Connectors → Snowflake
About Snowflake
Snowflake is a cloud data warehouse with a SQL interface. Snowflake stores both structured and semi-structured data, including JSON and XML.
About the Snowflake Connector
The Incorta Snowflake connector uses the Snowflake JDBC Driver to connect to Snowflake data, which lets you access information in SQL databases using standard SQL queries.
The Snowflake connector supports the following Incorta specific functionality:
Feature | Supported |
---|---|
Chunking | ✔ |
Data Agent | |
Encryption at Ingest | |
Incremental Load | ✔ |
Multi-Source | |
OAuth | |
Performance Optimized | ✔ |
Remote | |
Single-Source | ✔ |
Spark Extraction | ✔ |
Webhook Callbacks | ✔ |
Steps to connect Snowflake and Incorta
To connect Snowflake and Incorta, here are the high level steps, tools, and procedures:
- Create an external data source
- Create a schema with the Schema Wizard
- or, Create a schema with the Schema Designer
- Load the schema
- Explore the schema
Create an external data source
Here are the steps to create an external data source with the Snowflake connector:
- Sign in to the Incorta Direct Data Platform™.
- In the Navigation bar, select Data.
- In the Action bar, select + New → Add Data Source.
- In the Choose a Data Source dialog, in Database, select Snowflake.
- In the New Data Source dialog, specify the applicable connector properties.
- To test, select Test Connection.
- Select Ok to save your changes.
Snowflake connector properties
Here are the properties for the Snowflake connector:
Property | Control | Description |
---|---|---|
Data Source Name | text box | Enter the name of the data source |
Username | text box | Enter the username of the Snowflake account used to authenticate to the server |
Password | text box | Enter the password of the Snowflake account used to authenticate to the server |
Connection Pool | text box | Enter the connection pool. The default is 30. |
Connection String | text box | Enter the connection string for the Snowflake JDBC driver. The format is:jdbc:snowflake://<HOST>:<PORT>/<DATABASE_NAME> The <HOST> is <ACCOUNT_NAME>.snowflakecomputing.com , where <ACCOUNT_NAME> specifies the full name of your account provided by Snowflake. Note that your full account name might include additional segments that identify the region and cloud platform where your account is hosted. The <PORT> default is 443 for HTTPS. |
Set Extraction Timeout? | toggle | Enable this property to terminate the extraction job for a SQL query after a specified time. |
Stop After (Minutes) | text box | Enable Set Extraction Timeout? to configure this property. Enter the maximum number of minutes that the extraction job for a SQL query can run before the system automatically terminates it. |
Connection Properties | text box | Optionally enter connector properties for a custom connection to Snowflake in the format: propertyName=propertyValue , where each connector property is on a new line.Refer to the Connection Parameters section of the Snowflake JDBC Driver documentation for further information. |
Create a schema with the Schema Wizard
Here are the steps to create a Snowflake schema with the Schema Wizard:
- Sign in to the Incorta Direct Data Platform™.
- In the Navigation bar, select Schema.
- In the Action bar, select + New → Schema Wizard.
- In (1) Choose a Source, specify the following:
- For Enter a name, enter the schema name.
- For Select a Datasource, select the Snowflake external data source.
- Optionally create a description.
- In the Schema Wizard footer, select Next.
- In (2) Manage Tables, in the Data Panel, first select the name of the Data Source, and then check the Select All checkbox.
- In the Schema Wizard footer, select Next.
- In (3) Finalize, in the Schema Wizard footer, select Create Schema.
Create a schema with the Schema Designer
Here are the steps to create a Snowflake schema using the Schema Designer:
- Sign in to the Incorta Direct Data Platform™.
- In the Navigation bar, select Schema.
- In the Action bar, select + New → Create Schema.
- In Name, specify the schema name, and select Save.
- In Start adding tables to your schema, select SQL Database.
- In the Data Source dialog, specify the Snowflake table data source properties.
- Select Add.
- In the Table Editor, in the Table Summary section, enter the table name.
- To save your changes, select Done in the Action bar.
Snowflake table data source properties
For a schema table in Incorta, you can define the following Snowflake specific data source properties as follows:
Property | Control | Description |
---|---|---|
Type | drop down list | The default is SQL Database |
Data Source | drop down list | Select the Snowflake external data source |
Incremental | toggle | Enable the incremental load configuration for the schema table |
Fetch Size | text box | Used for performance improvement, fetch size defines the number of records that will be retrieved from the database in each batch until all records are retrieved. The default is 5000. |
Incremental Extract Using | drop down list | Enable Incremental to configure this property. Select one of the following options:
|
Incremental Column | drop down list | Enable Incremental and select Maximum Value of a Column for Incremental Extract Using to configure this property. Select the column to be used for Maximum Value of a Column. The Loader will track and use the greatest value or most recent timestamp for each load operation. |
Set Extraction Timeout? | toggle | Enable Incremental to configure this property. Enable this property to terminate the extraction job for an incremental SQL query after a specified time. This configuration will override the data source configuration. |
Stop After (Minutes) | text box | Enable Incremental and Set Extraction Timeout? to configure this property. Enter the maximum number of minutes that the extraction job for an incremental SQL query can run before the system automatically terminates it. This configuration will override the data source configuration. |
Query | text box | Enter the query to retrieve data from the Snowflake database |
Update Query | text box | Enable Incremental to configure this property. Enter the query to retrieve data updates from the Snowflake database. |
Incremental Field Type | drop down list | Enable Incremental to configure this property. Select the format of the incremental field:
|
Chunking Method | drop down list | Chunking methods allow for parallel extraction of large tables. The default is No Chunking. There are two chunking methods:
|
Chunk Size | text box | Select By Size of Chunking for Chunking Method to configure this property. Enter the number of records to extract in each chunk in relation to the Fetch Size. The default is three times the Fetch Size. |
Order Column | drop down list | Select a Chunking Method to configure this property. Select a column in the source table you want to order by before chunking. For a Chunking Method of By Size of Chunking, it is typically an ID column and must be numeric. For a Chunking Method of By Timestamp, it is a timestamp column. |
Chunk Period | drop down list | Select the chunk period to use in dividing the chunks:
|
Number of Days | text box | Select Custom for Chunk Period to configure this property. Enter the chunking period in days. |
Upper Bound for Order Column | text box | Optional. Enter the maximum value for the order column. |
Lower Bound for Order Column | text box | Optional. Enter the minimum value for the order column. |
Enable Spark Based Extraction | toggle | Enable a Spark job to parallelize the data ingest. Spark Based Extraction is available only when the Chunking Method is No Chunking. |
Max Number of Parallel Queries | text box | Enable Spark Based Extraction to configure this property. Enter the maximum number of parallel queries to run at a time. |
Column to Parallelize Queries on | drop down list | Enable Spark Based Extraction to configure this property. Select a numerical column in the source table that you want Spark to parallelize the extraction queries on. |
Memory Per Extractor | text box | Enable Spark Based Extraction to configure this property. Enter the numerical amount of memory to use per extractor in gigabytes (GB). |
Callback | toggle | Enable this option to call back on the source data set |
Callback URL | text box | Enable Callback to configure this property. Specify the URL. |
View the schema diagram with the Schema Diagram Viewer
Here are the steps to view the schema diagram using the Schema Diagram Viewer:
- Sign in to the Incorta Direct Data Platform™.
- In the Navigation bar, select Schema.
- In the list of schemas, select the Snowflake schema.
- In the Schema Designer, in the Action bar, select Diagram.
Load the schema
Here are the steps to perform a Full Load of the Snowflake schema using the Schema Designer:
- Sign in to the Incorta Direct Data Platform™.
- In the Navigation bar, select Schema.
- In the list of schemas, select the Snowflake schema.
- In the Schema Designer, in the Action bar, select Load → Load Now → Full.
- To review the load status, in Last Load Status, select the date.
Explore the schema
With the full load of the Snowflake schema complete, you can use the Analyzer to explore the schema, create your first insight, and save the insight to a new dashboard.
To open the Analyzer from the schema, follow these steps:
- In the Navigation bar, select Schema.
- In the Schema Manager, in the List view, select the Snowflake schema.
- In the Schema Designer, in the Action bar, select Explore Data.
Additional Considerations
Types of Incremental Loads
You can enable Incremental Load for a Snowflake data source. There are two types of incremental extracts:
Last Successful Extract Time
Fetch updates since the last time the tables were loaded. It is determined by the difference between the current time and the database timestamp.
Maximum Value of a Column
The column-based strategy depends on an extra column called Incremental Column in each table. The Snowflake connector supports both timestamp and numeric columns. A timestamp column is of the type date or timestamp. A numeric column is of the type int or long.
Changing the incremental load strategy requires a full load to ensure data integrity.
Incremental Load Example
In this example, the invoices
table must contain a column of the type Date or Timestamp in order to load the table incrementally with a last successful extract time strategy. In this case, the name of the date column is ModifiedDate
and the format of the column is Timestamp.
Here are the data source property values for this example:
- Incremental is enabled
- Query contains
SELECT * FROM invoices
- Update Query contains
SELECT * FROM invoices WHERE ModifiedDate > ?
- Incremental Field Type = Timestamp
?
is a variable in the update query that contains the last schema refresh date. When running an update query for an incremental load, the ?
character will be replaced with the last incremental reference to construct a valid query to the database. The ?
reference character is not valid in a standard query.