A Comprehensive Guide to the Snowflake Connector: What You Need to Know
In order to perform any analytical functions on business data, the data should be consolidated and brought to a centralized place. Data connectors are the ones that enable you to combine multiple sources of data into a centralized space. Whether a large enterprise or small business that needs data connectors to deal with business analytics and BI tools.
We can use multiple connectors to securely read and write data into a cloud data warehouse platform. Connectors also enable users to read data from other databases, applications and flat files. Here in this blog, we are going to discuss various connectors available in snowflake and how they help us in various operations on the Snowflake platform. Let’s get into the Snowflake connectors and drivers blog.
What is a connector?
In Simple terminology software connectors perform the transfer of data and functionality among multiple components. A database connector is defined as software that connects an application to any database.
What are Snowflake connectors?
Snowflake connector is software that facilitates an environment to connect to the Snowflake data warehouse platform and enables us to execute operations like Read/ Write, Metadata import functionality and bulk data loading.
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Snowflake Connector Uses
Following are the various operation we can perform using Snowflake Connectors and Drivers:
- Using Snowflake connectors we can Read/ write data from the Snowflake data warehouse.
- We can load bulk data to a table residing in the Snowflake data warehouse.
- Through multiple input links functionality, we can load bulk data into multiple tables at once.
- Connectors help to lookup records from a table in the Snowflake data warehouse
- Allows importing metadata from Snowflake data warehouse using InfoSphere Metadata Asset Manager (IMAM).
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List of Snowflake Connectors
Following are the list of Snowflake Connectors and Drivers:
- SnowSQL – Snowflake Command Line Interface
- Snowflake Connector for Spark
- Snowflake Connector for Python
- Snowflake Connector for Kafka
- Go Snowflake Driver
- Node.js Driver
- JDBC Driver
- .NET Driver
- ODBC Driver
- SnowCD (Connectivity Diagnostic Tool)
Let’s discuss each Snowflake connector and Driver in detail:
1) SnowSQL – Snowflake Command Line Interface
SnowSQL is an advanced command-line client for connecting to Snowflake. It Executes SQL queries and performs multiple DDL and DML operations which include data loading and unloading into and out of database tables.
SnowSQL is a Python-based command-line interface using which one can connect from Linux, Windows, and Mac OS. Snowflake provides platform-specific versions of SnowSQL for different operating systems and the versions may change over time.
2) Snowflake Connector for Spark
The Snowflake Connector for Spark brings Snowflake into the Apache Spark ecosystem. It allows Spark to write data to and read data from Snowflakes. It looks the same as many other Spark data sources (HDFS, PostgreSQL, S3, etc.) from the perspective of Spark.
Snowflake supports different versions of spark which include Spark 2.3, Spark 2.4, and Spark 3.0. Each version requires a separate Snowflake spark connector. It is essential to use the right version of the connector. This connector supports the bi-directional flow of data between a spark cluster and a snowflake cluster.
3) Snowflake Connector for Python
The Snowflake Connector for Python is a native Python package and does not depend on the ODBC or JDBC. This connector streamlines the process to connect your application to your cloud data warehouse. Snowflake Connector for Python supports multiple operations such as data loading, data accessing from external points(S3), query execution, etc.
4) Snowflake Connector for Kafka
The Snowflake Connector for Kafka performs the data reading functionality from one or more Apache Kafka topics and inserts the data into the Snowflake table. Snowflake supports two versions of Kafka connectors which include the Confluent package version of Kafka and the other one is open source software (OSS).
5) Go Snowflake Driver
The users can develop applications using the Go programming language in Snowflake. Go Snowflake drivers offer an interface for developing applications and performs multiple operations. This driver supports very limited operations and does not support operations such as PUT and GET. You can also connect to snowflake through JDBC driver or SnowSQL CLI using Go programming.
6) Node.js Driver
The Node.js driver is written solely using JavaScript. It provides an asynchronous Node.js interface to Snowflake. This driver supports multiple Node.js versions which include v10, V12, and v13.
Using this Node.js driver to perform multiple operations such as connect to Snowflake, consume results, Execute DDL/DML operations and terminate results.
7) JDBC Driver
In order to support core JDBC functionality, Snowflake offers a JDBC type 4 driver. To install JDBC drivers, the environment must be a 64-bit environment and require Java 1.8 or higher versions.
The JDBC driver can be used along with most client applications/tools that support JDBC for integrating with a database server. The best example of JDBC based application provided by snowflake is sfsql.
8) .NET Driver
The Snowflake.NET driver gives the users an interface to connect with the Microsoft.NET framework using which users can develop applications. It supports limited operations. Instead of using .NET Drive, you can also use JDBC or SnowSQL CLI drivers.
9) ODBC Driver
This driver is provided to connect to Snowflake using ODBC-based client applications. We can also use this driver with clients like SQL. The ODBC drivers are version-specific in Snowflake.
10) SnowCD (Connectivity Diagnostic Tool)
The SnowCD (i.e. Snowflake Connectivity Diagnostic Tool) acts as a troubleshooting tool and solves the network issues that arise when connecting to Snowflake.
Closing Thoughts
With this, we have come to the end of the Snowflake connectors and drivers blog. Each Connector and Driver has been designed to perform unique operations on the Snowflake data warehouse platform. You can also check out our Snowflake Tutorial here to have a complete overview of Snowflake architecture and its working style. You can also check out various Snowflake interview questions and answers here. Happy reading!
Author Bio
Yamuna Karumuri is a content writer at CourseDrill. Her passion lies in writing articles on the IT platforms including Machine learning, Workday, Sailpoint, Data Science, Artificial Intelligence, Selenium, MSBI, and so on. You can connect with her via LinkedIn.
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