Data warehouses are created with Snowflake through a Software-as-a-Service (SaaS) platform. This program allows its users to store their data in the Cloud. The Snowflake storage and compute resources scale well to meet changing storage requirements due to its hyper-elastic infrastructure.
Powered by Apache Spark, Cluster Computing operates at breakneck speeds. APIs include Java, Scala, Python, and R, and it provides a convenient environment for developing applications. It also includes Apache Spark, a framework for executing Spark data analysis applications. A key element of Spark is Hadoop's use for Storage, as well as for Process Management. Hadoop is the storage component of Spark as it has its own Cluster Management.
This article introduces you to the Snowflake connector i.e, Spark, and how to link the two using the Snowflake Spark Connector to read Snowflake tables into Spark DataFrame and write Spark DataFrame into Snowflake tables using Scala code.
A Snowflake Database Overview
Snowflake Database is a cloud-based data storage and analytics company (SaaS); it provides data storage and analytics for organizations. Developed for the cloud, Snowflake Database is a completely new SQL Database Engine.
The Database doesn't need to be downloaded or installed to be used. Instead, you can create an account online, which will give you access to the Web Dashboard, where you can create the Database, Schema, and Tables. Access to the database and tables can also be achieved by using Web Console, ODBC, and JDBC drivers, as well as Third-Party connectors.
The Snowflake program is simple and quick to learn if you have a background in SQL, as the architecture is new, but the ANSI SQL syntax and functionality are the same.
As we get into the details of Snowflake's modern data architecture, the following innovative features and functionality are discussed:
Agnostic about cloud power
Adaptability
Concurrency & Workload Separation
Administration at near-zero levels
Safety
Apache Spark: An overview
Large volumes of data are analyzed using Hadoop, which is widely used in the industry. Among the reasons is that Hadoop is based on a basic programming model (MapReduce), which facilitates Scalable, Flexible, Fault-Tolerant, and Cost-Effective Computing. As a method of maintaining query response time and program execution time, massive Datasets must be processed quickly.
Earlier this year, the Apache Software Foundation released Spark, which will speed up Hadoop computing. It is a General-Purpose Computing Engine, which is Open-Source, Scalable, and Distributed, used for processing and analyzing large data files from many sources, including HDFS, S3, Azure, and others.
The capabilities of Spark allow developers to develop iterative algorithms for looping through data sets in a loop and let them explore their data sets in an interactive/exploratory manner, i.e., repetitive queries in a database format. Apache Hadoop MapReduce is several orders of magnitude faster than this, so the latency of such applications could be significantly reduced. Apache Spark was developed through iterative algorithms for training Machine Learning systems.
Some of the reasons Apache Spark is one of the most widely used Big Data platforms include:
Processing speed is lightning fast.
Easy to use.
Advanced analytics are supported.
Stream processing in real-time and is flexible.
A growing and active community.
Machine Learning with Spark.
Cloud Computing with Spark.
How Snowflake Spark Connectors Work
Spark-Snowflake is a connector that connects Apache Spark to Snowflake databases, allowing Apache Spark to read and write data. Snowflake appears to Spark as if it were any other data source, including HDFS, S3, JDBC, etc. Specifically, Snowflake Spark Connector provides the data source "net.snowflake.spark.snowflake" and the short-form "Snowflake".
You must download and use the Snowflake Spark Connector for the correct Spark instance, as each Spark version has its own Snowflake Spark Connector. Connecting Snowflake to Spark through JDBC allows the following actions to be recorded in Spark.
You can create a Spark DataFrame by reading a Snowflake table.
Snowflake tables are created from Spark DataFrames.
Spark RDD/DataFrame/Dataset data is transferred between Snowflake and Spark via internal storage (generated automatically) or external storage (provided for by the user) which is used for storing temporary session data by the Snowflake Spark connector.
Snowflake performs the following actions when you access it via Spark:
Stages are used to create sessions and store them on the Snowflake schema.
During the session, it keeps the stage in place.
When the connection is terminated, the stage is used to store intermediate data.
A list of the Snowflake Spark Integration Parameters
For Snowflake Spark to read/write, you must use the following arguments:
The URL of your account, such as https://oea82.us-east-1.snowflakecomputing.com/.
Username and password for your account. Your account name can be found by entering the URL, for example, "oea82".
Snowflake user name, which is your login name.
User Password for SFPassword.
Snowflake Data Warehouse is called sfWarehouse.
Snowflake Database : Name of the database.
A table belongs to a schema in a database.
Modalities of saving
Using the saveMode property of Spark DataFrameWriter, you can set the SaveMode using mode() ; you can provide either the string below or a constant from the SaveMode class.
An existing file can be overwritten using Overwrite. SaveMode.Overwrite is also an option.
Adding data to an existing file can also be done with SaveMode.Append.
SaveMode.Ignore can be used instead of writing to an existing file when the file doesn't already exist. Ignore.
This option is either SaveMode.ErrorIfExists or ReturnErrorIfExists when a file already exists; alternatively, you can use SaveMode.ErrorIfExists when the file already exists.
Summary
Large data management becomes critical to organizations' success as they expand their businesses. When stakeholders and management work together using Snowflake connector for Spark Integration, the result is a quality product meeting requirements with ease. Polestar is an excellent choice if you need to export data from a source of your choice into your preferred database/destination like Snowflake.