TL;DR

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:

1. set up Snowflake as a source connector (using Auth, or usually an API key)
2. set up Apache Iceberg as a destination connector
3. define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Snowflake

Snowflake Data Cloud is a cloud-based data warehousing and analytics platform that allows organizations to store, manage, and analyze large amounts of data in a secure and scalable manner. It provides a single, integrated platform for data storage, processing, and analysis, eliminating the need for multiple tools and systems. Snowflake Data Cloud is built on a unique architecture that separates compute and storage, allowing users to scale up or down as needed without affecting performance. It also offers a range of features such as data sharing, data governance, and machine learning capabilities, making it a comprehensive solution for modern data management and analytics.

What is Apache Iceberg

For huge analytical tables, Apache Iceberg is a high-performance format. Using Apache Iceberg, engines such as Spark, Trino, Flink, Presto, Hive and Impala can safely work with the same tables, at the same time, providing the reliability and simplicity of SQL tables to big data. With Apache Iceberg, you can merge new data, update existing rows, and delete specific rows. Data files can be eagerly rewritten or deleted deltas can be used to make updates faster.

{{COMPONENT_CTA}}

Prerequisites

1. A Snowflake account to transfer your customer data automatically from.
2. A Apache Iceberg account.
3. An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Snowflake and Apache Iceberg, for seamless data migration.

When using Airbyte to move data from Snowflake to Apache Iceberg, it extracts data from Snowflake using the source connector, converts it into a format Apache Iceberg can ingest using the provided schema, and then loads it into Apache Iceberg via the destination connector. This allows businesses to leverage their Snowflake data for advanced analytics and insights within Apache Iceberg, simplifying the ETL process and saving significant time and resources.

Methods to Move Data From Snowflake to apache iceberg

• Method 1: Connecting Snowflake to apache iceberg using Airbyte.
• Method 2: Connecting Snowflake to apache iceberg manually.

Method 1: Connecting Snowflake to apache iceberg using Airbyte

Step 1: Set up Snowflake as a source connector

1. First, you need to have a Snowflake Data Cloud account and the necessary credentials to access it.

2. Once you have the credentials, go to the Airbyte dashboard and click on "Sources" on the left-hand side of the screen.

3. Click on the "Create a new source" button and select "Snowflake Data Cloud" from the list of available sources.

4. Enter a name for your Snowflake Data Cloud source and click on "Next".

5. In the "Connection" tab, enter the following information:  
- Account name: the name of your Snowflake account  
- Username: your Snowflake username  
- Password: your Snowflake password  
- Warehouse: the name of the warehouse you want to use  
- Database: the name of the database you want to use  
- Schema: the name of the schema you want to use

6. Click on "Test connection" to make sure that the connection is successful.

7. If the connection is successful, click on "Next" to proceed to the "Configuration" tab.

8. In the "Configuration" tab, select the tables or views that you want to replicate and configure any necessary settings.

9. Click on "Create source" to save your Snowflake Data Cloud source and start replicating data.

Step 2: Set up Apache Iceberg as a destination connector

1. Open the Airbyte platform and navigate to the "Destinations" tab on the left-hand side of the screen.
2. Click on the "Apache Iceberg" destination connector and select "Create new connection."
3. Enter a name for your connection and provide the necessary credentials for your Apache Iceberg database, including the host, port, database name, username, and password.
4. Test the connection to ensure that it is successful. 5. Select the tables or data sources that you want to replicate to your Apache Iceberg database.
6. Configure any additional settings or options for your connection, such as the frequency of data replication or any transformations that you want to apply to your data.
7. Save your connection and start the replication process.
8. Monitor the progress of your data replication and troubleshoot any issues that may arise.
9. Once the replication process is complete, verify that your data has been successfully replicated to your Apache Iceberg database.
10. Use your Apache Iceberg database to analyze and query your data as needed.

Step 3: Set up a connection to sync your Snowflake data to Apache Iceberg

Once you've successfully connected Snowflake as a data source and Apache Iceberg as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
2. Choose your source: Select Snowflake from the dropdown list of your configured sources.
3. Select your destination: Choose Apache Iceberg from the dropdown list of your configured destinations.
4. Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
5. Select the data to sync: Choose the specific Snowflake objects you want to import data from towards Apache Iceberg. You can sync all data or select specific tables and fields.
6. Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Snowflake to Apache Iceberg according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your Apache Iceberg data warehouse is always up-to-date with your Snowflake data.

Method 2: Connecting Snowflake to apache iceberg manually

Moving data from Snowflake to Apache Iceberg without using third-party connectors or integrations involves several steps, including extracting data from Snowflake, transforming it into a compatible format for Apache Iceberg, and finally loading the data into an Iceberg table.

Here’s a step-by-step guide to accomplish this task:

Step 1: Prepare Your Apache Iceberg Environment

Before you start moving data, ensure that you have an Apache Iceberg environment set up and ready to receive data.

1. Set Up a Hadoop/Spark Cluster: Apache Iceberg can integrate with compute engines like Spark. Make sure you have a Hadoop/Spark cluster running.
2. Install Iceberg: Install Apache Iceberg on your Spark cluster. You can do this by adding Iceberg’s library to Spark’s classpath.
3. Create an Iceberg Table: Define the schema and partitioning strategy for your Iceberg table, and create the table using Spark SQL or the Iceberg API.

Step 2: Extract Data from Snowflake

1. Query Data: Write a SQL query to select the data you want to export from Snowflake.
2. Export to CSV/JSON/Parquet: Use Snowflake’s data unloading capabilities to export the data into a file format that is compatible with Apache Iceberg, such as CSV, JSON, or Parquet. This can be done using the COPY INTO <location> command in Snowflake.
3. Store the Data: Store the exported data files in a location accessible to your Spark cluster, such as Amazon S3, HDFS, or a local filesystem.

Step 3: Transform Data (If Required)

Depending on the format you’ve chosen to export data from Snowflake, you may need to transform it to match the schema of your Iceberg table.

1. Read Data into Spark: Use Spark to read the data files into a DataFrame.
2. Transform Data: Apply any necessary transformations to the DataFrame to match the schema and partitioning strategy of your Iceberg table.
3. Write Data to Parquet (If Needed): If you’ve exported data in CSV or JSON format, convert it to Parquet, which is more efficient for Iceberg.

Step 4: Load Data into Apache Iceberg

1. Write DataFrame to Iceberg: Use Spark’s DataFrameWriter API to write the DataFrame to the Iceberg table.

df.write

  .format("iceberg")

  .mode("append") // Use "overwrite" if you want to replace existing data

  .save("path_to_your_iceberg_table")

2. Refresh Table: After loading, refresh the Iceberg table to ensure that the metadata is updated and your data is visible.

Step 5: Validate Data Transfer

1. Query Iceberg Table: Use Spark SQL to query the Iceberg table and validate that the data has been transferred correctly.
2. Check Data Integrity: Compare the results of similar queries run on Snowflake and Iceberg to ensure data integrity.

Step 6: Clean Up

1. Remove Temporary Files: If you created any temporary files or directories during the data transfer process, clean them up to prevent storage waste.
2. Monitor Performance: Keep an eye on the performance of your Spark jobs and the Iceberg table. Adjust configurations as needed for optimal performance.

Step 7: Automate the Process (Optional)

Once you have successfully moved data manually, you might want to automate the process for recurring data transfers.

1. Scripting: Write scripts to automate the export from Snowflake, transformation, and loading into Iceberg.
2. Scheduling: Use a job scheduler like Apache Airflow, Oozie, or a cloud service equivalent to schedule the data transfer jobs.

Notes:

• Ensure that you have the necessary permissions to access both Snowflake and the storage used by Apache Iceberg.
• Be mindful of the data volume and network throughput, as transferring large datasets can be time-consuming and may incur costs.
• Consider incremental data loads if you are dealing with frequently updated datasets to optimize the data transfer process.
• Always test your data migration process with a subset of data before moving the entire dataset.

By following these steps, you can manually move data from Snowflake to Apache Iceberg without relying on third-party connectors or integrations.

Use Cases to transfer your Snowflake data to Apache Iceberg

Integrating data from Snowflake to Apache Iceberg provides several benefits. Here are a few use cases:

1. Advanced Analytics: Apache Iceberg’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Snowflake data, extracting insights that wouldn't be possible within Snowflake alone.
2. Data Consolidation: If you're using multiple other sources along with Snowflake, syncing to Apache Iceberg allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
3. Historical Data Analysis: Snowflake has limits on historical data. Syncing data to Apache Iceberg allows for long-term data retention and analysis of historical trends over time.
4. Data Security and Compliance: Apache Iceberg provides robust data security features. Syncing Snowflake data to Apache Iceberg ensures your data is secured and allows for advanced data governance and compliance management.
5. Scalability: Apache Iceberg can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Snowflake data.
6. Data Science and Machine Learning: By having Snowflake data in Apache Iceberg, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
7. Reporting and Visualization: While Snowflake provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to Apache Iceberg, providing more advanced business intelligence options. If you have a Snowflake table that needs to be converted to a Apache Iceberg table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

1. Configure a Snowflake account as an Airbyte data source connector.
2. Configure Apache Iceberg as a data destination connector.
3. Create an Airbyte data pipeline that will automatically be moving data directly from Snowflake to Apache Iceberg after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!