AWS S3 External

Guide to integrate your S3 data into external table in Athena or Redshift Spectrum

S3 External is a datasource connection, which creates an external table in Athena or Redshift Spectrum, automatically by inferring the schema of the data. The data is not loaded into the warehouse, instead data is read from the source location itself when queries are run on the data warehouse.

Datasource Concepts

Before setting up the datasource, learn about datasource concepts here

Step by Step Guide

STEP-1: Configure Connection

To learn about Connection, refer here

• Log into Sprinkle application

• Navigate to Datasources -> Connections Tab -> New Connection ->

• Select S3 External

• Provide all the mandatory details

  • Name: Name to identify this connection

  • Access Key: Account -> My security credentials -> Access keys -> Create new access key -> Download key file -> Show access key. To know more, click here

  • Secret key: Account -> My security credentials -> Access keys -> Create new access key -> Download key file -> Show secret key. To know more: click here

  • Region: Region should be where the storage bucket was created, for example ap-south-1

  • Bucket Name

• Test Connection

• Create

STEP-2: Configure datasource

To learn about datasource, refer here

• Navigate to Datasources -> Datasources Tab -> Add ->

• Select S3 External

• Provide the name -> Create

• Connection Tab:

  • From the drop-down, select the name of connection created in STEP-2

  • Update

STEP-3: Create Dataset

Datasets Tab: To learn about Dataset, refer here. Add Dataset for each folder that you want to replicate, providing following details

• File Type: Select the File Format

  • JSON

  • CSV

    • Select Delimiter - Comma, Tab, Pipe, Dash, Other Character

  • Parquet

  • ORC

• Compression Type (Required): Select from none, bzip2, gzip, snappy

• Directory Path (Required) :Provide the full path like this: s3a://test-sprinkle-a/s3Ingest/s3Ingest13

• Flatten Level (Required): Select One Level or Multi Level. In one level, flattening will not be applied on complex type. They will be stored as string. In multi level, flattening will be applied in complex level till they become simple type.

• Destination Schema (Required) : Data warehouse schema where the table will be created

• Destination Table name (Required) : It is the table name to be created on the warehouse. If not given, sprinkle will create like ds_<datasourcename>_<tablename>

• Destination Create Table Clause: Provide additional clauses to warehouse-create table queries such as clustering, partitioning, and more, useful for optimizing DML statements. Learn more on how to use this field.

• Create

STEP-4: Run and schedule Ingestion

In the Ingestion Jobs tab:

• Trigger the Job, using Run button

• To schedule, enable Auto-Run. Change the frequency if needed