Snowflake Integrations

In today’s data-driven landscape, integrating data across systems is essential for breaking down silos and driving informed decisions. 

Snowflake supports data integration to share data, streamline data movement, and derive real-time insights using robust integration tools. Its architecture supports modern integration methods from batch processing to real-time streaming.

Let’s dig deep into the Snowflake integration in detail.

Table of Contents

• Introduction
• Importance of Snowflake Integrations
• Integration Methods
• Snowflake Connectors (overview and types)
• Schema Models (Star vs. Snowflake)
• Importing CSV files in Snowflake
• Frequently Asked Questions

Introduction to Snowflake

Snowflake is a cloud-native data warehousing platform for efficiently storing, analysing, and sharing data. 

• Snowflake can operate on multiple cloud environments, including AWS, Azure, and GCP.
• It provides a robust architecture that separates compute and storage. 
• It offers improved elastic scalability, handling any number of users, data volumes, and workloads.
• It provides a user-friendly and intuitive interface, which enables users to interact easily with Snowflake databases.

In short, Snowflake offers scalable, multi-cloud support with a user-friendly interface and a robust architecture.

Importance of Snowflake Integrations

These are compelling reasons why Snowflake integration is crucial for detailed data analytics and informed decision-making.

• Eliminating Data Silos: Snowflake integration unifies disparate data sources in one place. It helps businesses to process and analyse with a comprehensive and consistent data set.
• Enhancing Data Quality: Snowflake integration helps improve data quality by cleaning and transforming data.
• Streamlining Operations: The automated Snowflake integrations help reduce manual data management efforts. It helps reduce potential human errors, thus improving efficiency.
• Enabling real-time insights: Snowflake integration processes real-time data precisely to provide actionable insights to users.

Hence, Snowflake is vital for unifying data sources, improving data quality, and enabling real-time insights for data-driven decision-making.

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Snowflake Integration Methods

Snowflake supports different methods to integrate data sources with Snowflake.

• ETL (Extract, Transform, and Load): ETL is a traditional method for extracting data from sources and loading it into Snowflake databases. It is a good choice when data transformation is required before loading data into Snowflake.
• ELT (Extract, Load, and Transform): This method loads raw data into Snowflake and then transforms it. It is a better choice when you need more flexible data transformation.
• Batch Processing: This method is used when you load data in batches at different schedules in Snowflake.
• API-based integration: You can use APIs for seamless data integration between different systems.

These methods ensure reliable data flow between diverse platforms, enabling scalable data operations.

Snowflake Connectors 

Snowflake connectors are powerful integration tools that enable seamless data transfer between Snowflake and various external systems.

• Snowflake connectors allow efficient loading, querying, and data management from multiple sources into Snowflake. 
• They support both batch and streaming data integration.
• They enhance Snowflake's capabilities by integrating with programming languages like Python and Java, data frameworks like Spark, and streaming platforms like Kafka.
• They support secure authentication methods like OAuth, key pair authentication, and more. 

In addition to Snowflake connectors, Snowflake supports REST APIs and SnowSQL CLI for integration.

Snowflake connectors enable integration with various external systems, programming languages, and frameworks.

Types of Snowflake Connectors

We are now at the core of the Snowflake integrations guide. In this section, we'll learn about the three types of Snowflake connectors widely used for data integration. 

Let's check them out.

• Snowflake Connector for Python

The Snowflake Python connector allows developers to interact with Snowflake using Python’s DB API v2. It is a native package that does not rely on JDBC or ODBC drivers.

The key operations of this connector include:

• • Creating databases, schemas, and tables
  • Inserting and loading data from various sources
  • Executing queries synchronously and asynchronously
  • Handling query status, cancellation, and error management
  • Using Pandas DataFrames to write data into and read data from Snowflake

This connector also supports workload distribution for large data processing tasks. Query results can be retrieved as ResultBatch objects, PyArrow tables, or Pandas DataFrames.

• Snowflake Connector for Spark

The Spark Snowflake connector allows Apache Spark to read data from Snowflake and write data into Snowflake. It supports bi-directional integration and can be used in environments like AWS EMR, Databricks, or Qubole.

Key functionalities of this connector include:

• • Populating Spark DataFrames from Snowflake tables
  • Writing Spark DataFrames back into Snowflake
  • Supporting internal mode for temporary storage for sessions
  • Supporting external mode for persistent storage using AWS S3 or Azure Blob Storage

 

• Snowflake Connector for Kafka

The Snowflake Kafka connector supports near real-time data streaming from Kafka topics into Snowflake. It uses Kafka Connect, which facilitates integration between Kafka and external systems.

• • This connector supports both standalone and distributed modes. 
  • Each Kafka topic corresponds to a Snowflake table, with internal stages and pipes created for ingesting data.
  • This connector processes data through a defined workflow where topics feed internal stages, pipes manage data partitions, and tables store the final output.
  • Using standard Snowflake SQL commands, you can manage resources like stages, pipes, and tables.

In summary, Snowflake connectors enable smooth data integration, efficient querying, and bi-directional data exchange across platforms.

• Use cases of the Snowflake connectors

Let’s look at the use cases of Snowflake connectors at a glance.

• • Python connector: This connector is ideal for scripting ETL tasks and working with pandas for data analysis.
  • Spark connector: This connector is best suited for big data ecosystems and batch processing.
  • Kafka connector: You can use this connector for near-real-time pipelines with streaming data.

Schema Models (Star vs. Snowflake)

Let’s move on to learn the Star and Snowflake schema in this part of the Snowflake integrations guide.

In QlikView, the relational database schemas are of two types as listed below.

• Star Schema
• Snowflake Schema

Before exploring the two different schemas, we’ll understand the tables used to store data.

There are two types of tables: Fact table and Dimension table.

• Fact Table; The Fact table contains numeric data, such as IDs, keys, etc. It is connected to the dimension table across the data model. The fact table is placed at the centre of the Star or Snowflake schema, bounded by dimension tables in a data model.
• Dimension Table: A dimension table consists of descriptive or textual data attributes such as Product ID, Product name, Manager ID, Manager name, etc. Every dimension table describes the data of that particular table.

For example, the dimension table for a specific product provides information about that product. 

The highlighted ones in the figure below are the Dimension tables, and the fact table is connected to all the dimension tables. 

Now, let's move on to learning the star and Snowflake schema.

• Star Schema; As the name suggests, a star schema resembles a star with a fact table at its center and surrounded by all dimension tables. The central fact table is linked with the dimension tables directly

A star schema allows large batches of data to be loaded quickly. It is a flexible model and easily handles complex scripts. It also provides good RAM consumption.

A star schema represents the entity-relationship diagram between a fact table and dimension tables. In this schema, every dimension table contains a primary key. Additionally, they don’t have any parent tables.

• • Star schema Data model:
    
    The primary keys connect the central fact table with multiple dimension tables in the star schema data model. This schema is commonly used for Online Analytical Processing (OLAP), which provides high speed. The resulting star schema has a spoke, or a Star-like representation.
    
    The figure shows that the center table (Sales details) represents the Fact table. The dimension tables, such as product, location, customer, and order details, are connected across the fact table. 

 

• Snowflake Schema:  A Snowflake schema is a multi-dimensional data model where dimension tables are broken into sub-dimensions. You can use this schema for Business Intelligence (BI), OLAP warehouses, data marts, and relational databases.

Although breaking down dimensional tables creates complexity, it excels in analysing data. As the name suggests, the Snowflake schema’s ERD seems like a Snowflake.

• • Advantages of the Snowflake Schema: Below are the advantages of the Snowflake schema.
    • Snowflake schema increases flexibility.
    • It improves query performance with minimum disk storage
    • It is easy to maintain

The Top 40+ Best BigQuery Interview Questions & Answers 2025 article can help you understand key concepts and prepare for interviews.

Differences between Star Schema and Snowflake Schema

The table below, which is included in this section of the Snowflake integrations blog, clearly shows the key differences between the Star and Snowflake schemas.

Let’s take a glance!

Description	Star Schema	Snowflake Schema
Data Model	Top-down approach	Bottom-up approach
Normalisation/Denormalisation	Both the fact table and Dimension tables are in the denormalised form.	Fact tables are in the denormalised form. Dimension tables are in the normalised form.
Ease of use	It is easy to understand Low query rate	Its complex structure makes it harder to understand.
Ease of maintenance	It has redundant data, so it is not easy to maintain and update.	It has no redundancy, so it is easy to maintain and change.
Dimension Tables	It contains only a single-dimensional table for each dimension	Depending on the data, it contains more than one dimension table for each dimension.
Query Performance	It needs fewer foreign keys It takes less time for execution	It needs more foreign keys It takes a long time for execution
Joins	It has fewer joins	It has more Joins
Ideal use	We can prefer the star schema when the dimension table has fewer rows.	The Snowflake schema is preferred when the dimension table is relatively big. This schema helps to reduce the size of the data.

Importing CSV files in Snowflake

Let’s learn how to import CSV files in Snowflake in this section.

Snowflake allows uploading CSV files into Snowflake databases from local machines running Linux, macOS, or Windows.

In this example, we'll go through a step-by-step procedure to import a file known as ‘Organisations’. The file has three columns: O.id, O.location, and O.name. It is located in the ‘test1’ folder of the local machine, and contains the following structure:

• Twitter, Canada
• Amazon, Washington
• Flipkart, California

Step-by-step procedure for importing CSV files into Snowflake

Step 1: Create a stage

First, create an internal stage in Snowflake to store the CSV file temporarily.

CREATE OR REPLACE STAGE organizations_stage1;

Step 2: Define a file format

Create the file format that matches your CSV structure using the “FILE FORMAT” command.

CREATE OR REPLACE FILE FORMAT organizations_format
TYPE = 'CSV'
FIELD_DELIMITER = ', '
SKIP_HEADER = 1;

Step 3: Upload the CSV file.

Upload your CSV file from your local folder to the Snowflake stage using the “PUT” command.

• Linux/MacOS

put file:///tmp1/data1/Oragnizations.csv @organizations_stage1;

• Windows

put file://D: \test1\Organizations.CSV @organizations_stage1;

Step 4: Verify the stage contents

Now, you can verify if the Snowflake stage is populated with data from a file.

Select
b.$1,
b.$2,
b.$3
from @_stage1 (file_format => organizations_format1) c;

Step 5: Create the target table

Before running the ' COPY INTO ' command, you must create a table in the Snowflake database with a similar structure to the CSV file you will import.

Create or replace table organizations (
0.Id integer,
O.name varchar(100),
0.location varchar(100)
)

Step 6: Load data into the table

Load the data from the Snowflake stage into the Snowflake database table through the ‘COPY INTO’ command.

copy into test.organizations from @organizations_stage1;
copy into test.organizations from (select c.$1, c.$2, from @organizations_stage1 (file_format1 => organizations_format1) c);

Step 7: Verify the loaded data

Next, examine if the Snowflake database table is populated with data as shown below.

select * from organizations;

 

O.id	O.name	O.location
101	Twitter	Canada
102	Amazon	Washington
103	Flipkart	California

 

Summary

• Snowflake integration allows Snowflake to interact with a diverse ecosystem of tools, applications and data sources.
• The integration enables businesses to consolidate data, perform analytics, and gain actionable insights.
• It allows data migration from legacy systems, creates ETL/ELT workflows, and generates real-time analytics.
• It breaks down data silos and combines data from various sources into a single platform.
• It helps enhance data quality by cleaning, transforming, and standardising data.
• It enables real-time data analytics to provide up-to-date insights to users so they can make informed decisions.

Frequently Asked Questions

1. What types of data can Snowflake integrate with?

Ans: Snowflake can integrate with structured data, such as relational databases, and semi-structured data, such as  XML, JSON, and Parquet. It can also integrate with unstructured data through cloud storage.

2. Is Snowflake an ETL tool?

Ans: Snowflake's primary function is data warehousing, but it also supports data transformations and seamlessly integrates with ETL/ELT tools for data integration.

3. How secure are Snowflake integrations?

Ans: Snowflake offers secured integrations with the following features.

• Encryption of data in transit and at rest
• Network policies to control access
• Role-based access control to manage user permissions

4. Do Snowflake connectors support batch and bulk loading?

Ans. Yes, Snowflake connectors support batch and bulk loading. The effectiveness of bulk loading depends on the data volume and the loading frequency. You can use the COPY command to load data in bulk in Snowflake.

5. How can I integrate with Snowflake except using connectors?

Ans: You can integrate with Snowflake using:

• Snowflake REST APIs
• SnowSQL CLI
• Third-party orchestration tools

Conclusion

Let’s sum up! Snowflake integrations streamline data flow across platforms, improve data quality, and enable real-time analytics. This blog post covered various Snowflake integration methods, key connectors, schema models and  CSV import methods into Snowflake.

Snowflake integrations empower businesses to break data silos and make data-driven decisions efficiently.

To deepen your understanding, consider exploring the Snowflake course in MindMajix.