The Showdown: Snowpark vs. Spark for Data Engineers (2024)

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Should you migrate your big data workflows from Spark to Snowpark? Are you wondering what all the fuss is about? You’ve come to the right place.

In this article, Snowpark and Spark go head-to-head as we compare their crucial features. We’ll discuss the tradeoffs between the two tools, backing our claims with evidence from a benchmarking analysis.

Discover the best tool based on:

1. Use cases
2. Supported programming languages
3. Performance
4. Scalability
5. Ease of use
6. Infrastructure
7. Costs

What is Snowpark?

Snowpark is a new developer framework for Snowflake. It allows developers to write code in their preferred programming language (Python, Scala, or Java) and run that code directly on Snowflake.

The Snowpark framework allows you to perform many big data use cases in your preferred programming language:

1. Snowpark DataFrame API: Write queries and data transformations using the familiar DataFrames. Snowpark converts the operations to SQL to scale processing in Snowflake.
2. User-Defined Functions (UDFs): Execute business logic and train machine learning models directly on Snowflake data. Extend Snowflake data engineering with machine learning open-source libraries.
3. Stored procedures: Operationalize and orchestrate DataFrame operations and custom code to run on a desired schedule and at scale - all natively in Snowflake.

Although Snowpark offers many programming language options, this comparison will focus on Snowpark for Python.

You might be asking: “But we already have the Snowflake Connector for Python - what’s the big fuss with the Snowpark API?”

The Snowflake Connector does allow you to run Python code (or use ORM drivers for Go, PHP, .NET, etc.) and access the Snowflake data warehouse. But if you want to use DataFrames or other Pythonic solutions, the code execution will happen on your local machine. Snowpark, on the other hand, executes the code on the Snowflake data lake or data warehouse itself, without first moving the data to your machine. This gives you all the benefits of the fully managed, endlessly scalable, and highly performant Snowflake platform.

What is Spark?

Apache Spark is an open-source engine for executing data engineering, data science, and machine learning on single-node machines or clusters.

Spark allows users to run these use cases using RDDs (Resilient Distributed Datasets), the Spark DataFrame, or the Spark DataSet.

Until recently, Spark was the go-to tool for big data workflows. But it comes with its own set of limitations and challenges, such as lack of governance and security capabilities, significant time investments, high total cost of ownership, and inefficient runtime.

To understand whether the tradeoffs between Spark and Snowpark make it worth migrating to the latter, we conducted a benchmarking analysis (results covered below).

Side note: To make this comparison meaningful, we'll focus on Spark DataFrame and Spark, rather than SparkSQL. We’ll compare the two technologies with the assumption that you have a Snowflake data warehouse or data lake.

Snowpark vs. Spark: Comparison

There are multiple crucial features on which to compare the two tools:

1. Use cases
2. Supported programming languages
3. Performance
4. Scalability
5. Ease of use
6. Infrastructure
7. Costs

#1: Use cases

Both tools cover the same big data use cases:

• Data engineering: Provision ETL data pipelines, run data validation workflows, and administer data lakes and data warehouses.
• Data science: Train, develop, and provision machine learning models.
• Data analytics: Run SQL queries, or model the data in your data warehouse to present KPIs and other metrics.

The emphasis here is on big data. Unlike other technologies which are used in the same domain (e.g. Pandas), both Spark and Snowpark are designed to handle vast amounts of data without impairing performance.

Keep on reading to find out which tool is quicker at processing big data volumes.

#2: Programming languages

Both Spark and Snowpark support programming in Python, Java, and Scala. This allows data scientists and data engineers to collaborate and work together on the same big data workflows.

However, Spark offers an additional programming language - R. If you’re a data scientist familiar with R but not other languages, Spark is going to be the better alternative for you.

#3: Performance

When evaluating the performance of Snowpark and Spark, the best measure is the runtime - the time it takes to complete a Spark job or a Snowflake Snowpark workload.

But which one is faster? The question is hard to answer at face value because multiple factors affect performance, including dataset size, infrastructure, and the nature of the task. On top of that, both frameworks promise a high level of performance across all dimensions.

To answer this, we performed a benchmarking study using Keboola’s infrastructure to compare Snowpark with Spark.

Snowpark was the overwhelming winner. It came out on top in 7 out of 8 use cases. The result was confirmed across different engineering tasks, dataset sizes, and even infrastructures.

#4: Scalability

Both Snowpark and Spark are well-suited for big data engineering and science tasks. However, when comparing their scalability in relation to dataset sizes, it becomes evident that Snowpark outperforms Spark.

While both frameworks are capable of processing large-volume workflows (unlike Pandas or pure Python), PySpark's performance displays more noticeable degradation, resulting in longer runtimes as the dataset size increases.

In contrast, Snowpark showcases superior scalability, maintaining its efficiency even with larger datasets.

#5: Ease of use

When it comes to intuitiveness, both Snowpark and Spark offer a very user-friendly environment. If you know how to program in Python (or Java, or Scala), the frameworks will be easy to use.

Beyond their ease of use, these frameworks offer additional advantages that set them apart from other technologies. Data engineers and scientists speed up their processes and streamline workflows with features like automated schema detection, increased data quality with in-framework typing or other validations, and more.

Beware: “Ease of use” isn’t the same as “Ease of setup”.

Snowpark is very simple to get up and running, whereas Spark requires you to set up an entire infrastructure. This can be a challenging task, even for experienced data engineers.

#6: Infrastructure

Snowpark runs all code directly in the Snowflake data cloud, eliminating the need to move data out of the data lake or data warehouse.

In contrast, Spark adopts a different infrastructure methodology. It accesses Snowflake data through a connector, then transfers it to its own compute data platform, which typically consists of one or more distributed Spark clusters. The results are then either sent back to Snowflake or delivered to another downstream consumer.

This difference in infrastructure leads to two important shortcomings in Spark’s infrastructure:

1. Infrastructure overhead: To use Spark, you have two options. First, you can set up and administer the Spark infrastructure yourself, which demands considerable data engineering expertise and is no small feat. Alternatively, you can rely on third-party providers, such as Azure, AWS, or GCP, who offer Databricks as a service for running Spark.
2. Data movement: With Spark, data processing happens outside of Snowflake, requiring additional setup, security measures, networking configurations, and data movement costs.

These infrastructural downfalls have multiple consequences, including increased costs, potentially increased headcounts, and a slower time to market.

#7: Costs

In a head-to-head comparison, Snowpark emerges as a more cost-effective option than Spark, while also delivering superior performance. Here's why:

1. Faster performance: Snowpark's speedier performance translates into lower compute costs, as compute minutes are a significant cost component in big data processing.
2. Efficient data processing: Snowpark's data processing within the Snowflake environment minimizes data movement costs, including networking expenses.
3. Better infrastructure: Snowpark's execution within Snowflake eliminates the need for additional infrastructure provisioning. This eliminates overhead costs associated with managing Spark clusters and paying specialized talent, resulting in further cost optimization.

This is good in theory, but are these differences significant in comparison to Spark? To answer this question, we conducted a benchmarking study comparing Snowpark on Keboola's out-of-the-box infrastructure (built atop Snowflake) with Spark on Databricks.

The results revealed that Snowpark was superior for total cost of ownership, which was on average 25% cheaper than Snowpark, excluding talent costs.

The Final Verdict: Which One is Better for Data Engineers and Data Scientists?

When it comes to data engineering and data science workloads, both Snowpark and Spark stand as impressive frameworks. Both offer intuitive interfaces.

However, the clear winner for optimal performance and cost efficiency is Snowpark.

Snowpark surpasses Spark in several critical aspects. It offers better data processing performance, scales more seamlessly with increasing dataset sizes, demands lower infrastructural investments, and overall is a much more affordable option.

The only reason why you’d pick Spark over Snowpark is if you’re an R-only organization.

Curious about the details? 👀 Download the whitepaper and explore how Snowpark for Python benchmarks against Spark.

Snowpark + Keboola = Next Level

Keboola is taking your big data workflows to the next level with its Snowpark integration. With it, you’ll be able to access Snowflake’s capabilities directly from Keboola’s Workspaces.

This unlocks many benefits:

1. Build with Snowflake data, the Python way. Run your code from a Jupyter-based IDE directly in Keboola and seamlessly work with your Snowflake data. One-click integrations remove even the smallest frictions in Snowpark management.
2. Productize your data solutions. Turn your data solutions into fully fledged interactive apps using Keboola + Streamlit + Snowflake. This marriage of technologies allows you to prototype and productize your data solutions fast, accelerating time to market for self-serving data apps.
3. Focus on building products, not maintenance. When using Snowpark with Keboola, Keboola deals with all of the DataOps for you, so you can spend more time building big data use cases. Keboola takes care of dynamic backend scaling, Snowpark script versioning, GIT integrations, security, observability, user management, script sharing with your team members, and more.

Let Keboola take care of all the heavy lifting in the background, while you access state-of-the-art data processing features using Snowpark.

Create a forever-free account (no credit card required) and take it for a spin.

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