Migrating ETL Workloads to Apache Spark

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Introduction

As organizations evolve their data strategies, many find the need to transition from Spark-based architectures to Snowpark—Snowflake’s powerful, developer-friendly framework for building scalable data pipelines, directly inside the Snowflake Data Cloud. This migration enables teams to simplify infrastructure, optimize performance, and reduce operational overhead.

Why Consider Migrating to Snowpark?

Unified Data Processing

Snowpark allows developers to write transformation logic in familiar languages (Python, Java, Scala) and execute it within the Snowflake engine, leveraging its native compute power and automatic scaling.

Simplified Architecture

Migrating from Spark to Snowpark removes the need for separate Spark clusters or external compute infrastructure. This consolidation reduces latency, data movement, and administrative burden.

Pushdown Optimization

Snowpark’s SQL pushdown capability enables all operations to be executed within the Snowflake engine, increasing query performance and minimizing compute cost.

Stryv’s Expertise in Snowpark Migration

At Stryv, we bring deep experience in both Spark and Snowflake ecosystems, allowing us to design structured, reliable, and high-performance migration journeys from Spark to Snowpark.

Strategic Assessment and Planning

We assess current Spark applications and ETL pipelines, analyze performance bottlenecks, and map feature parity between Spark APIs and Snowpark functions.

Code Refactoring and Optimization

  • • Translate Spark DataFrame transformations into Snowpark-native operations.
  • • Refactor UDFs to leverage Python UDFs in Snowflake.
  • • Simplify orchestration using Snowflake Tasks and Streams.

Architecture Redesign

  • • Migrating logic into Snowflake via Snowpark.
  • • Designing Snowflake Virtual Warehouse strategies for performance and cost.
  • • Integrating with external data sources using Snowflake connectors (Kafka, S3, etc.).

Performance and Cost Benchmarking

  • • Query latency
  • • Virtual warehouse auto-scaling
  • • Cost per job vs. Spark workloads

Glossary of Key Concepts

  • • Snowpark: A developer framework in Snowflake that enables writing data pipelines using Python, Java, and Scala.
  • • Pushdown Architecture: An execution model where all transformations happen inside the Snowflake compute engine.
  • • Python UDF: Custom logic defined in Python and executed natively in Snowflake.
  • • Virtual Warehouse: Snowflake’s compute engine that supports parallelism and elastic scaling.

Cost and Operational Advantages

Compared to Spark, Snowpark eliminates infrastructure management and integrates directly with your data warehouse, drastically reducing DevOps overhead and compute complexity. You benefit from pay-per-use economics, workload isolation, and built-in concurrency scaling.

Ready to Migrate?

Let Stryv accelerate your Spark to Snowpark migration. Our expert engineers ensure a structured, efficient, and future-proof migration tailored to your data goals.

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