How to implement incremental loading in Snowflake using Stream and Merge?

Incremental loading refers to the process of selectively loading and updating only the new or changed data since the last load, rather than reloading the entire dataset. This approach is commonly used in data integration scenarios where you have a large dataset, and you want to minimize the amount of data transferred and processed during each data update.

The main advantages of incremental loading include:

1. Efficiency: Incremental loading reduces the amount of data transferred between systems, making the data integration process more efficient. This is particularly important when dealing with large datasets.
2. Faster Updates: Since only the changed or new records are processed, incremental loading typically results in faster update times compared to reloading the entire dataset.
3. Reduced Resource Usage: Incremental loading minimizes the impact on system resources, such as network bandwidth, storage, and processing power.

Incremental loading is widely used in data warehousing, business intelligence, and data integration scenarios to keep data up-to-date with minimal impact on resources. The implementation details may vary depending on the tools, databases, and platforms involved in the data integration process.

Incremental loading involves updating a dataset with only the new or changed data since the last load, rather than reloading the entire dataset. The specific approach you use depends on the characteristics of your data and the tools at your disposal.

Here are some common strategies for incremental loading:

Timestamp or Date-Based Incremental Loading:

• Include a timestamp or date column in your data to track when records were last modified.
• During each update, retrieve records with timestamps or dates greater than the maximum timestamp or date from the previous load.

Change Data Capture (CDC):

• Implement a mechanism to capture changes in the source data. This could involve using triggers, database logs, or tracking columns
• Identify and load only the changed records during each update.

Flag-Based Incremental Loading:

• Introduce a flag column in your data to mark records that have been added or modified.
• During each update, process only the records with specific flag values.

Log -Based Incremental Loading:

• Utilize transaction logs or change logs from the source system to identify changes.
• Query the logs to determine the added or modified records since the last load.

Example pseudo-code (assuming a function get Changes that retrieves changes from logs):

Hash-Based Incremental Loading:

• Generate a hash for each record based on its attributes.
• Compare the hashes to identify records that have changed.

Example SQL query (assuming a hash column named record_hash):

API or Webhook-Based Incremental Loading:

• If your data source provides APIs or web hooks, use them to fetch only the new or changed data since the last load.
• This is common in scenarios involving external services or online platforms.

Choose the strategy that best fits your data and system architecture. Additionally, consider factors such as performance, data integrity, and the capabilities of the tools and platforms you are using for data loading. Always test your incremental loading process thoroughly to ensure accuracy and efficiency.

How do you incrementally load data into a Snowflake with DBT?

In dbt (data build tool), you can incrementally load data into Snowflake using a technique called "incremental models." Incremental models allow you to update only the new or changed data in your destination (Snowflake) rather than reloading the entire dataset. Here's a general outline of the process:

Identify Incremental Key:

Identify a column or set of columns that can be used as an incremental key to determine which records are new or have been updated since the last run. This is typically a timestamp or a unique identifier for each record.

Create a Staging Table:

Create a staging table in Snowflake where you'll load the raw data. This table will hold the new and changed data from your source.

Create an Incremental Model:

Write a dbt model that selects data from the staging table and performs any necessary transformations. Use the incremental key identified earlier to filter only the new or changed record.

In the example above, 'id' is used as the unique key to identify new or changed records.

Run dbt Models:

Run your dbt models using the dbt run command. Snowflake incremental dbt will take care of executing the incremental logic and updating the target tables accordingly.