![]() S3 bucket “aws-simplified-athena-demo” contains source data I want to query. I have two buckets created in S3, one contains raw source data to read, and another is to store the results of SQL queries executed in AWS Athena. ![]() How to read data from Amazon S3 using AWS Athenaīefore we go to how-to steps, let’s first look at the sample data in the S3 bucket we want to read… If you prefer to learn through video, check out my YouTube video on this guide here. In this article, I will show you how to read the data stored in Amazon S3 bucket by connecting it to AWS Athena and executing some SQL queries. Amount of data scanned by the query is displayed after each execution in the console. The cost is determined by the amount of data scanned by each query executed. It follows a pay-as-you-go pricing model where you pay for the queries you run. You can also use complex joins, window functions and complex data types on Athena. You can write Hive-compliant DDL statements and ANSI SQL statements in the Athena query editor. It uses Apache Hive to create, drop, and alter tables and partitions. This eliminates the need for any data loading or ETL. It uses an approach known as schema-on-read, which allows you to project your schema onto your data at the time you execute a query. Sign-up for our 30 day free trial or sign-in to your Skeddly account to get started.Wondering what is AWS Athena and how it works? Let me give you a quick introduction…ĪWS Athena is a serverless query service which can be used to read and analyze large amounts of data directly from S3 using Standard SQL. Skeddly is the leading scheduling service for your AWS account. Automatic Partitioning With Amazon Athena.Partitioning Your Data With Amazon Athena.Using Compressed JSON Data With Amazon Athena.Getting Started with Amazon Athena, JSON Edition.We will take a look at both of these later on. There are a few techniques that you can use to reduce the amount of data scanned: Additional ThoughtsĪmazon Athena is priced based on the amount of data scanned. You can click the query to load it into the query editor (where you can edit it and/or re-execute it). You can access the query history in the Athena Management Console from the “History” tab. In many cases that bucket is called aws-athena-query-results. Query HistoryĪll Amazon Athena queries are recorded and the results are placed in a new S3 bucket. So you will want to pay attention to the “Data scanned” value. Notice the “Run time” and “Data scanned” statistics are reported for the query.Īmazon Athena is priced based on the amount of data scanned. This is done by issuing standard SQL SELECT statements. Now that our table is created, we can start executing queries on the data. Once we select the new database, our table appears in the list of tables. The new database has been created and appears in the “Database” dropdown field. You’ll notice that the query editor has been populated with a CREATE EXTERNAL TABLE query. That extra data will simply be ignored by Athena.įor step 4, we did not setup any partitions.Īfter clicking “Create table”, we are sent back to the Query Editor. The JSON data can include more data than we create columns for. Notice that the name of the columns matches the names of the properties in the JSON data. ![]() You can think of it as a folder or directory that the input data will be stored in.įor step 2, we selected “JSON” for the data format since our data is in JSON format. Notice the trailing slash at the end of the location. The location is a combination of bucket name and prefix. Previously, we created an S3 bucket called “athena-testing-1”, so under “Location of Input Data Set”, we specified s3://athena-testing-1/Test1/. So, in our Athena Management Console, we went to the “Catalog Manager” and clicked the “Add Table” button.įor step 1, we called our database “TestDb” and the table “Table1”. Creating the database is done in conjunction with creating the first table. Databases in Athena aren’t real databases: they don’t store anything aside from table schemas. The first thing we need to do to start querying data using Amazon Athena is to create our database and table. Note: Athena is capable of querying nested JSON data, but we are not doing that in this example. ![]() We placed the JSON files in our S3 bucket in a flat list of objects without any hierarchy: ![]()
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