AWS Athena

In this lesson, we will go through the functionalities of Amazon Athena, when to use it, and its benefits.

Core functionalities

The core functionalities offered by Amazon Athena are given as follows:

• Serverless architecture: Unlike traditional data warehouses that require server setup and management, Athena operates as a serverless service. We simply submit the queries, and Athena handles the underlying infrastructure for processing.

• Apache Spark support: Amazon Athena supports the open-source distributed processing system Apache Spark for running fast analytics workloads. Data analysts and engineers can use the Jupyter Notebook in Athena to perform data processing and programmatically interact with Spark. When we run Apache Spark applications on Athena, we submit Spark code for processing and receive the results directly. The simplified notebook experience in the Amazon Athena console allows us to develop Apache Spark applications using Python. This integration provides a seamless way to leverage the power of Spark within the Athena environment.

• Standard SQL support: Athena empowers us to utilize ANSI SQL, a widely used and recognized language for data querying. This makes it easy for data analysts and scientists already familiar with SQL to get started with Athena and analyze data promptly.

• Variety of data formats: Athena isn’t picky about the data format. It can seamlessly query data stored in commonly used data lake formats, including CSV, JSON, ORC, Parquet, and Avro. This flexibility eliminates the need for data pre-processing before querying.

• Cost-effectiveness: Athena follows a per-query pricing model where we are charged for the number of bytes scanned per query. Therefore, it is a cost-efficient solution for ad-hoc analysis, data exploration, or querying data stored in S3 data lakes.

Service integrations

Amazon Athena can be conveniently integrated with several Amazon services. However, the trademark use case is with S3 buckets.

Since Athena is serverless and compatible with multiple formats, it is ideal for performing ad-hoc SQL queries on data stored in S3. It is commonly used for quick data exploration, troubleshooting (e.g., analyzing web logs), or any scenario where we need to analyze S3 data using interactive SQL queries without managing servers.

It can also integrate with other AWS resources:

• Amazon QuickSight: Generate data visualizations from Athena query results for easy data exploration.

• Business intelligence tools & SQL clients: Integrate Athena with existing BI tools or SQL clients using JDBC/ODBC drivers for broader data analysis capabilities.

• AWS Glue Data Catalog: Leverage Glue’s metadata store for data in the S3 bucket to define tables and manage data in Athena. This metadata is accessible across the AWS account and integrates with AWS Glue’s ETL (Extract, Transform, Load) and data discovery features.

Performance optimization with Athena

While executing queries on an S3 bucket, we can face latency or failures due to multiple queries executing simultaneously. We can use S3 throttling at the service level to limit the rate of queries executing simultaneously. Below are some ways we can store our data in S3 to optimize the performance of Athena.

• Columnar data: It decreases the read time of the query. Apache Parquet and Apache ORC are the most commonly used columnar data stores. We can use AWS Glue to convert to these formats.

• Compressed data: Compressing data stored in Amazon S3 reduces the data transferred over the network when querying with Athena.

• Partition datasets: By partitioningPartitioning is a technique used in data storage and processing to organize data into smaller, more manageable subsets based on specific criteria. data, we limit the amount of data accessed at any given time to improve the speed of the query and avoid S3 throttling.

Benefits of AWS Athena

Amazon Athena provides the following benefits to the users:

• Simplified data analysis: Athena streamlines data analysis by removing server management complexities. We can focus on writing queries and extracting insights from the data.

• Faster time to insights: With its serverless architecture and ease of use, Athena allows us to get started with data analysis quickly. No need for a lengthy infrastructure setup or data manipulation before querying.

• Cost optimization: The pay-per-query model ensures we only pay for the queries we run, making Athena a cost-effective solution for exploratory analysis or occasional queries.

• Familiar interface: Using standard SQL makes Athena accessible to a wider range of users, including data analysts and scientists already comfortable with SQL.