Querying a data lake - Amazon Redshift
Demo: Query a data lakePrerequisitesCreating an external schema Querying data in your Amazon S3 data lake
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Querying a data lake

You can query data in an Amazon S3 data lake. First, you create an external schema to reference the external database in the Amazon Glue Data Catalog. Then, you can query data in the Amazon S3 data lake.

Demo: Query a data lake

To learn how to query a data lake, watch the following video.

Prerequisites

Before you work with your data lake in query editor v2, confirm the following was set up in your Amazon Redshift environment:

  • Crawl your Amazon S3 data using Amazon Glue and enable your Data Catalog for Amazon Lake Formation.

  • Create an IAM role for Amazon Redshift using the Amazon Glue enabled Data Catalog for Amazon Lake Formation. For details on this procedure, see To create an IAM role for Amazon Redshift using an Amazon Glue Data Catalog enabled for Amazon Lake Formation. For more information about using Redshift Spectrum and Lake Formation, see Using Redshift Spectrum with Amazon Lake Formation.

  • Grant SELECT permissions on the table to query in the Lake Formation database. For details on this procedure, see To grant SELECT permissions on the table to query in the Lake Formation database.

    You can verify in the Lake Formation console (https://console.amazonaws.cn/lakeformation/), Permissions section, Data lake permissions page, that the IAM role, Amazon Glue database, and tables have the proper permissions.

  • Confirm your connected user has permission to create schemas in the Amazon Redshift database and access data in your data lake. When you connect to a database in query editor v2, you choose an authentication method that includes credentials, which can be a database user or IAM user. The connected user must have the proper permissions and database privileges, such as a superuser. The Amazon Redshift admin user who created the cluster or workgroup has superuser privileges and can create schemas and manage the Redshift database. For more information about connecting to a database with query editor v2, see Connecting to an Amazon Redshift database.

Creating an external schema

To query data in an Amazon S3 data lake, first create an external schema. The external schema references the external database in the Amazon Glue Data Catalog.

  1. In the Editor view of query editor v2, choose CreateCreate, and then choose Schema.

  2. Enter a Schema name.

  3. For Schema type, choose External.

  4. Within Data Catalog details, the Region defaults to the Amazon Web Services Region where your Redshift database is located.

  5. Choose the Amazon Glue database that the external schema will map to and that contains references to the Amazon Glue tables.

  6. Choose an IAM role for Amazon Redshift that has the required permissions to query data on Amazon S3.

  7. Optionally, choose an IAM role that has permission to the Data Catalog.

  8. Choose Create schema.

    The schema appears under your database in the tree-view panel.

When creating the schema, if you receive a permission denied error for your database, check if the connected user has the database privilege to create a schema.

Querying data in your Amazon S3 data lake

You use the schema that you created in the previous procedure.

  1. In the tree-view panel, choose the schema.

  2. To view a table definition, choose a table. The table columns and data types display.

  3. To query a table, choose the table and in the context menu (right-click), choose Select table to generate a query.

  4. Run the query in the Editor.

    The following example SQL was generated by query editor v2 to query all the rows in Amazon Glue table named flightscsv. The columns and rows shown in the output are truncated for simplicity.

    SELECT * FROM "dev"."mydatalake_schema"."flightscsv";
                        
year    quarter   month   dom  day_of_week   fl_date    unique_carrier  airline_id   carrier   tail_num   fl_num		
2016    4         10      19   3             10/19/16   OO              20304        OO         N753SK    3086	 
2016    4         10      19   3             10/19/16   OO              20304        OO         N753SK    3086	
2016    4         10      19   3             10/19/16   OO              20304        OO         N778SK    3087		
2016	4         10      19   3             10/19/16   OO              20304        OO         N778SK    3087	
...