Snowflake is the data warehouse cloud platform that allows easy storing and managing large datasets. The snowflake connectors help to different data sources with Snowflake. In this blog, you will thoroughly learn the three Snowflake connectors that connect data sources with Snowflake – Python Connector, Spark Connector, and Kafka Connector. Get ready! Let’s discover more about them.
Snowflake connectors support connecting different data sources with Snowflake. In this way, Snowflake connectors support connecting platforms such as Python, Kafka, and Apache Spark with Snowflake. In short, you can load data into snowflake tables using the snowflake connectors seamlessly. Especially, the Spark connector achieves bi-directional data transfer between snowflake tables and Spark data frames.
This blog covers the Snowflake ecosystem, different types of Snowflake connectors, their installation, and configuration, and mainly how they help store data to Snowflake tables.
Let’s get started.
|Snowflake Connectors - Table of Contents|
Snowflake is a native data cloud platform that offers the features such as data warehousing, data lakes, data security, and many more. Now, let me shoot a question. So, what is the difference between Snowflake and other cloud services? You need to know that when you leverage Snowflake, you don’t need to select, install, or maintain any software or hardware because it will be managed by Snowflake itself. And you don’t need to set up any separate data warehousing, data lakes, and data security solutions when it comes to Snowflake. Simply put, all about data storage will be governed by Snowflake itself. In particular, Snowflake provides greater flexibility to manage big data effectively.
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The Snowflake ecosystem is made up of many components. It includes certified partners who developed cloud and on-premises solutions to connect with Snowflake, third-party tools and technologies that can connect with Snowflake, and Snowflake-provided clients such as SnowSQL, Connectors, and Drivers.
Apart from certified partners, technologies and tools, and Snowflake provided clients, the Snowflake ecosystem has a few other components: data integration, Business Intelligence, ML and Data Science, security and governance, SQL development and management, and native programming languages.
Let us discuss these components of the Snowflake ecosystem in brief as follows:
Partners and Technologies: You can connect Snowflake with a range of third-party technologies and tools right from Adobe, Alation to Zepl. Please note that it doesn’t mean that only selected technologies and tools can be connected with Snowflake. This is because there are possibilities that all the features of other technologies may not be supported with Snowflake, and they could arise issues too. Here, the drivers such as JDBC and ODBC support connecting these tools and technologies with Snowflake.
Data Integration: Snowflake's data integration is achieved based on the ETL method – Extract, Transform, and Load. Here, ‘Extract’ points out exporting data from a data source. And ‘Transform’ points out converting the data into a suitable format. ‘Load’ points out importing the modified data into the target databases. In this case, the transformation could happen even during the loading of data or after loading data into Snowflake tables. Here, you can connect a range of data integration tools right from Ab Initio, Boomi to Workato with Snowflake.
Business Intelligence (BI): The Snowflake ecosystem includes BI tools, which will support customers to make more informed decisions. Of course, BI tools help you to analyse data deeper and then generate insights, recommendations, and reports accordingly. And outputs of the analyzed data are visualized through dashboards, charts, and other graphical presentations via BI tools. You can connect various BI tools such as Adobe, TIBCO, and many more with Snowflake.
ML and DataScience: Machine Learning (ML) and Data Science are other analytical tools within the Snowflake ecosystem. Although ML and data science tools analyse datasets deeply, they prefer predictive modelling instead of analysing and reporting past data. ML-based tools trace the patterns in large datasets; So, you can also predict future trends and uncover actionable insights. Here, a range of advanced analytics tools from Alteryx to Apache Spark can be connected with Snowflake.
Security and Governance: This component of the Snowflake ecosystem deals with connecting various security and governance tools with Snowflake. In general, security solutions allow you to protect data from unauthorised access, including risk assessment, intrusion detection, data cataloging, data masking, and many more. Here, the security solutions from Acryl Data to TRUSTLOGIX can be connected with Snowflake.
SQL Development and Management: Snowflake offers you native SQL development and data querying interfaces such as Snowflake Worksheets and SnowSQL. In the same way, Snowflake can be connected with third-party SQL tools from Aginity, Dataops to SQL Workbench/J.
Native Programmatic Interfaces: Snowflake supports you to develop applications using different well-known programming languages and platforms. So, Snowflake connectors connect with the programmatic interfaces such as GO, JDBC, .NET, Node.js, PHP, and Python.
The following are the three types of Snowflake connectors used to connect external tools and technologies with Snowflake.
This connector acts as the interface in order to develop Python applications that can connect to Snowflake. This connector allows you to develop applications using Python database API v2 specification. In addition, the Python connector is a native and pure Python package and doesn’t require JDBC and ODBC drivers.
We can install the Python connector in Linux, macOS, and Windows environments. The installation of a Python connector requires the software packages such as Python, python package installer and manager, python packages,pyOpen SQL for macOS, OpenSSL, and FFI for Linux. Also, you can accomplish the installation of the python connector in the following three steps.
Step 1 – Installing the connector
Step 2 – Verifying the installation
Step 3 – Specifying a temporary directory
Snowflake connectors allow you to carry out different standard Snowflake operations as follows:
Pandas is nothing but a data analysis library. And data frame is a data structure that allows you to analyze and manipulate two-dimensional data. API methods provided with this connector help get data from Snowflake databases to Pandas DataFrames. Similarly, the API methods help write data from Pandas Dataframes to Snowflake databases.
Python connector plays a pivotal role while working in a distributed environment where you distribute workloads. This is because the Python connector takes the role of distributing works such as fetching and processing results. You can use result batches to distribute the work of fetching and processing results. Thus, different workers can use different result batches in parallel in order to fetch and process results.
After making a query, the results will be retrieved using different formats such as ResultBatch objects, PyArrow tables, and Pandas DataFrame objects. Then, results are serialized to move them to other workers.
The Python connector leverages Python database API V2.0 specification and uses many standard APIs and Snowflake-specific extensions. It includes many modules as follows, as you can see below:
Similarly, the Python connectors API includes many objects such as Connection, Cursor, Exception, ResultBatch, and ResultMetadata.
Also, the Python connector depends on many third-party libraries, which are required to communicate with Snowflake databases. And any change in any of these libraries drives the python connector to behave unexpectedly; in addition, it can potentially create conflicts with other libraries.
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This Snowflake connector for Spark works based on the Apache ecosystem. In general, Spark can read data from and write data into Snowflake. Snowflake supports all three versions of Spark – Spark 3.0, Spark 3.1, and Spark 3.2. Here, the Spark connector runs as a Spark plug-in. And it is provided as a Spark package (Spark - Snowflake).
Snowflake connector considers Spark data source similar to other data sources such as HDFS, Amazon S3, etc. In general, Spark connector connects Spark cluster and Snowflake connector bi-directional. We can access this Spark cluster either self-hosted or through AWS, Qubole, and Databricks services.
Now, let us see how the interaction is made. Actually, the interaction between Snowflake and Spark occurs in a bi-directional way as follows:
Moreover, data transfer between Spark Connector and Snowflake is accomplished in internal and external modes.
|Internal Mode||External Mode|
|Temporary storage is created and managed by Snowflake||Data is stored in an Amazon S3 bucket or Microsoft Azure|
Temporary storage will exist until the session ends. Once the session is over, data in the storage will be deleted.
|Users can specify a storage location, and data is stored in the specified location. Data is stored until users delete it manually.|
In order to install Spark connector and make data transfer with Snowflake effectively, we have to satisfy the following requirements;
Then, you need to carry out the following steps to install and configure the Spark connector to work with Snowflake.
|Check out and Learn How to configure the Spark|
Apache Kafka is the software platform that processes, stores, and transfers data of streaming events. It functions based on the publishing and subscribing model. In layman's terms – Kafka allows reading and writing a large volume of data asynchronously. In Kafka, an application can push data into a topic of Kafka; similarly, an application can subscribe to a topic to receive data. Here, topic is the storage part of Kafka, where a particular category of messages is stored. And each topic is divided into many partitions. Kafka allows many-to-many relationships between publishers and subscribers. So, a single application could publish as well as subscribe to many Kafka topics.
Kafka Connect is the framework that allows Kafka to connect with external systems. The Kafka connect supports reading data from Kafka topics and writing data into Snowflake tables by running in Kafka connect clusters. Here, ‘Kafka connect cluster’ supports running and scaling Kafka connectors. Kafka connector reads data from Apache Kafka and loads them into Snowflake tables.
There are two Kafka connector versions: Kafka confluent package version and Kafka package of Open Source Software (OSS) Apache.
The Kafka connector supports the Kafka Connect API version between 2.0.0 to 2.8.1. Then, you can configure Kafka for the desired retention time and storage limit. After that, Kafka connect cluster is installed and configured along with the required RAM. For example, 5 MB storage is required for a partition in a topic.
Then, you can install the Kafka connector by downloading the Kafka connector JAR file from Confluent Hub or Maven Central Repository. Kafka connectors can be configured by specifying the details such as Snowflake login credentials, topic name, and Snowflake table names. Each configuration will specify the topics, their respective tables, and database schemas. In addition, the Kafka connector depends on key pair authentication with a minimum of 2048 bit RSA key pair. You can create a public-private key using OpenSSL. And Kafka connector can be configured and started in two modes – Distribution mode and Standalone mode.
Kafka connector enables subscribing to one or more Kafka topics according to its configuration. Here, the configuration is provided by a configuration file or command line.
Let us see the processes involved in moving data into Snowflake tables from Kafka topics using Kafka connectors. Here, you can note that the internal stage, pipes, and tables are the Snowflake objects.
The workflow process is carried out as follows:
Kafka drops the objects such as internal stages, pipes, and tables when they are required no longer.
Internal Stage: As we know, the connector generates one internal stage for one Kafka topic. Here, the format of the stage name is given as follows:
In order to drop stages, you have to find the names of the stages by running SHOW STAGES, then run DROP STAGE to drop each stage that you want to remove.
Pipes: Kafka connector generates one pipe for every partition in a Kafka topic. The format of the pipe name is given as follows:
So, you can drop object pipes by executing SHOW PIPES and DROP PIPES, in this case.
Tables: Similarly, the Kafka connector generates one table for every Kafka topic. For instance, if TEMPERATURE_DATA is the Snowflake table name, then Kafka topic name is identified as temperature_data. Like the previous objects, you can drop the Snowflake object tables by executing SHOW TABLES and DROP TABLE.
In a nutshell, Snowflake connectors support loading data from different data sources into Snowflake tables. In this blog, we hope you have learned about different Snowflake connectors such as Python Connector, Spark Connector, and Kafka Connector. Now, you might have gone through installing and configuring the connectors and how to use the connectors in detail. We believe that this blog might have helped you stay up-to-date with Snowflake connectors.
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Madhuri is a Senior Content Creator at MindMajix. She has written about a range of different topics on various technologies, which include, Splunk, Tensorflow, Selenium, and CEH. She spends most of her time researching on technology, and startups. Connect with her via LinkedIn and Twitter .
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