Snowflake vs Kafka: 5 Critical Differences


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With ever-increasing data, organizations have realized the need to process the data. To make the most out of it, there has been a growing demand for separating storage and compute. This is where Kafka and Snowflake are the two major elements involved in the process of moving streaming data to a cloud data platform. 

In this guide, we have prepared a list of the parameters for making the Snowflake vs. Kafka comparison. Leap forward and go through the parameters.

Table of Contents

Key Differences between Snowflake and Kafka

Apache Kafka is a distributed event streaming platform that is used to create real-time streaming data pipelines and responsive applications. It combines messaging, storage, and stream processing to enable for both historical and real-time data storage and analysis. 

You can also refer to our blog on Kafka Event Streaming Platform, to learn more.

Snowflake is a fully managed SaaS platform that offers pre-built capabilities, including storage and compute separation, data sharing, data cloning, on-the-fly scalable compute, and third-party integrations support to extend the desired capabilities.

We have compiled a list of parameters based on which the Snowflake vs. Kafka comparison can be made. 

We’ll start with the working methodology of the two platforms.

Snowflake vs Kafka: Working Methodology

Apache Kafka is a messaging system. And messaging is the act of sending a message from one place to another. There are three principal factors involved in messaging. These are as follows:

  • Producer: It produces and sends messages to one or more queues.
  • Queue: It is a buffer data structure that receives (from the producers) and delivers messages (to the consumers) in a FIFO (First-In First-Out) manner. 
  • Consumer: It is subscribed to one or more queues. And it receives messages from the subscribed queues when published.

As mentioned above, Kafka also works in a similar manner. However, the Queue concept isn’t there. Instead, there is the concept of Topics, which is similar to Queue but with some additional specifications. The Topic is an immutable log of events.

Apache Kafka works in the following manner.

Kafka's Working Methodology
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  • Kafka, in general, accepts streams of events written by data producers
  • It keeps records chronologically in partitions across brokers (servers); a cluster is made up of several brokers
  • Each record includes information about an event. 
  • It groups records into topics; data consumers obtain data by subscribing to the required topics.

The primary objective of data warehousing is to have a centralized repository where data is stored from various sources in analysis-ready form. Snowflake’s working model includes three tiers: 

Snowflake's 3 tiers
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  • Database Storage: The data is loaded and stored in the bottommost layer, which is known as Database Storage. Snowflake reorganizes that data into its internal optimized, compressed, and columnar format before storing the data.
  • Query Processing: It is present in the middle layer, which provides the platform for analysis. In this layer, Snowflake processes queries using “virtual warehouses.”
  • Front-end client layer: The results after performing analysis, reporting, and data mining are visible to the user on the topmost front-end client layer. This layer includes a collection of services that combines and coordinates all the activities across Snowflake. 

Snowflake vs Kafka: User Persona

To work with Apache Kafka, you must have a strong technical background. It is generally used by experienced Java developers, tech leads, and solution architects. The learning curve is steep. You’ll need extensive training to learn Kafka’s basic foundations and the core elements of an event streaming architecture. 

Even the market penetration of Apache Kafka is extremely high—over 80% of the Fortune 100 companies use Apache Kafka to modernize their data strategies with event streaming architecture. It is deployed at scale in silicon valley tech giants, startups, and even traditional enterprises.

Snowflake is administered by a data engineer or database administrator (IT) but is significantly important to everyone in the organization since it is the centralized source of data. Users (typically Analysts) can build their reports by transforming data according to their use cases. The admin of the Snowflake should be well versed with SQL and programming ability in languages such as Python, Java, Scala, etc., while the business users can easily work on the warehouse with basic SQL knowledge.

Snowflake vs. Kafka: Use Cases

Being a cloud data warehousing solution, Snowflake has a variety of use cases. These include:

  • Storage: Snowflake, being a cloud data warehouse, is a great alternative to an on-prem data storage solution. It is a highly scalable, fully-managed durable, and cost-effective storage solution.
  • Reporting: Snowflake will act as a single source of truth for all your data. Hence, any data you need for preparing reports will be available in this centralized repository. Thus, it will facilitate the reporting process. In addition to that, numerous BI & reporting tools can be integrated effortlessly with Snowflake.
  • Analytics: Snowflake allows you to execute data analysis at any scale to gain insights. Its wide integration with various tools will help add value to operational business applications. 

Apache Kafka has a variety of use cases. Some of them are listed below:

  • Operational Metrics/KPIs Monitoring: Kafka is frequently used for operational monitoring data. This includes collecting & aggregating statistics from distributed applications in order to generate centralized feeds of operational data.
  • Log Aggregation Solution: Many organizations use Kafka as an aggregation solution. It is used to collect logs from multiple services throughout an organization and make them available to numerous consumers in a standard format.
  • Website Tracking: A website activity often generates a large quantity of data, creating different messages for each page view and user activity. Here,  Kafka guarantees that data is transferred and received by both parties successfully.

Snowflake vs Kafka: Limitations

Snowflake is a widely accepted data warehouse solution with numerous advantages. Its performance and ease of use help it stand out from others. However, with its multiple advantages, there are also a few limitations associated with Snowflake. These limitations are:

  • Lack of support for unstructured data: Snowflake only supports storing structured and semi-structured data.
  • No data constraints: Snowflake is infinitely scalable, and users must pay for everything they need. However, as Snowflake has a pay-as-you-go model, users might incur expensive bills if they don’t keep track of the storage capacity they are using.
  • Only bulk data load: While moving data to Snowflake, there is extensive support and guidance on bulk data loading. However, if users require continuous loading, they are limited to using Snowpipe, which isn’t robust and doesn’t have extensive support.

Even though Apache Kafka is an industry-wide messaging platform, some limitations are also associated with it. These involve:

  • Issues with Message Tweaking: The broker leverages system calls to convey messages to the customer. However, Kafka’s performance is compromised if the messages are modified.
  • No support for wildcard topic selection: It is evident that Kafka doesn’t support wildcard topic selection, as Kafka only matches the exact topic name. Because of this, it cannot address the use cases where the name of a wildcard topic differs.
  • Reduced Performance: There are no concerns with individual message size in general. However, when the size of the messages grows, the brokers and consumers start compressing them. As a result, when decompressed, the node memory is gradually used. In addition to that, compression occurs during the data flow in the pipeline. It has an impact on both throughput and performance.
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  • Behaves Clumsy: When the number of queues in a Kafka cluster rises, then due to buffering the latency also increases. Thus it behaves in a very slow and clumsy way.

Snowflake vs Kafka: Alternatives

People seek different features in a data warehouse solution that best fits their use cases. The features can be easy to use, reliable software solutions with built-in data analytics, data lake integration, and AI/ML integration. Other important factors considered when looking for alternatives to Snowflake include features, pricing, user interface, and the number of integrations. 

Here’s the list of the top 6 alternatives of Snowflake:

  • Google BigQuery
  • Amazon Redshift
  • Databricks Lakehouse Platform
  • Microsoft SQL Server
  • IBM Db2
  • Azure Synapse Analytics

Refer to our blog on top open source alternatives of Snowflake to learn more about the alternatives tools of Snowflake.

Event Stream Processing software has a wide market penetration in this data-driven world. People seek time-saving solutions, popular software solutions with in-built API designers, and pre-built & customized connectors. Other important factors considered when looking for alternatives to Apache Kafka include messages and integration.

Here’s a list of some of the tools which can be considered as an alternative to Apache Kafka:

  • Amazon Kinesis 
  • RabbitMQ
  • ActiveMQ
  • Red Hat AMQ
  • IBM MQ
  • Amazon SQS

Refer to our blog on top alternatives of Apache Kafka to learn more about the alternatives of Apache Kafka.

Summing It Up

In this article, you got an in-depth analysis of the parameters based on which you can make the Snowflake vs. Kafka comparison.

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We hope you have found the appropriate answer to the query you were searching for. Happy to help!

Manisha Jena
Research Analyst, Hevo Data

Manisha is a data analyst with experience in diverse data tools like Snowflake, Google BigQuery, SQL, and Looker. She has hadns on experience in using data analytics stack for various problem solving through analysis. Manisha has written more than 100 articles on diverse topics related to data industry. Her quest for creative problem solving through technical content writing and the chance to help data practitioners with their day to day challenges keep her write more.

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