Data Streaming in Snowflake

In this newsletter, we’ll provide an overview of the possibilities for streaming data using Snowflake.

The approaches discussed here focus on how Snowflake’s features and capabilities enable ingestion and processing data streams with a certain speed. The goal is to explore how businesses can create an advantage by using real time data to make time critical decisions, improve operational efficiency, and enhance customer experiences. Additionally, we’ll examine Snowflake’s architecture to enable a reliable solutions base, its suitability for streaming workloads, and the challenges it addresses.

What to Expect

You will gain an overview of the various built-in tools and techniques Snowflake provides to handle real time data streaming, including data ingestion, processing, and querying capabilities. We’ll cover Snowflake’s architectural components, such as tables, views, and processing capabilities, and explore how they are used to managing transactional workloads.

The main focus of this blog is to provide insights on how Snowflake enables real time data processing, the opportunities it presents, and the trade-offs to consider when implementing streaming solutions.

Real Time Data – an Overview

Real time data enables businesses to process information immediately after it is generated. Unlike traditional batch processing, which works with fixed intervals, real time data flows continuously, allowing for dynamic and immediate actions. This type of data processing has become increasingly important for organizations seeking to stay competitive in a fast-moving market.

Key Characteristics of Real Time Data

Real time data has distinct characteristics that differentiate it from other types of data flows:

• High Velocity: Data is processed at high speeds, often within milliseconds or seconds.
• Low Latency: Systems are designed to minimize delays, ensuring timely access to insights.
• Dynamic Streams: Continuous and often unpredictable data streams require flexible and scalable processing.

Each of these characteristics brings unique advantages and challenges to the table. For this reason, organizations need specialized tools and platforms to handle real time data effectively.

Why Real Time Data is Important

The value of real time data lies in its ability to provide immediate insights, which enable organizations to act without delay. It can significantly enhance several business areas, such as:

• Enhanced Decision-Making: Real time insights enable proactive responses, such as dynamically adjusting pricing or inventory based on live demand.
• Customer Personalization: By analyzing behavior in real time, companies can deliver tailored experiences that meet customer expectations in the moment.
• Operational Efficiency: Constant monitoring helps organizations to detect and address issues early, reducing downtime and optimizing performance.
• Risk and Fraud Detection: Rapid identification of anomalies or threats reduces exposure to fraud and operational risks.

The benefits of real time data are clear; however, its implementation comes with specific challenges, as detailed below.

Challenges with Real Time Data

While the advantages of real time data are significant, they are accompanied by a unique set of challenges:

• Handling Velocity and Volume: Processing large streams of data at high speed requires scalable systems.
• Ensuring Consistency: Maintaining data accuracy across distributed systems can be complex, especially when dealing with multiple sources.
• Integration Complexity: Real time data must often be combined with batch systems or legacy analytics, which introduces technical complexities.

The continuous flow and immediacy of real time data make it indispensable for businesses aiming to stay ahead in their industries. With the right tools and architecture, organizations can unlock its potential to deliver significant value and competitive advantage.

Real Time Data in Snowflake

Snowflake offers a modern approach to handling real time data by combining its cloud-native architecture with features designed for continuous ingestion, processing, and querying. With built-in tools like Snowpipe, streams, and tasks, Snowflake enables organizations to integrate real time data. In this section, we are going to explore how Snowflake manages real time data and its possibilities to meet the demands of streaming workloads.

Snowflake’s Architecture for Real Time Data

Snowflake’s architecture combines shared-disk and shared-nothing concepts. It uses a central, cloud-based data repository (the “shared-disk” portion) while compute resources (virtual warehouses) scale independently and process data in parallel (the “shared-nothing” aspect).

• Batch: Traditionally, data engineers batch large amounts of data into Snowflake using the COPY command or scheduled loads. The compute layer scales up for heavy loads and then scales back down for cost efficiency.
• Real Time: Continuous ingestion focuses on small, frequent data arrivals. The architecture still benefits from the separation of storage and compute, allowing Snowflake to handle real time feeds without blocking batch workloads or analytics queries.

Micro-partitions

Snowflake automatically divides tables into micro-partitions, compact storage units that group data based on natural ordering or load patterns.

• Batch: Micro-partitions shine when running large analytical queries, as Snowflake can prune out unnecessary partitions quickly, boosting performance for wide-ranging queries.
• Real Time: Frequent data loads generate more micro-partitions. The fine-grained organization remains beneficial, but you need to plan for slightly more overhead in terms of partition management, particularly if your real time data streams produce extremely high volumes.

Dynamic and Hybrid Tables

Speaking of tables, the next two entities are very helpful when considering processing real time data in Snowflake.

Dynamic Tables allow you to define continuous transformations within Snowflake. Think of them as similar to materialized views, but more flexible and with the ability to handle complex transformations and dependencies. You specify a SQL query for how the table should be built, and Snowflake automatically processes incremental changes from source tables behind the scenes.

• Incremental Data Processing: Instead of running ad-hoc or scheduled jobs, Dynamic Tables automatically update when new data arrives. You can set a target lag by specifying a refresh duration or defining a downstream dependency. Within the downstream dependency, the last dynamic table determines when the data needs to be refreshed.
• Continuous Pipelines: They reduce the need for manual orchestration with Snowflake Tasks or external job schedulers.
• Complex Transformations: Unlike materialized views (which are generally suited for simpler aggregates), Dynamic Tables can handle joins, window functions, and other advanced SQL operations.

Hybrid Tables are a newer concept that merges Snowflake’s columnar micro-partition storage with row-oriented features, making it easier to handle fast, high-volume inserts or updates. They aim to support both analytic (OLAP) and transactional (OLTP-like) workloads in a single Snowflake environment.

• Faster Row-Level Operations: Traditional Snowflake tables can handle inserts and updates at scale, but they’re optimized primarily for analytical reads. Hybrid Tables aim to make row-level operations more efficient.
• Mixed Workload Support: Combine real time event ingestion (often associated with row-level databases) and analytical querying (where column-based storage excels).
• Reduced Latency: By better handling small transactions and frequent data changes, Hybrid Tables can help lower the time it takes to get new data ready for querying.

Snowpipe and Snowpipe Streaming

Snowpipe is Snowflake’s continuous data ingestion service that loads data from external or internal stages. Typically, you point Snowpipe at a cloud storage location (e.g., Amazon S3) where new files land, and it automatically imports them.

• Classic Batch: The COPY command is often scheduled to run at fixed intervals (e.g., hourly or daily), which can introduce latency.
• Snowpipe: Instead of waiting for a scheduled batch, Snowpipe automatically pulls in smaller file increments soon after they arrive in the stage. This reduces the time between data generation and availability for queries.

However, Snowpipe is optimized for continuous loads of small files – think micro-batches, rather than large single-file loads. If your data volume spikes significantly, you might face higher costs or need to switch to different ingestion strategies to maintain throughput.

Snowpipe Streaming is an API-based approach that writes data directly to Snowflake tables, bypassing intermediate storage altogether. This can reduce latency and loading times even further than standard Snowpipe.

Why is it “More Real Time”? Data arrives instantly (or very close to it) in Snowflake, enabling near real time reporting. You don’t need to wait for batch files to land in cloud storage, and it’s often more cost-effective at scale, especially for high-frequency, small data events.

Additionally, the Snowflake Connector for Apache Kafka supports Snowpipe Streaming, allowing a near-seamless way to flow messages from Kafka topics straight into Snowflake tables. This integration is a significant step toward bridging the gap between streaming data platforms and Snowflake’s cloud data warehouse.

Snowflake Streams

Lastly, a Snowflake “Stream” tracks changes made to a table (inserts, updates, and deletes). This is particularly useful for Change Data Capture (CDC) workflows, enabling efficient processing of real time data.
You can design pipelines that react to these CDC streams, triggering transformations or downstream processes. This is ideal for scenarios where real time data updates must flow into transactional or operational systems.

Limitations and Possibilities of Real-Time Data in Snowflake

After breaking down some key factors, we will examine several limitations and possibilities when working with Snowflake and real time data.

While Snowflake aims for near real time processing, it isn’t designed as an ultra-low-latency, sub-second event-processing engine. Rapid, continuous ingestion can drive up costs if you’re not careful with warehouse sizing and auto-suspend settings. Each warehouse or streaming component could incur charges for frequent queries and data loads.

Monitoring multiple data streams, scaling them and managing their throughput can be complex. You’ll need robust DevOps and DataOps practices to ensure data integrity and consistent performance.

On the other hand, you can enable real time dashboards and analytics. With Snowpipe Streaming and the Kafka Connector, you can feed data directly into Snowflake and power dashboards that update within seconds or minutes. CDC streams enable event-driven pipelines where changes in your transactional databases trigger immediate actions in Snowflake, such as downstream transformations or alerts. Snowflake’s separation of storage and compute makes it easier to handle spiky workloads. Real time pipelines can scale up independently of other batch or analytical tasks, ensuring smooth parallel processing.

Many organizations mix real time flows with traditional batch loads. Snowflake’s architecture can accommodate both simultaneously without conflict.

Conclusion

By bringing together Hybrid Tables for row-based writes, Snowpipe Streaming for near real time ingestion, Snowflake Streams for continuous change detection, and Dynamic Tables for automated transformations, organizations can introduce real time data flows on top of their existing batch approach. This collaboration of Snowflake features opens up opportunities to capture high-velocity data and process it almost immediately, enabling decision-makers to access live metrics, personalize customer interactions as they happen, optimize operational processes, and detect anomalies or fraud with minimal delay. When combined with standard batch ingestion, this architecture preserves historical data and large-scale analytics while adding a complementary layer for low-latency, event-driven insights.

However, there are challenges and limitations to consider. More frequent writes can drive up compute costs, especially if data arrives at unpredictable volumes and velocities. Ensuring consistent data models becomes trickier if schema changes occur rapidly, as real time pipelines need to keep up with adjustments in source systems. Hybrid Tables, while excellent for rapid inserts, may not always yield the same query performance as columnar tables when it comes to large-scale analytical workloads; likewise, the cost overhead of always-on streaming pipelines can be significant if not carefully sized and monitored. Complexity also rises with more moving parts, and a robust DataOps or DevOps framework is critical for controlling ingestion, transformation, and monitoring across multiple modes of data processing.

Still, integrating these real time capabilities into an existing batch-loading architecture can be done gradually by setting up a dedicated real time pipeline that feeds into Snowflake alongside the traditional bulk loads. The new pipeline could ingest event data using Snowpipe Streaming and store it initially in Hybrid Tables, where Streams and Dynamic Tables can process changes in near real time. Historical data loads and heavy analytical queries would continue using the existing batch approach with columnar, micro-partitioned tables. Over time, teams can merge both approaches at the consumption layer—whether in BI dashboards, ML pipelines, or operational analytics—to combine the immediacy of real time data with the depth of historical context, all within the same Snowflake ecosystem.

– Jan Paul Langner (Scalefree)

Get Updates and Support

Please send inquiries and feature requests to [email protected]. 

For Data Vault training and on-site training inquiries, please contact [email protected] or register at www.scalefree.com.

To support the creation of Visual Data Vault drawings in Microsoft Visio, a stencil is implemented that can be used to draw Data Vault models. The stencil is available at www.visualdatavault.com.