst, to delve into his experiences and insights on using AWS Athena to streamline data operations and manage costs effectively.
Marco, who works as a senior data analyst at a mid-sized tech firm, enthusiastically shared his journey of discovering the nuances of AWS Athena. His narrative was peppered with practical advice and thoughtful strategies, making it an enlightening conversation for anyone keen on optimising their data querying processes.
Understanding the Basics
Our conversation kicked off with Marco explaining the fundamentals of AWS Athena. “Athena is fantastic because it doesn’t require you to manage any infrastructure,” he began. “You can run SQL queries directly on data stored in Amazon S3, which is a game-changer for anyone dealing with large datasets.”
The serverless nature of Athena, coupled with its use of Presto for SQL querying, gives users the flexibility to focus on data analysis rather than infrastructure management. However, Marco was quick to point out that Athena’s pay-per-query pricing model means that efficiency is key. “The cost is based on the amount of data scanned,” he noted, “so optimising how you store and query data can lead to significant savings.”
Optimising Data Storage
From here, Marco dove into the crux of his experience: optimising data storage to reduce the amount of data scanned. “Partitioning data is crucial,” he emphasised. “By partitioning data based on frequently used columns, such as date or region, you can drastically cut down the data scanned during queries.”
He shared an instance where partitioning data by the date transformed their whole approach. “We had a massive dataset, and by partitioning it by date, we only scanned a fraction of it for our daily reports. It saved us time and, importantly, cost.”
Marco also stressed the importance of using columnar storage formats like Parquet or ORC. “These formats are much more efficient for querying than traditional row-based formats like CSV. The efficiency comes from the way they store data, which reduces the amount of unnecessary data read during queries.”
Compression, Marco explained, is another powerful tool in the data analyst’s arsenal. “Compressing files using methods like gzip or snappy can significantly lower the data scanned, and subsequently, the costs.”
Managing Tables and Queries
Our discussion naturally transitioned into managing tables within Athena. Marco underscored the importance of defining external tables and using the AWS Glue Data Catalog for managing metadata. “The Data Catalog is a lifesaver,” he said with a chuckle. “It keeps everything organised and makes it easier to optimise your queries.”
When it comes to writing efficient queries, Marco’s advice was simple but impactful. “Always use SELECT statements that retrieve only the necessary columns,” he advised. “And apply filters early in your queries. It sounds basic, but these steps make a huge difference in the data scanned and the query’s cost.”
Cost Management and Advanced Techniques
Marco’s approach to cost management was methodical and forward-thinking. “Archiving cold data to lower-cost storage tiers like S3 Glacier or S3 Infrequent Access helps in managing expenses,” he noted. “And implementing lifecycle policies in S3 automates the archiving process, which is a time-saver.”
He also shared advanced techniques that have enriched his team’s data querying capabilities. “Using CTAS (Create Table As Select) statements has allowed us to create new tables with the results of complex queries, facilitating deeper data transformations,” he explained.
Marco was particularly excited about the potential of federated queries. “Athena’s ability to run queries across multiple data sources, including relational databases and other AWS services, is an area we’re exploring. It’s a powerful feature that opens up new possibilities for data analysis.”
Conclusion
As our conversation came to a close, Marco reflected on his journey with AWS Athena. “It’s a versatile tool that, when used correctly, can transform how a company approaches data analysis,” he said. “The key is to continually optimise your data storage, write efficient queries, and employ smart cost management practices.”
Marco’s insights serve as a valuable guide for any organisation looking to maximise the potential of AWS Athena. By implementing these strategies, businesses can ensure they’re not only saving on costs but also enhancing their data querying processes.
Lilianna Stolarz