Now that Snowpark is generally available, data engineering and data science teams can now perform ETL with Python, running Python as well as Java and Scala natively in Snowflake, ensuring all data teams can collaborate on a single Snowflake platform. Learn why Snowpark’s release is so groundbreaking for industry experts.
Data engineers and data scientists, especially those at Hakkoda, are excited about the recent release of Snowpark. This intuitive set of libraries and runtimes in Snowflake allows users to deploy and process Python, Java, or Scala code. For Python, this means that Snowpark takes care of serializing the custom code into Python byte code and pushing down all the logic to run in Snowpark’s secure Python runtime built right into the Snowflake engine.
Data projects require a wide range of tools and programming languages, which can lead to complex pipelines and data silos. Snowpark democratizes data engineering and data science, empowering Python, Java or Scala users to extend their capabilities to the data science realm by having an all in one location to model data. Snowpark also allows analysts and engineers to easily leverage ML (machine learning) and deep learning.
This impressive array of sophisticated tooling has motivated data experts and business leaders alike to migrate to Snowpark. In this blog post, we’ll go over Snowpark’s performance capabilities and why businesses benefit from a migration to Snowpark.
Why Leverage Snowpark for Your ETL/ELT Workloads
The advantages of Snowpark certainly don’t end with the ability to perform ETL with Python. There are three significant gains for companies migrating to Snowpark from alternative providers. Businesses that utilize Snowpark see:
- Performance Improvements
- Cost Savings
- Collaboration & Governance Gains
Performance Improvements
When it comes to performance, Snowpark is capable of delivering high quality outputs with increased efficiency. Hakkoda clients that migrated to Snowpark off of alternative platforms saw a 96% performance improvement. Aggregation of price and performance results across 30+ real customer POC and production results showed that customers saw a median of 3.5x faster performance and 34% cost savings with Snowpark over managed Spark.
Snowpark’s performance benefits stem from Snowflake’s elastic compute engine. Snowflake’s distributed engine features logically integrated but physically separated storage and compute. It was built using a multi-clustered, shared data architecture that plans and optimizes the execution of concurrent workloads. SQL developers were the first to benefit from this engine, which comes with many built-in optimizations such as auto clustering and micro-partitioning. Snowpark extends Snowflake’s engine beyond SQL to include Python, Java, and Scala developers.
Cost Savings Running Data Transformations with Snowpark
Another important rationale for a migration to Snowpark is the potential for significant cost savings. Snowpark allows companies to spend less money on dedicated servers by instantly connecting to a Snowflake warehouse and only paying for what they use.
On top of that, Snowpark eliminates the over-provisioning of large Spark/Hadoop/Databricks servers and reduces administrative overhead. Let Snowflake do the work for you on spinning up servers, troubleshooting, and monitoring.
Snowpark’s functionality on the level of cost savings is truly unbeatable. Their solutions are simple, moving a process like troubleshooting away from the level of code to that of underlying data. On inferior platforms, it can take from 30 minutes to a couple of hours to correctly provision and set up batch jobs to run on data transformations and data analysis. Snowpark drives this time down to seconds.
Collaboration and Governance on Snowpark
The biggest benefit of leveraging Snowpark is the most obvious: Your data never has to leave Snowflake. All transformations and data science modeling stays within your walls, and there’s no movement of data when you need to use external data vendors. All orchestration occurs within Snowflake.
Flattened architecture is another benefit that supports better governance. With less outside data vendors and more ownership within Snowflake, you reduce security threats, improve collaboration, and flatten your total architecture. On Snowflake’s platform, your users can perform Data Ops and MLOps at scale, all within your unique Snowflake environment.
Beyond ETL with Python: Which Departments Should Care About Snowpark?
Snowpark benefits teams that work within data engineering, data science, and data apps. The gains are numerous for each of these teams, but we’ll hone in on the top three or four for each department.
Data Engineering
Data engineering teams are among those who stand to benefit most from Snowpark’s release and continued feature additions. Data engineers will see their day-to-day improved by functionalities like the ability to:
- Perform all data transformation and ingestion from their Snowflake environment
- Connect to external APIs while leveraging open-source libraries
- Orchestrate all data ops out of snowpark without additional resources needed
Data Science
Similar to data engineers, data scientists can use Snowpark to:
- Leverage the Snowpark ML Modeling API (public preview) to scale out feature engineering and simplify model training
- Run model training and deployment all out of one environment
- Centralized data for increased data collaboration between departments or companies while easily incorporating new data into ML models
- Leverage zero copy cloning to share sample data without additional storage pricing
- Quickly spin up compute resources with no provisioning required for model training
Data Apps
Finally data apps teams can also use Snowpark as the processing layer to build data applications . Data apps teams can:
- Monetize data applications with Snowflake Native Apps on the Snowflake Marketplace
- Securely share machine learning IP without giving up the “secret sauce”
- Collaborate on their data with others while never allowing it to leave Snowflake
Capabilities of Snowpark, an Overview
Snowflake designed Snowpark to address what they saw as a specific pain point in the world of data and technology. Snowpark supports individual users in leveraging complex infrastructure using Python, Scala, or Java to help data engineers, data scientists, and application developers generate insights. Snowpark for Python, for instance, helps “empower the growing Python community…to build secure and scalable data pipelines and machine learning workflows directly into Snowflake.” In addition to allowing employees to perform ETL with Python or another preferred language, Snowpark provides production-level support for a variety of programming contracts, such as UDFs and Vectorized UDFs and stored procs.
Along with Python-familiar syntax, Snowpark also provides secure access to the Python ecosystem via their partnership with Anaconda. All Snowpark users can benefit from thousands of the most popular packages that are pre-installed from the Anaconda repository, including fuzzy wuzzy for string matching, h3 for geospatial analysis, and scikit-learn for machine learning and predictive data analysis. Additionally, Snowpark is integrated with the Conda package manager so users can avoid dealing with broken Python environments because of missing dependencies. As Anaconda wrote in their official press release, “Snowflake’s investment in Anaconda is a step towards providing users of the Data Cloud effortless access to the most popular Python open-source packages while ensuring the security and governance Anaconda is known for.” Not only can engineers and developers access the Python ecosystem, they can do so in a highly secure sandbox environment that complies with governance and security policies.
Other important capabilities in Snowpark for Python include the ability to run secure Python-based workflows in a single place without having to move the data somewhere else. These workflows are run using Snowflake’s secure processing and are aided by Anaconda’s dependency management. This allows data experts to build data workflows and pipelines in Anaconda libraries.
Why do these specific capabilities matter? With Snowpark, data users can create streamlined pipelines. Since Snowpark’s release, data scientists have deemed the platform’s ability to use a popular and versatile programming language, such as Python, directly in the cloud, a complete game-changer. However, Snowpark for Python is just the tip of the iceberg. Snowflake also announced that they are currently expanding the platform’s functionality based on the feedback they receive from users.
Leveraging Snowpark with Hakkōda
At Hakkoda, our highly-trained team of experts can help your business move to a modern data stack. Using the latest functionalities and tools, such as Snowpark for Python, Hakkoda’s 100% SnowPro certified team builds data solutions that suit your objectives. You can worry about growing your business – Let our knowledge and expertise take care of the rest.
To start your data innovation journey with state-of-the-art data services and solutions, contact us today.