A data service can be a valuable asset for organizations that utilize big data and datasets from multiple sources. Fortunately, Amazon offers cloud-based products for data management and query processing.
But while Amazon Athena and Amazon Redshift are both data warehouse tools that enable users to access and analyze their data, the products differ in their features, capabilities and functionality. We will be comparing each of these solutions so that you can determine which product would best suit your data processing needs.
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What is Amazon Athena?
Amazon Athena is a cloud-based query service for large-scale data analysis. Buyers of the product can use standard SQL to prepare and analyze their datasets or integrate with other business intelligence tools for increased functionality.
What is Amazon Redshift?
Amazon Redshift is a data warehousing tool that enables users to access and analyze their data with machine learning. The product can access and analyze both structured and semi-structured data using SQL.
Amazon Athena vs. Amazon Redshift software comparison
Data access
The Athena software can access and analyze data that is stored in Amazon S3, relational, non-relational, object and custom data sources. Amazon S3 stores important data across multiple facilities, and users can also integrate with AWS Glue to create a unified metadata repository. It can automatically crawl data services to access data and populate the data catalog, where the fully-managed ETL capabilities can then process the data and prepare it for analysis. Glue displays new and modified table and partition definitions from the discovered data within the platform console.
The Athena Data Source Connectors that run on AWS Lambda can allow users to access data from Amazon DynamoDB, Apache HBase, Amazon DocumentDB, Amazon Redshift, AWS CloudWatch, AWS CloudWatch Metrics and JDBC-compliant relational databases. With the Athena Query Federation SDK, users can build connectors to integrate with any data source. Athena supports complex data types and SerDe libraries for accessing various data formats, including Parquet, CSV, Avro, JSON and ORC.
Redshift utilizes structured and semi-structured data from Amazon S3, data warehouses, operational databases, data lakes and third-party data sets to develop actionable insights. Redshift’s streaming capabilities allow users to connect and ingest data from multiple Kinesis data streams at once with SQL. It can parse data from Apache logs, TSV, JSON and CSV formats. Users can load and transform data into the Redshift data warehouse with Data Integration Partners to access data from third-party sources.
Additionally, the system can access data from cloud-native, traditional, containerized, serverless web services-based and event-driven applications. The Amazon Redshift Data API enables database connections and data access from programming languages and platforms supported by the AWS SDK, including Java, Ruby, Go, Python, PHP, Node.js and C++. For example, Amazon Kinesis Data Firehose can load streaming data into Amazon Redshift to quickly produce near real-time analytics.
Data analysis
In addition to data log processing, Athena users can perform ad-hoc analyses of their data. The software also scales automatically, meaning that users can run interactive queries in parallel for faster processing and analyses of larger datasets.
With standard SQL to run queries, users can analyze their data directly within Amazon S3. Athena uses the Presto SQL query engine for low latency data analysis, enabling users to run queries against large datasets in Amazon S3 using ANSI SQL. Users can join data across multiple sources using SQL constructs for fast analysis and then store the results in S3. Additionally, integrations with BI products through the JDBC driver can allow users to benefit from even more external features and capabilities.
Using SQL, analysts can benefit from Redshift’s AWS-designed hardware and machine learning to gain actionable insights with high-quality performance. The Redshift system can analyze exabytes of data in Amazon S3 to run analytical queries. In addition, it can provide valuable information on data by performing ad-hoc business analysis, including anomaly detection, machine learning-based forecasting and what-if analyses.
The system also has native advanced analytic processing solutions for standard scalar data types. This includes native support for processing Spatial data, HyperLogLog sketches, DATE & TIME data types and semi-structured data. As for data analysis visualization, Redshift’s Query Editor v2 feature allows users to see their query results, load data visually, and create schemas and tables. In addition, users can integrate the product with external BI partners’ solutions to expand its analysis capabilities.
Unique functions and features
Athena does not require any infrastructure management, as the serverless product automatically handles configuration, software updates, failures and scaling. Using Athena SQL queries with SageMaker machine learning models can enable users to gain advanced insights, such as sales predictions, customer cohort analysis and anomaly detection.
Athena is secured through AWS Identity and Access Management policies, access control lists, and Amazon S3 bucket policies. This means that users can control their S3 buckets, manage access to their S3 data, restrict querying of S3 data through Athena, query encrypted data in S3 and write encrypted results back into S3. It supports server-side encryption and client-side encryption. Customers using Athena only pay for the amount of data scanned by each query. Therefore, buyers can save money by compressing, partitioning or converting their data to a columnar format, reducing the amount of data scanned to execute a query.
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Redshift has automated optimizations that deliver high performance and speed. It can process thousands of queries at once on datasets from gigabytes to petabytes. This is made possible through the system’s use of columnar storage, zone maps and data compression to reduce the amount of input and output necessary for processing queries. Redshift uses machine learning for automatic workload management of memory and concurrency for maximized query throughput.
Users have a lot of control over aspects and features, including setting the priority of queries, changing the number or type of nodes in their data warehouse and adjusting their end-to-end encryption settings. Payment for Amazon Redshift is based on the features and needs of the user. They offer different node types that accommodate the user’s data size, growth and performance required. Users can choose the best cluster configuration for their needs for pay-as-you-go pricing or use additional payment options based on their services.
Which is the best data warehouse solution for you?
When determining the best data warehouse solution for your organization, there are several factors you should consider. For example, products that require the utilization of third-party applications must be able to connect with the tools your organization uses to generate data. Therefore, ensure that you will be able to access your datasets from their respective sources within your chosen data warehouse solution.
Additionally, considering your organization’s use cases and needs can help you determine which option has the most accommodating features and capabilities. For example, if you wish to utilize your solution often to process complex queries from multiple data sources, Redshift may be a better option. However, if you intend to use your product less frequently and on smaller datasets, Athena’s software may be a more economical choice for your needs. By analyzing the characteristics and requirements of your organization, you can compare them to each product’s features and make an educated decision on the best data warehouse option.