Snowflake Data Cloud
vs. Spark-Based Lakehouses

Choose Snowflake with Confidence

This resource kit includes:

  • Ebook: Spark to Snowflake Migration Guide
    • Migration best practices for fast and governed data pipelines in Snowpark.
  • Guide: Migrate to the Cloud: The How and Why of Modernizing Your Data Platform
    • Learn how to execute a carefully planned migration including more capabilities at lower cost.
  • On-Demand Webinar: Enabling a Multi-Cloud Strategy with Snowflake
    • Hear about Snowgrid, a cross-cloud technology layer that forms a grid of interconnected regions.
  • Solution Brief: Snowpark – Fast, Governed Pipelines Using Familiar Programming
    • Developers bring data processing and pipelines written in Python, Java, Scala, and SQL to Snowflake’s elastic processing engine.
  • Forrester Study: 612% ROI! Read Forrester’s Total Economic Impact™ Study 0f Snowflake
  • Snowpark Customer Report
    • Learn about how customers see a median of 3.5x faster performance and 34% cost savings with Snowpark over managed Spark for data engineering and AI/ML workloads
  • Business Value Calculator
    • Calculate your annual estimated benefits of migrating to Snowflake.

Download the Kit

Snowflake
BY THE NUMBERS

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CUSTOMER
ROI

Snowflake customers can expect an ROI of 612% and total benefits of more than $21 million over three years, according to Forrester’s Total Economic Impact™ Study.

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FORTUNE 2000 CUSTOMERS

Snowflake has attracted a multitude of customers from within the Fortune 2000.

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REDUCTION
IN RUNTIME

AMN Healthcare saw a reduction of runtime by 75% by switching to Snowflake from a spark-based lakehouse.

Snowflake Data Cloud vs.
Spark-Based Lakehouses

Choose between a self-managing, connected platform delivered as a service, and sets of siloed infrastructure you manage in your environment.

Capabilities
Spark-based Lakehouse
Snowflake
Self-Managing Spark-based Lakehouses require intensive planning, setup and management. This means users may spend additional time and energy manually configuring data files, query optimizations, separate compute clusters, security and governance. Snowflake Data Cloud has near-zero administration, no planned downtime for scaling or maintenance, an elastic multi-cluster compute engine, and unified security and governance.
Single Engine Customers must manage their own pools of compute resources and availability. Multiple engines are required for different use cases and languages, and customers are responsible for selecting runtimes, node types, modes, setting up pools and writing init scripts themselves. Snowflake includes an elastic multi-cluster compute engine that can both scale up and scale out to support many concurrent workloads against the same data. Customers frequently see lower costs and faster time to value.
Governance & Security Spark-based Lakehouses put the onus of security on the customer. Enabling security controls require many manual processes, such as reviewing alerts and constantly validating controls – which often differ between cloud provider, product edition, object type and compute clusters. Security is built-in and delivered as part of the service. From tri-secret secure encryption, automatic key rotation, end-to-end encryption for data at rest and in transit to dynamic data masking, Snowflake provides a set of advanced security features embedded within the platform.
Collaboration Spark-based Lakehouses have limited features for collaborating with data inside and outside your organization. This is because the data is frequently siloed by workspace and requires additional configuration to unlock it—and even when unlocked by an additional manual process, only particular objects can be shared, like single tables but not views, functions, or applications. Snowflake enables cross-cloud, cross-region connectivity using Snowgrid, a proprietary cross-cloud technology layer that forms a grid of interconnected regions. Snowflake also supports sharing data, functions, and apps both inside and outside your organization.

Start your migration Journey

DOWNLOAD MIGRATION KIT

HEAR FROM
SNOWFLAKE CUSTOMERS

By taking advantage of the Snowflake virtual warehouse, we were able to meet our one-to-three-minute SLA for processing pipelines and bring down total runtimes by as much as 75%.

READ THE CASE STUDY

ARIO MELENDEZ

Director, Data Management and Analytics, AMN Healthcare

We’ve been able to improve our unit margin by 5%—that means customers are converting better and getting the cars they want. This requires us to manage and process at scale. Our Spark architecture wouldn’t have worked; it has to be Snowflake.

READ THE CASE STUDY

PATRICK CALLINAN

Director of Insights and Data Science, CarTrawler

With the Snowpark feature, the benefits of being able to run data science tasks, such as feature engineering, directly where the data sits, is massive. It’s made our work a lot more efficient and a lot more enjoyable.

READ THE CASE STUDY

REBECCA VICKERY

Data Science Lead, EDF

Before, we had to move the data for processing with other languages and then bring results back to make those accessible. Now with Snowpark, we are bringing the processing to the data, streamlining our architecture and making our data engineering pipelines and intelligent applications more cost effective with processing happening within Snowflake, our one single platform.

READ THE BLOG

SUHAS JOSHI

Senior Director of Clinical Data Analytics, IQVIA