Snowflake is a Software-as-a-Service (SaaS) platform and is an entirely cloud-based data analytics and storage Data warehouse. To work with cloud infrastructure, it is created and designed with an entirely new SQL database. It gives the users an option to store data in their cloud. Snowflake has a very elastic infrastructure and it adjusts itself with the change in user’s storage needs.
For accessing flake, you don’t need to install and download the database as you did with traditional databases. You first need to create an account that gives you access to the web console. Access the console and then create the database, schema, and tables. By using web consoles, JDBC and ODBC drivers, or third-party connectors one can access the database and tables.
Apache Spark is an open-source distributed general processing system used for data processing at a large scale. For application development, it provides you with different high-level API’slike Python, Java, and Scala. For seamless execution of Spark applications, there is a framework called R. Apache Spark. Hadoop is used in two ways by spark one for process management and the other for storage. Spark uses Hadoop for storage as it has its own cluster management.
This article will provide you with all the necessary information regarding Snowflake Spark and a deep dive into how to read Snowflake table from Spark DataFrame and write Spark DataFrame to Snowflake table.
Snowflake Spark Connector
A Snowflake Spark connector “spark-snowflake” permits Apache Spark to read data from, and write data to Snowflake tables. Snowflake is treated as data sources similar to HDFS, JDBC, S3, etc by Spark whenever we use a connector. In reality, the data source that the Snowflake Spark connector provides is “net.snowflake.spark.snowflake” and its short-form is “snowflake”.
For each Spark version, Snowflake Spark provides a different Spark connector so you have to keep in check that you are downloading the right version. The communication between Snowflake and Snowflake Spark Connector is established via the JDBC driver and it performs the following actions:
- You may create a Spark DataFrame, By reading a table from Snowflake.
- Create a Snowflake table from a Spark DataFrame.
The data transfer between Snowflake and Spark RDD/DataFrame/Dataset is done via Snowflake internal storage that is generated automatically or external storage which is provided by the user.
While accessing Snowflake from Spark, it performs the following actions:
- Along with storage on Snowflake schema a session is created with stage.
- The stage is maintained throughout the session.
- The stage is used to store intermediate data and the stage is dropped when the connection is terminated.
The Snowflake 1.1 dependent library is automatically downloaded by the Maven Dependency and it also includes all the relevant jar files included in the project.
The code in this section should be included in the Maven configuration file pom.xml under <dependencies>……/dependencies> tag.
The <version> tag designates which version of the driver you want to use. Here Version 3.13.7 is used for demonstration purposes.
Write Spark DataFrame to Snowflake Table
- By using the DataFrame’s write() method, one can write a Spark DataFrame to a Snowflake table.
- You need to use the format() method to give either snowflake or net.snowflake.spark.snowflake as the data source name.
- Use the Option() method to state options like username, account, URL, schema, password, etc.
- Use the dbtable option for specifying the Snowflake table name that you want to write to
- And, Use mode() to indicate if you wanted to ignore, overwrite or append the file that is already present.
Below is a sample of Snowflake Spark Connector code in Scala:
Read Snowflake Table from Spark DataFrame
Using the read() method (which is the DataFrameReader object) of the SparkSession and providing data source name via format() method, connection options, and table name using dbtable.
Here is an example of the same:
In this article, we got to know how Snowflake Spark helps in writing Spark DataFrame to Snowflake Table and read Snowflake Table from Spark DataFrame which proves to be really helpful for the organizations that are expanding and managing large amounts of data it is really crucial for them, to achieve the desired efficiency.
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