How To Send Data From Snowflake to Amazon Kinesis

In this article, we will explore the process of sending data from Snowflake to Amazon Kinesis, two powerful tools for managing and analyzing data.

Sending data from Snowflake to Amazon Kinesis is not a common requirement in data engineering. In case you are looking for a guide on how to send data from Amazon Kinesis to Snowflake, read this tutorial instead. Otherwise, go ahead.

Understanding Snowflake and Amazon Kinesis

What is Snowflake

Snowflake is a cloud-based data warehousing platform that provides scalable and elastic storage for all your data. It offers a unique architecture that separates storage and compute, allowing you to scale each independently. Snowflake is built for the cloud and designed to provide performance, scalability, and simplicity in data warehousing. It allows you to store and analyze structured and semi-structured data, making it a versatile platform for a wide range of use cases. With its unique cloud-native architecture, Snowflake can handle massive data volumes and execute complex queries with speed and efficiency.

Snowflake data warehouse architecture separates storage and computation, ensuring that you only pay for the resources you use. This cost-effective approach makes it an attractive option for organizations of all sizes.

What is Amazon Kinesis

Amazon Kinesis (also referred as AWS Kinesis) is a powerful platform for streaming data processing and analysis. It can handle real-time data from various sources, such as web applications, IoT devices, and log files. With Kinesis, you can process and analyze data as it arrives, enabling you to take immediate actions based on real-time insights.

Amazon Kinesis offers various services to cater to different streaming and processing needs. One of its main services is Kinesis Data Streams, which allows you to build custom applications that can process and analyze streaming data. Kinesis Data Streams helps with data ingestion from thousands of data sources simultaneously and processes it in real-time using applications you build with popular programming languages such as Java, Python, Ruby, etc.

Amazon Kinesis also provides Amazon Kinesis Firehose, a fully managed service that makes it easy to load streaming data into data lakes, data stores, and analytics tools. It can automatically scale to handle any amount of data and can transform and load data into destinations such as Amazon S3, Amazon Redshift, or Amazon Elasticsearch Service. It has a tight integration with most of the AWS services.

Amazon Kinesis also offers Kinesis Data Analytics, a service that allows you to perform real-time analytics on streaming data without the need to write complex code or manage infrastructure. With Kinesis Data Analytics, you can use standard SQL queries to analyze streaming data and gain insights in real-time.

The use cases for sending data from Snowflake to Amazon Kinesis

There could be some practical use cases where organizations would want to send data from Snowflake to Amazon Kinesis as following:

1. Real-time dashboard updates from historical data:

Imagine a retail company that has been storing years of sales data in Snowflake. They've recently built a real-time dashboard system that relies on Kinesis to update. Now, they want to populate this dashboard with a real-time stream of their historical sales data to test and validate its performance and visualizations.

Although, it is recommended to connect Snowflake directly with the dashboard instead of sending data to Kinesis first, but if there are certain technical constraints or requirements where direct connection might not be desirable, you may need to send data from Snowflake to Kinesis.

2. Migrating to a new event-driven architecture:

Imagine a finance company that has stored loan and transaction data in Snowflake. They are migrating to an event-driven microservices architecture, where events in a Kinesis stream trigger various microservices. By sending data from Snowflake to Kinesis, they can simulate real-time loan or transaction events to ensure the new system behaves correctly.

3. Cross-platform synchronization:

Imagine a multinational company that operates in different regions with different data systems. They centralized their data in Snowflake for analytics. However, one regional branch uses a system that gets its data from a Kinesis stream. To ensure that branch has access to the latest data updates, the company streams necessary data updates from Snowflake to Kinesis.

4. Integrating with legacy real-time systems:

Imagine an e-commerce company uses a recommendation system that listens to a Kinesis stream for user activity and adjusts product recommendations in real-time. While the latest user activity is directly sent to Kinesis, they also want to integrate user behavior insights derived from historical data stored in Snowflake. They stream segments of this historical data to Kinesis to refine their recommendation engine further.

5. Simulating real-time events for training and development:

Imagine a tech company that is developing a fraud detection system that uses machine learning models to detect anomalous transaction patterns from a Kinesis stream. They have historical transaction data with known fraud cases in Snowflake. By sending this data to Kinesis, they can simulate real-time fraudulent transactions to train and test their system.

6. Feed to third-party systems for compliances:

Imagine a pharmaceutical company that is bound by regulations to report drug testing results in real-time to a third-party system. This third-party system fetches data from a Kinesis stream. To ensure compliance, they send batches of their test results from Snowflake to Kinesis.

These are just a few use cases we can imagine. It is possible that you have a use case similar to one of these or maybe a totally different use case for sending data from Snowflake to Kinesis. If you have a different use case, we would love to hear about it, write to us.

Setting Up Your Snowflake Account

Before you can transfer data from Snowflake to Amazon Kinesis, you need to set up your Snowflake account. Here's a step-by-step guide:

Creating a Snowflake Account

To create a Snowflake account, you need to visit the Snowflake website and sign up. Provide the necessary information, such as your email address and preferred username. Once you've completed the registration process, you'll receive an email with further instructions to activate your account.

Configuring Your Snowflake Account

After creating your Snowflake account, you'll need to configure it by setting up your virtual warehouse, creating databases, and granting appropriate privileges to users. Snowflake provides comprehensive documentation that outlines the steps involved in configuring your account. Follow the instructions carefully to ensure a smooth setup process.

Setting Up Your Amazon Kinesis Account

Once you have your Snowflake account ready, it's time to set up your Amazon Kinesis account. Here's how you can do it:

Creating an Amazon Kinesis Account

To create an Amazon Kinesis account, go to the Amazon Web Services (AWS) website and sign in to your AWS account. Navigate to the Kinesis service and select "Create Kinesis Account." Follow the on-screen instructions to set up your account, providing any necessary billing information. Once you've completed the setup, your Kinesis account will be ready to use.

Configuring Your Amazon Kinesis Account

After creating your Amazon Kinesis account, you'll need to configure it by creating a Kinesis data stream and defining appropriate data retention settings. You may also need to set up Kinesis Data Firehose or Kinesis Data Analytics, depending on your specific needs. Refer to the AWS documentation for detailed instructions on configuring your Kinesis account.

Preparing Data for Transfer

Before you can transfer data from Snowflake to Amazon Kinesis, it's essential to understand data formats and prepare your data accordingly.

Understanding Data Formats

Snowflake supports various data formats, including JSON, CSV, Parquet, and Avro. When preparing your data for transfer, ensure that it is in a format compatible with both Snowflake and Amazon Kinesis. Transformation tools or scripts may be required to convert data into the desired format.

Preparing Your Data in Snowflake

Prior to transferring data, you may need to perform some data transformation or cleansing tasks in Snowflake. This ensures that the data is clean, consistent, and ready for analysis in Amazon Kinesis. Snowflake provides robust data manipulation capabilities, allowing you to apply complex transformations using SQL queries.

The Process of Sending Data from Snowflake to Amazon Kinesis

Sending data from Snowflake to Amazon Kinesis is not a standard operation provided by Snowflake's native capabilities. However, it's feasible by either using an intermediary application or writing a script or writing a serverless function using services such as AWS lambda. Use any of these methods to extract data from Snowflake tables on specified intervals and then publish it to Kinesis. For the purpose of this guide, we chose to do this using a Python script.

Here's a step-by-step approach to build your own Snowflake to Amazon Kinesis data pipeline.

Setting up your environment

Make sure your Snowflake and Amazon Kinesis accounts are ready for programmatically transferring the data.

1. Snowflake: Make sure you have the necessary permissions to execute queries and access the data you wish to export.
2. Amazon Kinesis: Create a Kinesis stream to which you will send the data. Note the stream name and AWS credentials.

Extracting data from Snowflake

Develop an application or script that uses the Snowflake JDBC or ODBC driver or a Snowflake client SDK (e.g., for Python) to query and extract data from Snowflake. If your programming language is not supported, you can directly make http requests to Snowflake SQL REST API endpoints. As we’re going to use Python for this article and Snowflake does provide an SDK for Python, we will use it in the sample Python code provided in the next section of this article. Our Python script will query the Snowflake datasets and extract the desired data.

Create a Kinesis data stream

In your Amazon Kinesis account, create a data stream to receive the data from Snowflake. Configure the stream with appropriate retention settings and choose the desired number of shards.

Write code to send extracted data to Amazon Kinesis

Amazon Kinesis provides SDKs in multiple languages to stream data to Amazon Kinesis. If your programming language is not supported, you can directly use AWS Kinesis Data Streaming Service to send data. As AWS SDK is available in Python, we will use it to push the extracted data to the Amazon Kinesis stream. Ensure that you batch the data appropriately for Kinesis's throughput limits.

Sample Python Implementation:

Here's a simple example using Python with the Snowflake Python SDK and AWS Python SDK:

PYTHON
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# Snowflake Python SDK docs - https://docs.snowflake.com/en/developer-guide/python-connector/python-connector

import snowflake.connector

# AWS Python SDK docs - https://aws.amazon.com/sdk-for-python/

import boto3




# Connect to Snowflake

conn = snowflake.connector.connect(

user='<YOUR_USER>',

password='<YOUR_PASSWORD>',

account='<YOUR_ACCOUNT>.snowflakecomputing.com',

warehouse='<YOUR_WAREHOUSE>',

database='<YOUR_DATABASE>',

schema='<YOUR_SCHEMA>'

)




# Query Data from Snowflake

cur = conn.cursor()

cur.execute('SELECT * FROM <YOUR_TABLE> WHERE ...') # Adjust the query as needed




# Connect to Kinesis

kinesis = boto3.client('kinesis', region_name='<YOUR_REGION>')




# Send Data to Kinesis Stream

for row in cur:

kinesis.put_record(

StreamName='<YOUR_KINESIS_STREAM>',

Data=str(row),

PartitionKey='<YOUR_PARTITION_KEY>' # Choose an appropriate partition key based on your use-case

)




# Clean Up

cur.close()

conn.close()

For complete automation, you can set up a cron job to run this script on desired intervals so that it will send the new data to Amazon Kinesis regularly. This is a basic example, and in a production scenario, you'd want to make it more robust by error handling, logging, etc.

Conclusion

In this article, we explored the process of sending data from Snowflake to Amazon Kinesis. We discussed the benefits of integrating Snowflake with Amazon Kinesis and provided step-by-step instructions for extracting data from Snowflake, and transferring it to Amazon Kinesis. Now, armed with this knowledge, you can efficiently send your valuable data from Snowflake to Amazon Kinesis.