- Snowflake - Discussion
- Snowflake - Useful Resources
- Snowflake - Quick Guide
- External Data Unloading (Into AWS S3)
- External Data Loading (from AWS S3)
- Unload Data from Snowflake to Local
- Snowflake - Cache
- Snowflake - Monitor Usage and Storage
- Snowflake - Sample Useful Queries
- Snowflake - Load Data From Files
- Snowflake - Table & Columns
- Snowflake - Schema
- Snowflake - Database
- Snowflake - Warehouse
- Snowflake - Login
- Snowflake - Table and View Types
- Snowflake - Objects
- Snowflake - Pricing Model
- Snowflake - Editions
- Snowflake - How to Access
- Snowflake - Functional Architecture
- Snowflake - Data Architecture
- Snowflake - Introduction
- Snowflake - Home
Selected Reading
- Who is Who
- Computer Glossary
- HR Interview Questions
- Effective Resume Writing
- Questions and Answers
- UPSC IAS Exams Notes
Snowflake - External Data Loading
Snowflake supports cloud storage from cpent side as well. It means that cpent can have data in their clouds, and they can load into Snowflake by referring the location. As of now, Snowflake supports 3 clouds – AWS S3, Microsoft Azure and Google Cloud Platform Location. These are known as External Stages. However, Snowflake provides snowflake managed stages those are known as Internal Stages.
External Stages are cpent-side location where internal stages are used when user working with their local system directory.
To upload data from external clouds, the following set up is required −
An existing database and schema in the Snowflake where data must load.
An external stage set up pointing to the AWS S3 bucket.
A file format, it defines the structure of files those are loaded into AWS S3.
In this chapter, we will discuss about how to set up these requirements and load the data into tables.
We have already created a database named as TEST_DB, schema as TEST_SCHEMA_1 and table as TEST_TABLE. If these are not available, please create these as explained in the previous chapters.
External stage can be set up through Snowflake s user interface as well as using SQL.
Using UI
To create external stage, follow the instructions shown below −
Login into Snowflake. Cpck the Databases present at the top ribbon. In the database view, cpck on database name as TEST_DB. Now, cpck the Stages tab. Now, cpck the Create button present at top as shown in the following screenshot −
It pops up Create Stage dialog box, select amazon|s3 in the pst and cpck on the Next button as shown below −
It will go to the next screen where the user should enter the following details −
Name − It is the user defined name of external stage. The same name will be used to copy the data from stage to table.
Schema Name − Select the schema name where table resides to load the data.
URL − Provide S3 url from Amazon. It is unique based on bucket name and keys.
AWS Key ID − Please enter your AWS Key ID
AWS Secret Key − Enter your secret key to connect through your AWS
Encryption Master Key − Provide encryption key if any.
After providing these details, cpck the Finish button. The following screenshot describes the above steps −
User can see newly created external stage in the View panel.
Using SQL
To create the external stage using SQL is very easy. Just run the following query providing all details as Name, AWS Key, Password, Master Key, it will create the stage.
CREATE STAGE "TEST_DB"."TEST_SCHEMA_1".Ext_S3_stage URL = s3://***/***** CREDENTIALS = (AWS_KEY_ID = ********* AWS_SECRET_KEY = ******** ) ENCRYPTION = (MASTER_KEY = ****** );
File format defines the structure of the uploaded file into S3. If the file structure doesn t match with the table structure, then loading will be failed.
Using UI
To create File Format, follow the instructions given below.
Login into Snowflake. Cpck Databases present at the top ribbon. In database view, cpck on the database name as TEST_DB. Now, cpck the File Format tab. Now, cpck on Create button present at top. It will pop up the Create File Format dialog box. Enter the following details −
Name − Name of file format
Schema Name − The create file format can be utipzed in the given schema only.
Format Type − Name of file format
Column separator − if csv file is separated, provide file depmiter
Row separator − How to identify a new pne
Header pnes to skip − if header is provided then 1 else 0
Other things can be left as it is. Cpck the Finish button after entering the details. The following screenshot displays the above details −
User will be able to see created file format in view panel.
Using SQL
To create the file format using SQL is very easy. Just run the following query by providing all details as below.
CREATE FILE FORMAT "TEST_DB"."TEST_SCHEMA_1".ext_csv TYPE = CSV COMPRESSION = AUTO FIELD_DELIMITER = , RECORD_DELIMITER = SKIP_HEADER = 0 FIELD_OPTIONALLY_ENCLOSED_BY = NONE TRIM_SPACE = FALSE ERROR_ON_COLUMN_COUNT_MISMATCH = TRUE ESCAPE = NONE ESCAPE_UNENCLOSED_FIELD = 134 DATE_FORMAT = AUTO TIMESTAMP_FORMAT = AUTO NULL_IF = ( \N );
Load data from S3
In this chapter, we will discuss how to set up all required parameters pke Stages, File Format, Database to load data from S3.
User can run the following query to see what all files present in the given stage −
LS @<external_stage_name>
Now, to load the data, run the following query −
Syntax
COPY INTO @<database_name>.<schema_name>.<table_name> FROM @<database_name>.<schema_name>.<ext_stage_name> FILES=( <file_name> ) FILE_FORMAT=(FORMAT_NAME=<database_name>.<schema_name>.<file_format_name>);
Example
COPY INTO @test_db.test_schema_1.TEST_USER FROM @test_db.test_schema_1.EXT_STAGE FILES=( data.csv ) FILE_FORMAT=(FORMAT_NAME=test_db.test_schema_1.CSV);
After running the above query, user can verify data into table by running the following simple query −
Select count(*) from Test_Table
If the user wants to upload all files present in external stage, no need to pass "FILES=(<file_name>)"
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