You can also download and use the Athena JDBC driver and run queries from your favorite Business Intelligence tool.Įach Athena table can be comprised of one or more S3 objects each Athena database can contain one or more tables. You can run your queries from the AWS Management Console or from a SQL clients such as SQL Workbench, and you can use Amazon QuickSight to visualize your data. Athena is based on the Presto distributed SQL engine and can query data in many different formats including JSON, CSV, log files, text with custom delimiters, Apache Parquet, and Apache ORC. Your queries are expressed in standard ANSI SQL and can use JOINs, window functions, and other advanced features. Behind the scenes, Athena parallelizes your query, spreads it out across hundreds or thousands of cores, and delivers results in seconds.Īthena includes an interactive query editor to help get you going as quickly as possible. You don’t have to build, manage, or tune a cluster or any other infrastructure, and you pay only for the queries that you run. You simply point Athena at some data stored in Amazon Simple Storage Service (Amazon S3), identify your fields, run your queries, and get results in seconds. Today I would like to tell you about Amazon Athena.Īthena is a new serverless query service that makes it easy to analyze large amounts of data stored in Amazon S3 using Standard SQL. Instead, they simply want to point-and-shoot: identify the data, run queries that are often ad hoc and exploratory in nature, get the results, and act on the results, all in a matter of minutes. They want to do this at high speed and they don’t want to spend a whole lot of time preprocessing, scanning, loading, or indexing data. These days, many people routinely process and query data in structured or semi-structured files at petabyte scale. When querying data from the second sync onward, there will be duplicate entries.The amount of data that we all have to deal with grows every day (I still keep a floppy disk or two around in order to remind myself that 1.44 MB once seemed like a lot of storage). The S3 destination plugin only supports the "append" write mode, which means it will keep adding new files and never remove the old ones. One thing we haven't covered here is how data will be updated the next time you run a sync. This allows you to use the power of Athena to query your infrastructure data, and use the results to inform your security and compliance decisions. In this tutorial, we showed how to use CloudQuery to load infrastructure data into an S3 bucket, and then use Glue Crawler and Athena to query the data. For more information about ways to address this issue, see this GitHub comment (opens in a new tab). AWS tags are case sensitive, so this can be a common cause. If you see an error like Row is not a valid JSON Object - JSONException: Duplicate key, this means some of the data in a JSON column contained keys where only the case differed (e.g. Let's start by exporting the name of the bucket we'll be using in the rest of this guide as an environment variable:Īthena is case-insensitive. We'll use the AWS CLI (opens in a new tab) to do this in this tutorial, but you can also use the AWS web console or Terraform/CloudFormation if you prefer. We will need to upload the sync results to S3 so that Athena can query them. Let's get started! Step 1: Create a Bucket for the Data To accomplish this we will load data into S3 using CloudQuery, set up a Glue Crawler to automatically create the database and tables in a Glue Catalog, and then query those tables using Athena. This allows you to get fine-grained insights into your infrastructure data, all from the convenience of a serverless query environment running on AWS.īy the end of this post, you will be able to query your AWS infrastructure data using Athena: In this tutorial, we will show you how to load your cloud infrastructure data into S3 using CloudQuery and query it using Athena. Name Herman Schaaf Twitter hermanschaaf IntroductionĪthena is a serverless query service by AWS that allows you to query data in S3 using standard SQL.
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