Snowflake CDC: Basics, Scale Challenges, and Tools

To make the most of Snowflake, you’ll want to ingest data from your operational databases (such as MySQL, Postgres, or MongoDB) and make it available for analytics. The most common way to do this while ensuring data freshness and accuracy is through change data capture (CDC).

The article below highlights the role of CDC in data synchronization, challenges in scaling CDC processes, and a comparative analysis of various CDC solutions for loading data into Snowflake.

Understanding Snowflake CDC – the What and Why

Change Data Capture (CDC) is a method used in data engineering to efficiently identify and capture changes made to a source dataset. This approach ensures that only the altered data is transferred to the target system, significantly reducing data volume, improving data freshness, and overall enhancing the efficiency of data synchronization. Different CDC methods can are used in database replication processes.

When it comes to Snowflake, implementing CDC can bring about transformative benefits. Snowflake’s architecture, designed for elasticity and scalability, makes it an ideal platform for processing and analyzing large volumes of changing data. By integrating CDC into Snowflake, organizations can achieve real-time or near-real-time data updates, enhancing the timeliness and relevance of their analytics.

While starting is straightforward, scaling up introduces challenges such as handling increased data volume and complexity, requiring strategies like incremental loading and advanced monitoring. Various CDC solutions, including open-source tools like Debezium and platforms such as Upsolver, cater to different needs and complexities, making careful evaluation essential for choosing the right solution.

Why Ingest CDC Data into Snowflake?

1. Enabling real-time analytics: CDC allows incremental data updates, providing analysts and data scientists access to fresh data to power real-time dashboards and machine learning models. This improves the relevance and timeliness of analytics.
2. Efficiency and cost savings: By only dealing with the changed data, there’s a significant reduction in the volume of data being loaded and processed, leading to cost savings in storage and compute resources.
3. Data integrity and consistency: By capturing changes at the source and applying them to the target, CDC maintains synchronization and consistency between systems, improving data accuracy and reliability for downstream processes

Typical Data Sources for CDC Ingestion

CDC is commonly employed with operational databases such as:

Transactional databases like MySQL, PostgreSQL, and Microsoft SQL Server, where it’s crucial to capture every change to maintain the integrity of transactional records.

Learn more: MongoDB to Snowflake (recorded workshop)

NoSQL databases such as MongoDB, which are often used for applications with rapidly changing data.

Learn more: SQL Server to Snowflake (recorded workshop)

Event streams: such as Kafka, which are central to event-driven architectures and can be used to stream CDC data.

Learn more: Kafka to Snowflake (article)

A quick demo video – building a CDC pipeline to Snowflake

To understand the types of challenges you might encounter in database replication (and how we solve them at Upsolver), check out the video below:

The Challenge: Easy to Start, Hard to Scale

Implementing CDC in Snowflake offers a straightforward path to begin leveraging real-time data synchronization. However, as organizations scale, the complexities and challenges around high-volume CDC data processing become increasingly apparent. These incldue:

1. Increased data volume and velocity: As businesses grow, so does the volume and velocity of their data. Handling terabytes of rapidly changing data requires robust and scalable data pipelines. This surge can strain the CDC process, leading to potential bottlenecks in data transfer and processing.
2. Complexity in data transformation: With scale, the complexity of data transformation escalates. The need to cleanse, aggregate, join, and enrich data in real-time or near-real-time becomes a formidable task, demanding more sophisticated approaches and resources.
3. Maintaining data consistency: Ensuring data consistency across various systems becomes increasingly challenging. The risk of data drift, where the data in the source and the target becomes out of sync, escalates with the scale of operations.
4. Resource optimization: Balancing compute and storage resources efficiently becomes crucial. Incorrect resource allocation can lead to inflated costs or under-utilized systems, impacting the overall performance of the CDC process.
5. Error handling and recovery: As data pipelines become more complex, the likelihood of errors increases. Developing robust error handling and recovery mechanisms is essential to maintain continuous data flow and integrity.
6. Monitoring and observability: Effective monitoring of CDC processes is vital to detect issues early and respond promptly. However, achieving this at scale requires a more sophisticated set of tools and practices.

Popular CDC Solutions for Snowflake

Three of the most popular tools to replicate databases into Snowflake are:

1. Open source Debezium
2. General-purpose ELT tools (Airbyte, Fivetran)
3. Upsolver

1. Open Source Debezium

Read the full guide: Debezium for CDC – Benefits and Pitfalls

Debezium provides a solid foundation for change data capture through its database connectors and streaming architecture. However, using Debezium in production involves significant operational challenges related to resilience, scale, and end-to-end pipeline management from source database to target data store

Pros:

• Flexibility and lightweight architecture
• Low latency streaming
• Easy to integrate into modern data stacks
• Support for a wide range of databases
• Open source and community backed
• Handles some schema change automation

Cons:

• Requires engineering work for data ingestion into targets
• Additional effort needed for ensuring reliability and availability
• Reliance on Kafka which adds infrastructure complexity
• Typical open source maintenance challenges (upgrades, security, etc.)

2. General Purpose ELT Tools 

Read the full article: Airbyte vs Fivetran vs Upsolver – Which is the Best CDC Tool?

Universal data pipeline tools such as Airbyte and Fivetran offer broad connectivity across hundreds of sources and destinations. They excel at syncing SaaS applications and automating API data extractions. While convenient for lightweight ELT workflows, the generalized nature of Airbyte and Fivetran make them less ideal for mission-critical, high-volume streaming use cases that require specialized data engineering. Their pricing models can also drive costs up quickly.

Pros:

• Large repository of pre-built connectors
• Managed services with some vendor support

Cons:

• Not optimized for large-scale, continuous streams
• Significant costs for data warehousing and transformations
• More suited for smaller data volumes

3. Upsolver

Request a demo of Upsolver

Upsolver provides a fully managed CDC solution optimized for streaming database changes to data lakes and warehouses. It delivers simplicity, fresh data, and built-in transformations without the overhead of Kafka or Spark. Benefits include:

• Cloud-native architecture designed for scale
• Simplifed management for continuous pipeline via low-code or SQL
• Real-time cleansing and aggregation
• Output to multiple destinations

Upsolver replicates databases with automatic schema management, handles large data volumes, and enables data engineers to easily transform and enrich change streams on the fly. The result is fresh, prepared data delivered on your terms. 

Key Considerations for Choosing a CDC Solution

1. Scale and Complexity: Consider the volume of data and the complexity of your CDC requirements. Solutions like Upsolver are better suited for high-scale, complex scenarios.
2. Data Sources and Destinations: Assess the variety of data sources and destinations involved. Open-source tools offer broad compatibility, while Snowflake table streams are limited to internal changes.
3. Technical Expertise: Evaluate the level of technical expertise available. Open-source solutions require more hands-on management, while tools like Airbyte provide a more user-friendly interface.
4. Budget and Resources: Consider budget constraints and resource availability. Open-source tools can be cost-effective but may require more time and expertise to manage.
5. Customization Needs: Determine the level of customization needed. Open-source tools offer more flexibility, while proprietary solutions might offer less customization but are easier to use.

Strategies for Scaling CDC in Snowflake

1. Incremental loading: Instead of bulk loading, incremental loading of only changed data can significantly reduce the load on the system, making the process more manageable.
2. Optimizing data transformations: Employing tools and techniques that allow for efficient data transformations – like stream processing or batch processing depending on the use case – can help manage complex data transformations at scale.
3. Resource management: Leveraging Snowflake’s ability to dynamically scale compute resources ensures that the system can handle varying loads efficiently without incurring unnecessary costs.
4. Implementing robust error handling: Establishing comprehensive error logging, alerting mechanisms, and automated recovery processes can help quickly identify and resolve issues, maintaining the integrity of the CDC process.
5. Advanced monitoring tools: Utilizing advanced monitoring and observability tools specifically designed for large-scale data operations can provide deeper insights and proactive management of the CDC pipelines.
6. Scalable architecture design: Designing a scalable architecture, which may include partitioning data and parallel processing, ensures that the system can accommodate growth without significant re-engineering.

Next Steps

Learn more about Upsolver for Snowflake CDC