Table of Contents –
- Introduction
- Snowflake Spark Connector
- Maven Dependency
- Write Spark DataFrame to Snowflake Table
- Read Snowflake Table from Spark DataFrame
- Conclusion
Introduction
Snowflake is a Software-as-a-Service (SaaS) platform and is an entirely cloud-based data analytics and storage Data warehouse. It is constructed and designed with an entirely new SQL database to interact with cloud architecture. 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. Read the various Snowflake Features at Hevo’s blog.
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. The database and tables can be accessed via online terminals, JDBC and ODBC drivers, or third-party connectors.
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 post will give you with all of the important information on Snowflake Spark as well as a thorough analysis into how to read Snowflake table from Spark DataFrame and write Spark DataFrame to Snowflake table.
Snowflake Spark Connector
Spark Connector Snowflake The “spark-snowflake” Snowflake Spark connector allows Apache Spark to read 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. The data source provided by the Snowflake Spark connector is “net.snowflake.spark.snowflake,” with the short-form “snowflake”.
Snowflake Spark provides a separate Spark connection for each Spark version, so make sure you’re downloading the correct 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.
- From a Spark DataFrame, create a Snowflake table.
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.
- Throughout the session, the stage is maintained.
- The stage is used to store intermediate data and the stage is dropped when the connection is terminated.
Maven Dependency
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.
This code should be placed in the pom.xml file within the dependencies>……/dependencies> tag.
The <version> tag designates which version of the driver you want to use. Here Version 3.13.7 is used for displaying purposes.
Write Spark DataFrame to Snowflake Table
- A Spark DataFrame can be written to a Snowflake table using the DataFrame’s write() method.
- You need to use the format() method to give either snowflake or net.snowflake.spark.snowflake, where the name of data source is snowflake.
- 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
- Use mode() to specify whether you want to ignore, overwrite, or append the existing file.
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:
Output:
Conclusion
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.
Hevo Data, a No-code Data Pipeline, provides you with cloud-based data analytics and storage. It allows you to export data from your selected data sources and load it using its strong integration. There are over 100 sources (including over 40 free sources) and you can put data straight into a Data Warehouse or the destination. of your choice like Snowflake. It also has a fault-tolerant architecture that ensures your data is safe.. Hevo provides you with a structured, systematic, efficient, and fully automated solution to manage data in real-time and have the data ready for analysis.
