Athena helps you analyze unstructured, semi-structured, and structured data stored in Amazon S3. Examples include CSV, JSON, or columnar data formats such as Apache Parquet and Apache ORC. You can use Athena to run ad-hoc queries using ANSI SQL, without the need to aggregate or load the data into Athena.
Quicksight - Athena integrates with Amazon QuickSight for easy data visualization. You can use Athena to generate reports or to explore data with business intelligence tools or SQL clients connected with a JDBC or an ODBC driver.
Glue - Athena integrates with the AWS Glue Data Catalog, which offers a persistent metadata store for your data in Amazon S3. This allows you to create tables and query data in Athena based on a central metadata store available throughout your AWS account and integrated with the ETL and data discovery features of AWS Glue.
Additional Integration Information - https://docs.aws.amazon.com/athena/latest/ug/athena-aws-service-integrations.html
Connecting using JDBC Tools You can use a JDBC connection to connect Athena to business intelligence tools and other applications, such as SQL Workbench. https://docs.aws.amazon.com/athena/latest/ug/connect-with-jdbc.html
Using ODBC Drivers - https://docs.aws.amazon.com/athena/latest/ug/connect-with-odbc.html
| Function | Description | Example | More Details |
|---|---|---|---|
| regexp_replace | Searches a string for a regular expression pattern and replaces every occurrence of the pattern with the specified string. REGEXP_REPLACE ( source_string, pattern [, replace_string [ , position ] ] ) | “regexp_replace”(“c”.“address1”, '[0-9A-Za-z]','') | https://docs.aws.amazon.com/redshift/latest/dg/REGEXP_REPLACE.html |
For dates you will have to truncate the field to get the portion of the date you want.