Published in: Snowflake Cloud Data Solutions

Efficiently Managing Dynamic Tables in Snowflake for Real-Time Data and Low-Latency Analytics

Author Deepinder

Published on: April 11, 2025

Managing Dynamic Tables in Snowflake: Handling Real-Time Data Updates and Low-Latency Analytics

In this data-driven environment, businesses aim to use the potential of real-time information. Snowflake’s dynamic tables stand out as a valuable feature for enabling near-real-time data changes and low-latency analytics. However, effectively maintaining these dynamic tables presents technological issues that, if not handled, might impede performance and raise expenses.

This article digs into the challenges of handling dynamic tables in Snowflake, provides techniques for dealing with them, and contains practical code examples to help you apply these solutions successfully.

What Are Dynamic Tables in Snowflake?

Dynamic tables make data engineering in Snowflake easier by offering a dependable, cost-effective, and automated approach to changing data. Instead of handling transformation stages using tasks and scheduling, you specify the final state using dynamic tables and let Snowflake run the pipeline.

Here’s why they are helpful:

Declarative programming: Using declarative SQL, you may define pipeline results without thinking about the stages involved, decreasing complexity.
Transparent orchestration: It allows you to easily design pipelines of various types, from linear chains to directed graphs, by chaining dynamic tables together. Snowflake handles the coordination and scheduling of pipeline refreshes depending on your data freshness goals.
Performance benefits with incremental processing: For workloads that are well-suited to incremental processing, dynamic tables can outperform complete refreshes.
Easy switching: With a single ALTER DYNAMIC TABLE statement, you may smoothly go from batch to streaming. You may choose how frequently data is refreshed in your pipeline, which helps to balance cost and data freshness.
Operationalization: Dynamic tables are completely visible and controllable in Snowsight, with programmatic access to create your own observability applications.

A dynamic table reflects query results. Thus, there is no need for a separate target table or bespoke code for data transformation. An automatic mechanism updates the findings regularly using scheduled refreshes. DML operations cannot be used to alter the content of a dynamic table since the query itself determines it. The automatic refresh mechanism converts query results into a dynamic table.

How Dynamic Tables Work?

When you create a dynamic table, you describe the query that will change data from one or more base objects or dynamic tables. An automatic refresh procedure runs this query on a regular basis, updating the dynamic table with any changes made to the base objects.

Source

This automatic method calculates the modifications made to the base items and combines them into the dynamic table. The process does this job using computing resources associated with the dynamic table. For additional information on resources, see Understanding the cost of dynamic tables.

When creating a dynamic table, specify the data’s “freshness” (goal latency). For example, you may specify that the data should not exceed five minutes behind the base table’s changes. Based on this freshness goal, the automatic procedure schedules refresh to keep the data in the dynamic table up to date within this time frame (within five minutes of base table modifications).

If the data doesn’t need to be as fresh, you can choose a longer target freshness time to save money. For example, if the data in the target table only has to be one hour behind the updates to the base tables, you may set a goal of one hour (rather than five minutes) to save money.

Challenges in Managing Dynamic Tables

Although dynamic tables provide significant versatility, they present certain issues.

Data Consistency: Ensuring precision and preventing outdated information during frequent changes.
Resource Consumption: Frequent changes may result in elevated computational expenses.
Complex Query Optimization: Reconciling low-latency analytics with query efficacy.

To address these problems, a comprehensive strategy incorporating incremental updates, efficient data pipelines, and resource optimization is essential.

Strategies for Effectively Managing Dynamic Tables

1. Incremental Data Loading

Processing just the new or modified data, rather than reloading the complete dataset, is fundamental to effective dynamic table management.

Example

The following illustrates the implementation of incremental updates in Snowflake:

				
					-- Create a staging table for new or updated records
CREATE OR REPLACE TEMP TABLE staging_table (
    id INT,
    data_usage DECIMAL,
    update_time TIMESTAMP
);

-- Insert new data into the staging table
INSERT INTO staging_table (id, data_usage, update_time)
VALUES (1, 1024, CURRENT_TIMESTAMP),
       (2, 2048, CURRENT_TIMESTAMP);

-- Merge staging table into the dynamic table
MERGE INTO dynamic_table AS target
USING staging_table AS source
ON target.id = source.id
WHEN MATCHED THEN
    UPDATE SET target.data_usage = source.data_usage, target.update_time = source.update_time
WHEN NOT MATCHED THEN
    INSERT (id, data_usage, update_time)
    VALUES (source.id, source.data_usage, source.update_time);

This method guarantees that only essential data is dealt with hence minimizing resource usage and enhancing update speed.

2. Optimizing MERGE Operations

Frequent MERGE procedures can be resource-demanding, particularly when managing extensive datasets.

Optimal Strategies for MERGE:

Employ filters to process just pertinent records.
Index essential columns to expedite lookups.

				
					-- Optimized MERGE with filters
MERGE INTO dynamic_table AS target
USING staging_table AS source
ON target.id = source.id AND source.update_time > target.update_time
WHEN MATCHED THEN
    UPDATE SET target.data_usage = source.data_usage
WHEN NOT MATCHED THEN
    INSERT (id, data_usage, update_time)
    VALUES (source.id, source.data_usage, source.update_time);

This query enhances efficiency by incorporating a filter on update_time, so preventing superfluous updates.

3. Partitioning and Clustering

Partitioning and clustering enhance query performance by structuring data to reduce scan duration.

Code Example: Clustering a Table

				
					-- Cluster the dynamic table on frequently queried columns
ALTER TABLE dynamic_table
CLUSTER BY (update_time, id);

This guarantees that queries filtering by update_time and id may swiftly identify pertinent data, hence minimizing latency.

4. Utilizing Materialized Views for Analytical Purposes

Materialized views offer a pre-calculated result set, enhancing query efficiency for recurrent queries.

Code Example: Establishing a Materialized View

				
					-- Create a materialized view for frequently accessed data
CREATE MATERIALIZED VIEW customer_usage_summary AS
SELECT id, SUM(data_usage) AS total_usage
FROM dynamic_table
GROUP BY id;

Materialized views are refreshed automatically, guaranteeing updated analytics with low resource expenditure.

Best Practices for Optimizing Performance

To enhance the efficiency of your dynamic tables, it is essential to comprehend the system, conduct experiments, and refine your approach depending on the outcomes. For instance:

Formulate strategies to enhance your data pipeline considering cost, data latency, and reaction time requirements.

Put the following into practice:

Commence with a compact, static dataset to expedite query development.
Evaluate performance with dynamic data.
Adjust the dataset to ensure it satisfies your requirements.
Modify your workload in accordance with the findings.
Reiterate as necessary, prioritizing actions that have the highest performance effect.

Furthermore, downstream lag can effectively control refresh dependencies among tables, guaranteeing that refreshes occur just when required. Refer to the performance documentation for further details.

Conclusion

Dynamic tables in Snowflake offer an exceptional foundation for managing real-time data changes and low-latency analytics. By adhering to best practices like incremental updates, streamlining MERGE processes, and utilizing materialized views, one may surmount hurdles and fully harness the capabilities of Snowflake for business requirements.

Using appropriate methodologies may guarantee constant, high-performance analytics, even in the most challenging real-time settings. Explore Snowflake’s documentation and try these strategies to improve your data operations.

If you’re ready to embark on this journey and need expert guidance, subscribe to our newsletter for more tips and insights, or contact us at Offsoar to learn how we can help you build a scalable data analytics pipeline that drives business success. Let’s work together to turn data into actionable insights and create a brighter future for your organization.