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AWS Analytics

Query S3 Data Using Amazon Athena

Updated on Oct 01, 2021

Amazon Athena is serverless interactive query service that makes it easy to analyze large-scale datasets in Amazon S3 using standard SQL. Athena is easy to use. Simply point to your data in Amazon S3, define the schema, and start querying using standard SQL.

First of all let's create a Workgoup. It is used to separate users, teams, applications, workloads, and also to set limits on amount of data for each query or the entire workgroup process.

Go to the Workgroups page & Click on Create Workgroup.

Athena Workgroup<br>
Athena Workgroup

Provide a name & description for your workgroup.

  • Workgroup name: bigdata-wg
  • Description: Bigdata Analysis TPCH Workgroup
Workgroup Details<br>
Workgroup Details

Next Click on Browse S3 button, to choose your S3 Bucket to store the Athena Query Results. Optionally you may choose to Encrypt the query results.

Select Query engine version. You may choose Automatic.

Workgroup Query Engine<br>
Workgroup Query Engine

Optionally on the Settings section, you may choose to select the various settings like Publish query metrics to AWS Cloudwatch etc.

Optionally on the Per query data usage control section, you may set Data limits to restrict the maximum amount of data a query is allowed to scan for the workgroup.

Workgroup Usage Control<br>
Workgroup Usage Control

Optionally on the Workgroup data usage alerts section, you may set CloudWatch alarms to alert thresholds, when queries running in this workgroup scan a specified amount of data within a specific period.

Optionally set your preferred key/value pair Tags.Finally click the Create workgroup button.

Now on the Workgroups listing page, Select your Workgroup. Next click on the Actions button & select Switch workgroup.

Switch Workgroup
Switch Workgroup

Now our bigdata-wg is the Current Workgroup.

Current Workgroup<br>
Current Workgroup

Next go to the Query editor page.

First of all let us create a database.

Athena Database
Athena Database
-- Database

Next we will create an external table based on pipe delimited data files stored in S3 bucket.

Region External Table
Region External Table
-- Region Table
  r_regionkey INT,
  r_name CHAR(25),
  r_comment VARCHAR(152)
LOCATION 's3://bigdata-gen/bigdata/region/';

Let's query the number of records in the external table.

Query External Table Record Count
Query External Table Record Count
-- Region Table Record Count
SELECT count(*) count FROM region;

Finally, lets preview the data in the external table.

Preview External Table Data
Preview External Table Data
-- Region Table Preview
SELECT * FROM region LIMIT 10;

Similarly, we will create few more tables and analyze the data stored in S3 buckets with our favourite SQL based queries.

Sales Line items Table
Sales Line items Table
Sales Line items Table Record Count
Sales Line items Table Record Count
Sales Line items Table Data Preview<br>
Sales Line items Table Data Preview

Try to further analyze the data with various complex SQL queries using Joins, Group By etc on our external tables.

Complex SQL Query Analysis<br>
Complex SQL Query Analysis

Also take note on the Run time & the Data scanned.

You may download the sample DDL & DQL from here to try at your end.

Athena has integration with AWS Glue Data Catalog, which offers a persistent metadata store for our data stored in Amazon S3. This allows us to create tables and query data in Athena based on a central metadata store.

Data Catalog
Data Catalog

Within the Data catalog are the Glue Metadata Tables.

Glue Database Tables
Glue Database Tables

The table schema definition in Glue.

Glue Table Schema<br>
Glue Table Schema