This article discusses AWS Athena, an Amazon analytics service that focuses on retrieving static data stored in S3 buckets using conventional SQL expressions. Because it is serverless and there is no infrastructure to operate, it is a robust solution that can help clients quickly acquire insights into their data stored on S3.
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What is AWS Athena?
Amazon Athena is an interactive query service that makes it possible to use normal SQL to evaluate data in Amazon S3. You can also point your AWS Athena to any other database and query it with standard SQL.
In the area of cloud computing, AWS is regarded as a leader. Amazon provides nearly a hundred services, all of which provide competitive performance and cost-effective solutions for running workloads when compared to on-premise infrastructures.
Amazon provides a vast range of services, including computation, storage, databases, analytics, IoT, security, and much more. The Data Analytics domain is also one of these services. This enables customers to create architectures that answer critical questions about their business decisions.
AWS Athena is creating ripples in the data analytics field ever since its inception. The main highlight of Amazon Athena is that it is serverless meaning you don’t have to worry about setting up or managing any infrastructure and has an auto-scaling feature meaning it can deal with complex queries and large datasets.
You can also count on it to execute parallel queries and quickly generate results. Because of this architecture, Amazon can charge Athena users only for the queries they run, making the service a cost-effective choice for enterprises using Amazon S3.
Some of the other services you can use with Amazon Athena are Amazon S3, Amazon Lambda, Amazon Glue, and Amazon SageMaker.
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AWS Athena Pricing
You will get charged only when you run a query on a directed database. The amount charged directly depends on the complexity of the query and the amount of data scanned by that query.
Compressing, partitioning, or transforming your data to a columnar format can save you money and improve speed since each of these procedures minimizes the amount of data that Athena must scan to run a query.
The quantity of bytes scanned by Amazon Athena is rounded up to the nearest megabyte, with a 10MB minimum price per inquiry. Data Definition Language (DDL) instructions such as CREATE/ALTER/DROP TABLE, statements for managing partitions, and failed queries are all free.
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AWS Athena Pricing Example
Consider a table with three columns of equal size saved on Amazon S3 as an uncompressed text file with a total size of 3 TB. Because text formats cannot be divided, running a query to extract data from a single column of the table requires Amazon Athena to scan the entire file.
This query would cost: $15. (Price for 3 TB scanned is 3 * $5/TB = $15)
For more in-depth details about pricing, visit the AWS website.
AWS Athena vs. AWS Glue
In AWS data services, distinguishing between Athena and Glue is crucial. While Athena enables SQL-based querying directly on S3, Glue serves as an extensive ETL solution. Let’s examine the differences between AWS Athena and AWS Glue to make informed choices.
| Aspects | AWS Athena | AWS Glue |
| Use Case | Querying data directly in Amazon S3 using SQL. | Extracting, transforming, and loading (ETL) data. |
| Primary Function | Query execution on data stored in S3. | ETL service with data catalog and job coordination. |
| Programming Language | SQL | Python or Scala scripts for ETL transformations. |
| Serverless Model | Fully serverless. No infrastructure management. | Serverless, but ETL jobs can require resources. |
| Schema Discovery | Relies on AWS Glue for schema discovery. | Provides automated schema inference. |
| Concurrency | Handles multiple concurrent queries. | Supports parallel execution of ETL jobs. |
AWS Athena vs other services
In this section, we will talk about the services that are similar yet very different to Amazon Athena and also about its competition.
AWS Athena vs AWS Redshift
Amazon RedShift is an Aws data warehouse service that allows users to analyze data using normal SQL-based clients and business intelligence (BI) tools. Redshift caters to a distinct set of requirements than Athena. Redshift is better suited for enterprises that need to aggregate data from multiple sources into a similar format and can execute more complicated multipart SQL queries.
AWS Athena vs AWS Elastic MapReduce (EMR)
Distributed data processing frameworks such as Apache Hadoop, Apache Spark, and the Presto SQL query engine can all be used with Amazon EMR. Custom code, particular cluster setups, or exceptionally huge data volumes are best suited for EMR.
Athena, on the other hand, can query data processed by EMR without interfering with existing EMR processes. EMR is used for machine learning, data warehousing, and financial analysis, for example.
AWS Athena vs Microsoft SQL server
SQL Server is a relational database management system that can be used for transaction processing, business intelligence, and analytics. It is utilized in sectors like e-commerce and data warehousing for database management and analysis.
Athena and SQL Server are both tools in the same category. Although SQL Server works effectively with Windows-based applications, other choices may be more suitable for use in non-Windows contexts.
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Amazon Athena Use Case
Here we will discuss a particular use case of AWS Athena and see how integrations will help better the performance.
As you can see in the diagram above, it depicts a simple data pipeline in which data is retrieved and put into S3 buckets from a variety of sources. These are unprocessed data, which implies they haven’t been transformed yet. You can now connect to this data in S3 using Amazon Athena and begin analyzing them.
You don’t need to set up any databases or external tools to query the raw data, therefore, it’s a really straightforward approach. After you’ve completed your research and obtained your desired results, you may use an EMR cluster to do complex analytical data transformations, clean and process the raw data, and then return it to S3.
You can utilize Amazon Athena to query your processed data for further analysis at this point. It’s worth noting that Amazon QuickSight can connect straight to Athena and create spectacular images of your data stored on S3. Alternatively, you can migrate your data to Redshift, an MPP Data warehouse for quick data analysis, and then use QuickSight to view your data from Redshift.
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AWS Athena Benefits
These are some of the benefits that users can utilize when using Amazon Athena:
Serverless
AWS Athena spares you all the hassle of infrastructure administration because it’s distributed as a fully managed serverless service. You won’t have to bother about clustering, capacity management, or data loading.
Cost-Effective
AWS Athena is not only cost-effective, but it is also significantly less expensive than its competitors. The service does not charge you for compute instances. Rather, you just pay for the queries you execute.
Widely accessible
AWS Athena is broadly accessible to everybody – not just developers and engineers as it conducts its queries using normal SQL. Standard SQL queries are easy and straightforward, so even business analysts and other data specialists can use them.
Flexibility
The open and versatile architecture of Amazon Athena guarantees you’re not tied to a single provider, technology, or tool. For example, you may work with a variety of open-source file formats and swap between query engines without having to change the schema.
Learn more about AWS Athena from our blog on AWS Tutorial!
AWS Athena Limitations
These are some of the challenges that users face while using Azure Athena.
Running complex and resource-intensive queries might impact performance. Athena processes queries on the data stored in Amazon S3, and while it’s designed for scalability, extensive or poorly optimized queries can lead to longer execution times.
Data Transfer Costs
When dealing with large datasets, be mindful of data transfer costs. Athena processes data stored in S3, and costs may be incurred based on the volume of data scanned during query execution.
Query Complexity
Extremely intricate queries may face limitations due to the underlying architecture. It’s advisable to break down complex queries into smaller, more manageable ones for optimal performance.
Concurrency Considerations
Although Athena can handle multiple concurrent queries, there are practical limits. Intensive workloads with a high degree of concurrency may experience performance degradation.
Conclusion
We have seen that AWS Athena provides the ability to use standard SQL statements on data stored in S3 buckets. It is a cost-effective solution when compared to its competitors as it charges only when you use a query on the database. There are several benefits to use it which are mentioned in the blog. It has many integration options with other AWS services like S3, Redshift, EMR, Quicksight, etc., so this is a no-brainer choice if you are already using AWS services for your workload.
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