Kafka to Snowflake: What’s the Best Ingestion Method?

Moving data from Kafka to Snowflake can help unlock the full potential of your real-time data. Let’s look at the ways you can turn your Kafka streams into Snowflake tables, and some of the tradeoffs involved in each.

Why move your data from Kafka to Snowflake?

Apache Kafka is widely used for real-time data streaming and high throughput, low latency processing. Kafka is built as a distributed event streaming platform with a robust publish-subscribe (a.k.a. “pub-sub”) model and strong fault tolerance. This makes it well-suited for continuously moving logs, metrics, or other event data that requires immediate processing.

Kafka generates high-velocity, high-volume, semi-structured data. Businesses want to make that data available for consumption via SQL queries and self-service visualization. Snowflake is a natural target for this type of data. Snowflake is not a data lake, but it separates storage from compute and provides a multi-cluster shared data architecture that can handle varying analytics workloads. Hence, it is seen as a scalable solution for processing the high velocity data streams generated by Kafka and combining them with other data sources.

What do you need to look out for?

There are multiple ways to move your Kafka data into Snowflake, using either native tools or third-party ones. As with any data ingestion pipeline, things will be quite simple at a smaller scale, and get more complicated as you start working with higher volumes of data or more complicated use cases. 

When building your Kafka-to-Snowflake pipeline, you should think about:

  • Data consistency and processing guarantees: Kafka streams will often contain late arriving events, out-of-order data, and duplicates. While there has been alot written about Kafka being able to support exactly once semantics, that only is true within a given partition, and any reasonable Kafka implementation will have numerous partitions for performance reasons. Poor data quality can affect the reliability of your downstream reports, and different ingestion tools offer varying levels of consistency, ordering, and exactly-once processing guarantees. It’s important to understand whether a separate effort will be needed to address these issues.
  • Transformation costs: Kafka data tends to become big data. Analytics requirements may demand continuous transformation at a significant scale. For example, you might need to join real-time user activity data from Kafka with historical user profile data stored in Snowflake to generate personalized recommendations. In these cases, you need to be mindful of the associated costs and decide whether you want to run these transformations entirely in Snowflake, or in a separate lower-cost compute layer such as a data lake. (See: ETL pipelines for Kafka data).
  • Schema evolution and data type compatibility: Your data streams evolve over time – for example, due to new fields being added to user activity logs or existing fields being modified. If your pipeline doesn’t address schema drift automatically, changes in schema and data types can disrupt or pollute your downstream analytics applications that lead to dropped or incorrect data.
  • Observability and error handling: Reliable data pipelines require continuous data observability to ensure data consistency and quality. For production use cases, you will need schema and data value monitoring, detection, logging, and alerting on anomalies, and error-handling features, in order to quickly identify and resolve issues.
  • Data engineering overhead: Any additional component will add some level of data engineering burden, which you would obviously want to minimize. Tools that require coding at the file system level in Java or Scala are typically harder to manage than declarative SQL-based platforms. Additionally, there could be data management challenges around removing stale data or optimizing file sizes that add further complexity.

Every solution you consider will address these issues to some extent, but none will solve all of them perfectly. It’s important to map out your analytics and governance requirements in advance, and evaluate any given pipeline tool or framework against them.

Kafka to Snowflake: What Are Your Options?

The most popular solutions to write Kafka data into Snowflake are:

  1. Snowflake-Native Tools: Kafka connector, COPY INTO from object storage
  2. Application-centric ingestion  tools such as Fivetran or Stitch
  3. Big data ingestion tools such as Upsolver SQLake
  4. Hand-coded pipelines using Spark

Native tools: The Kafka Connector or COPY INTO

The Kafka connector for Snowflake lets you stay within the Snowflake ecosystem and prevents the need to introduce external tooling into the mix. This connector uses Snowpipe or the Snowpipe Streaming API to ingest your Kafka data into Snowflake tables.

If Snowflake is your only target and you don’t have requirements for data quality and observability, the Snowflake Connector for Kafka will probably be your best choice. However, when you’re operating at larger scales or have diverse analytical targets, or where data quality problems are critical to avoid, you might want to consider some of the potential drawbacks:

  • Configuration and monitoring can get tricky: According to Snowflake’s best practices blog, you would still need to manage partitions, buffer properties, and other configurations in Kafka to control for cost and performance. While Snowpipe Streaming solves some of these problems, it introduces other challenges which we’ve covered in our previous article.
  • Vendor lock-in and ‘closed’ architecture: Using the Kafka Connector creates a proprietary pipeline where your data is loaded into Snowflake in a proprietary file format. This further locks you into Snowflake and prevents you from leveraging other tools that could help reduce costs or improve performance for some use cases. 
  • Transformation costs: Continually processing data in Snowflake can create significant and ongoing data transformation costs, which could far exceed the compute and storage costs of ingesting the data.
  • Lack of built-in data quality and observability: The Kafka Connector does not currently support schema detection. Monitoring and error handling need to be implemented via Java Management Extensions. It does guarantee exactly once or strong ordering leading to duplicates, dropped data and inconsistent data that has to be solved for in Snowflake. 

You can maintain a higher level of control over the raw data by writing the Kafka events to blob storage (such as Azure Blob or Amazon S3), and then periodically moving the data into Snowflake using the COPY INTO command. However, this approach is best suited for batch workloads (defeating the purpose of using Kafka in the first place) and requires managing a separate virtual warehouse to ingest and transform the data before it can be written into your production tables.

Multi-purpose ELT tools

ELT tools such as Fivetran or Stitch are designed to handle a wide variety of sources and targets, offering a more generalized approach to data integration. They tend to focus on application sources, such as Salesforce, Google Analytics and advertising APIs such as Google Ads and Facebook ads. These tools often provide a user-friendly, self-service experience managed via graphical user interfaces (GUIs) and SQL-based transformations.

However, there are some trade-offs when using ELT tools for moving Kafka data to Snowflake:

  • Addressing Kafka-specific challenges: Since ELT tools are built for “small data” use, they usually don’t address the unique characteristics of big streaming data, such as ordering, exactly-once semantics, and handling late-arriving or out-of-order events. Consequently, you might need to develop custom solutions (such as after-the-fact jobs in Snowflake)  to manage these challenges, increasing the complexity and maintenance overhead of your pipeline.
  • Transformation costs: ELT means the Transform is happening in Snowflake. This can become expensive, for the same reasons we’ve covered above. You will be paying for both Snowflake compute resources and the ELT tool, potentially increasing your overall data processing costs.
  • Scale limitations: These tools were built for small data and often run in their own SaaS environment.  You need to stress test for your current and future scale to understand the performance and cost impacts of a multi-fold increase in volume. 
  • Limited flexibility: While these tools offer a convenient self-service experience, they may not provide the same level of customization and flexibility as more specialized solutions. You might encounter limitations when trying to build complex, high-performance data pipelines tailored to your specific use case.
  • Data observability: 

Specialized big data ingestion tools

Specialized big data ingestion tools such Upsolver are designed specifically for handling large-scale streaming data, such as Kafka events. Upsolver offers various advantages over other solutions when it comes to moving Kafka data into Snowflake:

  • Streaming data quality: Upsolver is built to handle the unique challenges of Kafka data, such as ordering, exactly-once semantics, and late-arriving or out-of-order events. This means that it’s well-suited for managing Kafka data streams without the need for additional custom solutions.
  • Cost-effective transformations: With Upsolver, you can ingest directly to Snowflake with light cleansing operations performed en route, or  perform powerful data transformations such as aggregations and joins before ingesting data into Snowflake. This can significantly reduce your overall cloud compute costs by offloading compute to the data lake. Also, Upsolver is priced primarily based on the amount of data ingested, making it a more predictable and cost-effective solution and encouraging broader usage of data within the business.
  • No data engineering overhead: Upsolver provides a UI for Snowflake ingestion so that any user can configure pipelines. 
  • Open architecture with multiple target support: Upsolver can write data to other databases such as Amazon Redshift, as well as data lakes and serverless query engines like Amazon Athena and Starburst. This flexibility allows you to leverage the most suitable storage and processing solutions for your use case.
Published in: Blog , Building Data Pipelines
Eran Levy
Eran Levy

As an SEO expert and content writer at Upsolver, Eran brings a wealth of knowledge from his ten-year career in the data industry. Throughout his professional journey, he has held pivotal positions at Sisense, Adaptavist, and Webz.io. Eran's written work has been showcased on well-respected platforms, including Dzone, Smart Data Collective, and Amazon Web Services' big data blog. Connect with Eran on LinkedIn

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