How to Create Snowflake Iceberg tables in using Coalesce.io ?.

What is Snowflake Iceberg table?

A Snowflake Iceberg table is a type of table that integrates Snowflake with Apache Iceberg, an open table format designed to handle large-scale data stored in data lakes. Snowflake Iceberg tables allow Snowflake users to leverage Apache Iceberg's powerful features, such as efficient data storage, advanced partitioning, schema evolution, and data versioning. This integration effectively transforms Snowflake into a data lakehouse solution, combining the best aspects of data lakes and data warehouses.

The benefits of Snowflake Iceberg tables include:

1. Flexibility for Evolving Data Models

• Schema Evolution: Snowflake Iceberg tables allow for schema changes, like adding or renaming columns, without rewriting or reloading data. This flexibility supports data model evolution and makes it easier to adapt to changing business requirements.
• Time Travel and Versioning: Iceberg tables maintain snapshots of data, allowing users to query historical data versions. This is useful for auditing, compliance, or tracking changes over time.

2. ACID Compliance and Data Integrity

• Reliable Transactions: Snowflake Iceberg tables support ACID transactions, ensuring that data operations are consistent, reliable, and free from partial failures.
• Data Consistency: With ACID compliance, Iceberg tables ensure that concurrent users get consistent results, making them suitable for mission-critical applications where data accuracy is paramount.

3. Seamless Multi-Engine Access

• Interoperability with Other Tools: Apache Iceberg is designed to be engine-agnostic, so data in Snowflake Iceberg tables can also be accessed by other compute engines like Spark and Presto.
• Unified Data Experience: Organizations can leverage multiple analytics tools on the same data, providing flexibility for data engineering, data science, and analytics teams to work on a unified dataset.

4. Data Lakehouse Capability

• Unified Data Management: Snowflake Iceberg tables allow Snowflake to serve as a data lakehouse, combining the flexibility of a data lake with the reliability and performance of a data warehouse.
• Support for Structured and Semi-Structured Data: With Iceberg tables, Snowflake can manage and analyze structured and semi-structured data within the same environment, reducing complexity.

High Performance and Query Efficiency

• Partition Pruning: Iceberg tables support advanced partitioning and pruning, allowing Snowflake to scan only relevant parts of the data, which significantly improves query performance.
• Optimized File Storage: By efficiently organizing data in large files and minimizing metadata load, Iceberg tables ensure that even massive datasets are easy to access and quick to query.
• Automatic Compaction: This feature consolidates fragmented data, improving query efficiency and reducing the time required to retrieve data.

Now that we understand Snowflake iceberg tables and their benefits, let’s look at how to create iceberg tables in Coalesce.io.

Coalesce.io being a data transformation and orchestration platform designed to streamline the development, management, and automation of data pipelines . is is built to work with modern cloud data warehouses like Snowflake, Coalesce.io provides a no-code and low-code environment, enabling users to create, schedule, and monitor data transformations with ease.

In this article, I’ll demonstrate how to create an Iceberg table in Snowflake using Coalesce.io. For this example, I’ll walk through a simple scenario: processing customer data in a pipeline. After cleansing the source data, I’ll write the cleaned data into a Snowflake Iceberg table.

Step 1: Adding a Source Table

a. Launch Development Workspace: Start by launching your development workspace in Coalesce.io.

b. Add Source Table:

- In the top-left corner, click the (+) icon.

- A small pane will open with two options: "Add Source" and "Create New Node".

- Select "Add Source" to begin adding a source table in your development workspace.

- A new tab will open, displaying a list of all schemas and tables available in the connected Snowflake database on the left-hand side. From this list, select the table you want to use as your source table. In this example, we'll choose the "CUSTOMER_MASTER" table as our source. Once you've selected the table, click the "Add Source" button at the bottom-right corner to finalize the addition

After completing the previous step, you’ll see a source node named "CUSTOMER_MASTER" added to your development workspace. This node represents the source table we will use to create your icerberg table.

In this example, the customer data my pipeline receives from the source is in XML format. To create an Iceberg table, I will first transform the XML data into a tabular format.

Coalesce.io offers an easy way to transform data from XML and JSON files. With a simple one-click feature, data can be converted into a tabular format. To first transform the XML data into a tabular structure, we’ll add a stage node following the source node in our pipeline.

To extract column metadata from the XML data, simply right-click on the attribute containing the customer data, then select Derive Mapping and choose From XML.

Coalesce.io will derive attribute metadata from the source data and automatically generate a transformation to extract data from the XML file into individual attributes.

We can now drop the first attribute from the node and click on the create button.Coalesce.io will create a staging table in snowflake.

Step 1: Creating a Snowflake icerberg table

Now that we’ve extracted the data from the XML source into a staging table in Snowflake, the next step is to apply a transformation and write the data into an Iceberg table in Snowflake.

To create a iceberg table in our pipeline, Coalesce.io offers an easy-to-use node that can be configured with just a few clicks and by setting a few parameters. One of the key benefits of Coalesce.io is its low-code or no-code platform, which simplifies the process of building data transformations and workflows, allowing users to focus on data rather than coding. This accessibility makes it easier for teams to collaborate and quickly implement iceberg tables without extensive programming knowledge in coalesce.io .

click here to get the iceberg node package and how to install package in coalsce interface from coalesce.io.

Now that we have installed a iceberg table node in our workspace, we can use it to create a iceberg table in our project. To create a iceberg table, follow the steps below:

a. Right-click on the staging node that we have added in your workspace in Step 1, then select the "Snowflake Iceberg table" option under "Add Node" > "Iceberg-Tables"

b. Once we select this option, a iceberg table node named "ICT_CUSTOMER_MASTER" will be added to our workspace. You can change the name of the node by clicking the edit button next to it.

c. Now, we will configure our icerberg table node by setting the properties for our icerberg table. Click on "Node Properties" in the top right corner.

Under the "Node Properties" section, find the "Storage Location" configuration. Select the desired storage location or schema name where you want your iceberg table to be created. In this example, we will choose the "Conformed" storage location, which corresponds to a conformed schema in our Snowflake database. This ensures that the iceberg table is created in the appropriate schema in our project.

d. In a next step, click on the "Iceberg Option". Under this option you will find a different configuration options.

• Type Of Catalog :- Specify the type of catalog.

Snowflake

Polaris

• Snowflake External Volume Name:-Specifies the identifier (name) for the external volume where the Iceberg table will store its metadata files and data in Parquet format. An external volume must be created in Snowflake beforehand as a prerequisite..
• Base Location Name:- The path to a directory where Snowflake can store data and metadata files for the table. This should be specified as a relative path from the table’s EXTERNAL_VOLUME location.
• Clsuter key:- True/False determines whether the Iceberg table will be clustered.

- True: This allows you to specify the column on which clustering will be applied.

- Allow Expressions Cluster Key: When set to True, it allows you to add an expression to the specified cluster key.

- False: Indicates that no clustering will be performed.

After setting the appropriate values for the configuration, click the Create button. This action will create the Iceberg table in Snowflake.

Once the table is created, click the Run button to process the data in the development environment. After the node execution is complete, you will be able to see the data in the external cloud. In this article, I used an AWS S3 bucket as the external cloud storage.

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