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Typically, it involves multiple platforms and engines like Snowflake, Apache Spark™, Trino and Apache Flink™ handling distributed data engineering, serving interactive query patterns and handling streams, with every engine reading one governed copy of the data. Enterprise teams that build this way accumulate two investments that are expensive to disturb: a PySpark codebase spanning jobs, notebooks, shared libraries and user-defined functions (UDFs), and an external catalog such as AWS Glue, Apache Polaris™, Databricks Unity Catalog or Microsoft OneLake that every other engine already trusts. For these teams, the question is whether they can get there without a multiquarter rewrite or a catalog cutover that touches every other engine.\u003C/p\u003E\r\n\u003Cp\u003EWhen these teams evaluate consolidating their workload in Snowflake, the migration is usually framed as two coupled choices: to rewrite the Spark code against a platform-native API, and to hand the tables to a different catalog. Coupling those choices is what makes the migration disruptive, because the rewrite runs into the long tail of production code while the catalog change reaches every engine that is not Snowflake. This post explores how we separated the two decisions, using \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/product/features/snowpark-connect-apache-spark/\"\u003ESnowpark Connect for Apache Spark\u003C/a\u003E on the compute plane and an external catalog projected into Snowflake through a catalog-linked database as the metadata plane. After evaluating their workload with Snowflake, the teams can optionally modernize their catalog to Horizon to unlock the full suite of enterprise AI and data platform capabilities.\u003C/p\u003E\r\n\u003Ch2\u003EThe migration challenge: Coupled compute and metadata cutovers\u003C/h2\u003E\r\n\u003Cp\u003EHistorically, moving Spark workloads onto a single analytics platform has meant running two migrations at the same time. The compute-plane migration rewrites PySpark jobs, notebooks and Spark SQL onto that platform's native DataFrame or SQL surface while the metadata-plane migration hands the tables to that platform's catalog. Each migration is tractable in isolation, but when run together, they force every producer and consumer of every table to be accounted for before the first workload can move.\u003C/p\u003E\r\n\u003Cp\u003EThere are a few practical questions that decide the risks and benefits for the migration. How will the platform perform on this workload, and at what cost for the same unit of work? How can that be verified without disturbing the pipelines running today? Is there an incremental path, or does the first workload commit the whole estate? What does rollback look like once both the code and the catalog have moved?\u003C/p\u003E\r\n\u003Ch4\u003EProblem 1: The rewrite comes before the evidence\u003C/h4\u003E\r\n\u003Cp\u003EThe visible part of a PySpark estate is its scheduled production jobs. The rest is shared libraries, UDFs, notebooks, ad hoc analysis and the continuous integration (CI) pipelines that grew around them over several years. A rewrite onto a different DataFrame or SQL API has to account for all of it, because a half-rewritten codebase risks delaying the migrations since new risks get discovered with each piece of code being migrated.\u003C/p\u003E\r\n\u003Cp\u003EMore critically, the rewrite is a prerequisite for the evidence that would justify it. How a job performs on a new engine is a property of that engine's optimizer rather than a prediction from the old execution plan, so it stays unknown until the job runs there. Maintenance cost has the same shape, because cluster patching, version upgrades and capacity tuning are only priced accurately once a workload runs without them. Total cost of ownership (TCO) therefore remains an estimate until the port is far enough along to compare, and that is the point at which backing out costs the most.\u003C/p\u003E\r\n\u003Ch4\u003EProblem 2: The catalog choice used to be a one-way door\u003C/h4\u003E\r\n\u003Cp\u003EAmazon Elastic MapReduce (EMR), Trino, Apache Flink and Amazon Athena resolve tables through the catalog and expect to observe every committed snapshot. Historically, a platform's own catalog was closed: Metadata lived in a proprietary format with no open protocol in front of it, so handing the tables over stranded every other engine. That is why the catalog decision, rather than the code rewrite, set the date of the cutover window, and why teams treated it as irreversible. That constraint no longer holds, because \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/user-guide/snowflake-horizon\"\u003ESnowflake Horizon Catalog\u003C/a\u003E exposes the \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-access-using-external-query-engine-snowflake-horizon\"\u003EApache Iceberg REST API\u003C/a\u003E, so other engines read and write Snowflake-managed Iceberg tables using Snowflake’s roles and policies.\u003C/p\u003E\r\n\u003Ch2\u003EChoosing a catalog\u003C/h2\u003E\r\n\u003Cp\u003EWith the compute question answered separately, the remaining decision is which system arbitrates table state. This is optionality rather than a gate, because both answers are first-class Iceberg catalogs that keep other engines working, and neither has to be settled before a workload moves. The choice stems from two different, perfectly valid views of where metadata authority belongs. Each choice has its own valid use cases based on an organization's lakehouse setup.\u003C/p\u003E\r\n\u003Ch3\u003ESnowflake Horizon Catalog as the catalog of record\u003C/h3\u003E\r\n\u003Cp\u003EIn this model the tables become Snowflake-managed Iceberg tables and Horizon Catalog arbitrates their state. A table that uses Snowflake as the \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg\"\u003EIceberg catalog\u003C/a\u003E has full native Snowflake platform support, while a table that uses an external catalog has limited platform support.\u003C/p\u003E\r\n\u003Ch3\u003EAn external catalog as the catalog of record\u003C/h3\u003E\r\n\u003Cp\u003EIn this model AWS Glue, Apache Polaris, Unity Catalog or OneLake stays authoritative and Snowflake participates through a catalog-linked database. No table is reregistered and no table management changes hands, so the migration touches neither the codebase nor the catalog. It suits organizations that have already committed to an external catalog but still want to use the Snowflake platform's rich capabilities.\u003C/p\u003E\r\n\u003Cp\u003EThe rest of this post follows the external catalog choice, because it provides an easy, nondisruptive path for users to evaluate the Snowflake platform without fully committing to it.\u003C/p\u003E\r\n\u003Ch2\u003ESnowflake's solution: Decoupling the compute plane from the metadata plane\u003C/h2\u003E\r\n\u003Cp\u003ESnowflake implementations of open table formats have decoupled compute and metadata, so rather than asking customers to move both at once, we made a deliberate engineering choice: Treat them as two independent migrations, and make each one optional.\u003C/p\u003E\r\n\u003Ch3\u003ERun existing Spark code on Snowflake with minimal changes\u003C/h3\u003E\r\n\u003Cp\u003E\u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/product/features/snowpark-connect-apache-spark/\"\u003ESnowpark Connect for Apache Spark\u003C/a\u003E leverages the Spark Connect protocol to enable users to run their Spark apps in an optimized way on Snowflake’s vectorized engine. Teams point their Spark sessions at Snowflake instead of a cluster, and their existing DataFrame and Spark SQL code keeps working, with no clusters to size, patch or scale.\u003C/p\u003E\r\n\u003Cp\u003ERather than translating one API into another, Snowpark Connect implements the Spark Connect protocol itself. That distinction is what the migration rests on: Most existing Spark code runs as is. For the parts that don't, the \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/blog/migrate-spark-to-snowflake/\"\u003ECoCo Spark Migration skill\u003C/a\u003E assesses compatibility, converts code and validates the changes.\u003C/p\u003E\r\n\u003Ch3\u003EZero-touch metadata with live catalog projection\u003C/h3\u003E\r\n\u003Cp\u003EA \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-catalog-linked-database\"\u003Ecatalog-linked database\u003C/a\u003E is a Snowflake database that behaves as a live, bidirectional projection of an external Iceberg REST catalog as introduced in an earlier post on \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/blog/engineering/snowflake-platform-data-lake/\"\u003Ebringing the Snowflake platform to data lakes\u003C/a\u003E. Namespaces appear as schemas and remote tables appear as tables, while statements on either side propagate to the other. Snowflake commits its writes through the same catalog every other engine already uses, with the \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/blog/engineering/iceberg-rest-catalog-reliable-writes/\"\u003Econcurrency control and commit resilience\u003C/a\u003E on the shared write path. Snowflake deliberately only projects the catalog rather than copying it, because the system other engines already trust stays the one that decides table state.\u003C/p\u003E\r\n\u003Cp\u003ENext, we will explore a detailed case study demonstrating how an enterprise system can perform this migration seamlessly. We will examine a real-world pipeline that processes large-scale transactional data managed in an AWS Glue Data Catalog, walking through both the metadata setup and compute reconfiguration required to transition the job to Snowflake without code rewrites or catalog relocations.\u003C/p\u003E\r\n\u003Ch2\u003EExample: Running an AWS Glue Spark job on Snowflake\u003C/h2\u003E\r\n\u003Cp\u003EConsider a PySpark job that reads a raw sales table managed by AWS Glue, aggregates it by product and writes a report table back. On the metadata side, the Glue catalog is projected into Snowflake through a \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/user-guide/tables-iceberg-configure-catalog-integration\"\u003Ecatalog integration\u003C/a\u003E, which also supplies the \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/blog/engineering/iceberg-catalog-credentials/\"\u003Estorage credentials\u003C/a\u003E Snowflake uses to read the files, and a \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://docs.snowflake.com/en/sql-reference/sql/create-database-catalog-linked\"\u003Ecatalog-linked database\u003C/a\u003E:\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet":{"id":"code-snippet-cffc93a40d","language":"sql","codeSnippet":"CREATE DATABASE glue_lake_db\r\n  LINKED_CATALOG = (\r\n    CATALOG = 'my_glue_catalog_int',          -- Iceberg REST integration for AWS Glue\r\n    ALLOWED_NAMESPACES = ('sales_lakehouse')  -- limits visibility to one Glue database\r\n  );\r\n\r\nSELECT SYSTEM$CATALOG_LINK_STATUS('glue_lake_db'); -- confirms discovery is running","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_1567443652":{"id":"blog-text-fed02ac0ca","text":"\u003Cp\u003ESnowflake discovers the Glue namespace as a schema and its Iceberg tables as tables, with no per-table registration step, resolved through the explicit \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/blog/engineering/solving-identifier-mismatch-multi-engine-lakehouse/\"\u003Eidentifier casing contract\u003C/a\u003E at the catalog-linked database boundary. On the compute side, what changes is the endpoint the session talks to:\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"code_snippet_596835408":{"id":"code-snippet-12605dfa22","language":"python","codeSnippet":"from snowflake import snowpark_connect\r\n\r\nspark = snowpark_connect.get_session()  # Snowflake stands in for the Spark driver\r\n\r\nsales = spark.table(\"glue_lake_db.sales_lakehouse.raw_sales\")  # identifiers Glue already uses\r\nreport = sales.groupBy(\"product_id\").agg(...)                  # the aggregation that ran on Snowflake against AWS Glue Catalog\r\nreport.write.insertInto(\"glue_lake_db.sales_lakehouse.top_products\")","multiLine":true,":type":"snowflake-site/components/code-snippet"},"blog_text_2024033860":{"id":"blog-text-493c72a4e8","text":"\u003Cp\u003EThe aggregation, the column expressions and the write are the same code that ran on Glue. What did not change is the table layout, the catalog that governs it or the identifiers other engines use, so a downstream Amazon EMR job sees the new Iceberg snapshot on its next read without knowing the writer changed.\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"image":{"id":"image-f02d9f5cab","height":"511","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--b81dcf57-a706-4699-b02a-b1282767a0dd/figure-1.-bring-your-spark-workloads-to-snowflake-with-minimal-code-changes-and-no-catalog-migration.png?quality=85&preferwebp=true","alt":"Figure 1. Bring Your Spark Workloads to Snowflake With Minimal Code Changes and No Catalog Migration","lazyEnabled":true,"isLcpImage":true,"width":"797","title":"Figure 1. Bring Your Spark Workloads to Snowflake With Minimal Code Changes and No Catalog Migration",":type":"snowflake-site/components/image"},"blog_text_1884469967":{"id":"blog-text-575a7f47c9","text":"\u003Ch2\u003EThe payoff: Snowflake capabilities on data you never moved\u003C/h2\u003E\r\n\u003Cp\u003EBecause neither the codebase nor the catalog has to move first, the migration changes shape. Instead of one program with a date attached, it becomes a sequence of workload moves that ship as ordinary production changes. The reductions in work are concrete:\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cb\u003ENo rewrite project:\u003C/b\u003E Existing PySpark DataFrame code, Spark SQL and much of the surrounding tooling run as they are, so migration becomes mostly a connection exercise.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ENo table reregistration:\u003C/b\u003E Tables do not have to be recreated in Snowflake, and there is no dual-catalog synchronization service to build and operate.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ENo coordinated cutover:\u003C/b\u003E Amazon EMR, Trino, Athena, Apache Flink and Databricks keep reading the same tables, including snapshots that Snowflake compute wrote.\u003C/li\u003E\r\n\u003Cli\u003E\u003Cb\u003ENo rollback runbook:\u003C/b\u003E Returning a workload to its previous Spark environment is a configuration change, because the tables are still ordinary Iceberg tables in the same catalog.\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003EReversibility becomes a property of the architecture rather than an operational procedure. That is what answers the evaluation questions the migration opens with, because performance and cost can be measured on the real workload against production traffic rather than modeled from a benchmark.\u003C/p\u003E\r\n\u003Cp\u003ECustomers are already seeing the impact. \u003Ca rel=\"noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/en/customers/all-customers/snapshot/klarent/\"\u003EKlarent, a Telenav company,\u003C/a\u003E moved its heavy Spark pipelines off Amazon EMR with Snowpark Connect and now runs them 3.6x faster at 60% lower TCO, with its longest pipeline going from more than 10 hours to under 3.\u003C/p\u003E\r\n\u003Ch2\u003EModernizing the catalog\u003C/h2\u003E\r\n\u003Cp\u003EModernizing your catalog to Horizon unlocks broader platform capabilities and makes your data AI-ready. By moving to Snowflake-managed Iceberg tables, you gain features such as managed storage, automated clustering, cloning, continuous table maintenance and tuning, and cross-region disaster recovery.\u003C/p\u003E\r\n\u003Cp\u003EOwning the table lifecycle allows Snowflake to optimize performance continuously via Automatic Clustering, data compaction, manifest compaction and snapshot expiration tailored to your workload. Furthermore, other engines can still interact seamlessly through the Horizon Iceberg REST Catalog API, thereby enabling engines such as Apache Spark, Apache Flink and Trino to read and write under unified roles and governance policies.\u003C/p\u003E\r\n\u003Cp\u003EThe following table highlights some of the trade-offs between the two catalog models:\u003C/p\u003E\r\n\u003Cp\u003E \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_1087611296":{"id":"blog-text-b5a67b080e","text":"\u003Ctable\u003E\r\n\u003Cthead\u003E\u003Ctr\u003E\u003Cth\u003EDimension\u003C/th\u003E\r\n\u003Cth\u003EHorizon Catalog as the catalog of record\u003C/th\u003E\r\n\u003Cth\u003EExternal catalog as the catalog of record\u003C/th\u003E\r\n\u003C/tr\u003E\u003C/thead\u003E\u003Ctbody\u003E\u003Ctr\u003E\u003Ctd\u003ETable management\u003C/td\u003E\r\n\u003Ctd\u003EMoves to Snowflake-managed Iceberg tables\u003C/td\u003E\r\n\u003Ctd\u003EUnchanged\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EMetadata authority\u003C/td\u003E\r\n\u003Ctd\u003EHorizon Catalog with automatic table maintenance\u003C/td\u003E\r\n\u003Ctd\u003EThe external catalog, with Snowflake participating (no Snowflake-driven table maintenance)\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EOther engines connect through\u003C/td\u003E\r\n\u003Ctd\u003EHorizon Iceberg REST Catalog API\u003C/td\u003E\r\n\u003Ctd\u003EThe external catalog they already use\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EPermission model\u003C/td\u003E\r\n\u003Ctd\u003EUnified Snowflake users, roles and policies\u003C/td\u003E\r\n\u003Ctd\u003ETwo systems, each governing its own plane\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EPolicy enforcement\u003C/td\u003E\r\n\u003Ctd\u003EFull enforcement at the catalog, for every caller\u003C/td\u003E\r\n\u003Ctd\u003ELimited support Snowflake enforcement, like policies and tags over external tables\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003EManaged storage\u003C/td\u003E\r\n\u003Ctd\u003EYes\u003C/td\u003E\r\n\u003Ctd\u003ENo\u003C/td\u003E\r\n\u003C/tr\u003E\u003Ctr\u003E\u003Ctd\u003ECross-region disaster recovery\u003C/td\u003E\r\n\u003Ctd\u003EYes\u003C/td\u003E\r\n\u003Ctd\u003ENo\u003C/td\u003E\r\n\u003C/tr\u003E\u003C/tbody\u003E\u003C/table\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"blog_text_394062176":{"id":"blog-text-d90bb99379","text":"\u003Ch2\u003EConclusion\u003C/h2\u003E\r\n\u003Cp\u003EFor an organization with years invested in Spark, the payoff is work that no longer has to happen. The PySpark code keeps running on Snowpark Connect, so there is no rewrite project to staff, and the clusters stop needing to be sized, patched and capacity-planned. The teams on Trino, Apache Flink and Amazon EMR carry on against the same tables in the same catalog, which a catalog-linked database leaves in place, so there is no cutover to negotiate across groups that do not report to each other. Because a workload can be moved back with a configuration change, none of it has to be committed to in advance.\u003C/p\u003E\r\n\u003Cp\u003EThe step that proves it is a small one: Move a single workload, let it run against production traffic, then compare what it costs and how it performs against what the cluster was doing. If those numbers hold, the same tables become available to Snowflake's governance and AI without being copied into another system, so an estate that was expensive to change becomes something the organization can build new work on. For a walkthrough that builds this end to end against AWS Glue, see "\u003Ca href=\"[INSERT_LINK_HERE]\"\u003EInteroperable Lakehouse with AWS Glue, Snowflake Iceberg Tables, and Snowpark Connect for Apache Spark\u003C/a\u003E."\u003C/p\u003E\r\n\u003Cp\u003EReady to try it on your own workload? Start with \u003Ca href=\"https://docs.snowflake.com/en/migrations/sma-docs/migrating-with-cortex-code/README\" target=\"_blank\" rel=\"noopener noreferrer\"\u003Ethe CoCo Spark Migration skill\u003C/a\u003E to assess your existing Spark code, then run one job on Snowpark Connect and compare.\u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"}},":itemsOrder":["blog_text","code_snippet","blog_text_1567443652","code_snippet_596835408","blog_text_2024033860","image","blog_text_1884469967","blog_text_1087611296","blog_text_394062176"],":type":"wcm/foundation/components/responsivegrid"},"responsivegrid_premium_content_banner":{"columnCount":12,"gridClassNames":"aem-Grid aem-Grid--12 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authors"],":type":"snowflake-site/components/title-v2"},"authors":[{"authorImage":{"id":"image-e8198100d7","height":"512","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--3c685473-2104-45bf-bbb6-f17256a8bfa3/maninder-parmar.png?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"width":"512",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-aac8a22639","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/maninderjit-parmar/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Maninderjit Parmar"},"authorTitle":"Senior Software Engineer"},{"authorImage":{"id":"image-c44d0d2ed3","height":"200","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--87f010e6-54e8-4f50-9431-f25540dacc3a/saurin.jpg?quality=85&preferwebp=true","alt":"Saurin Shah","lazyEnabled":true,"isLcpImage":false,"width":"200",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-a7affabd11","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/saurin-shah/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Saurin Shah"},"authorTitle":"Senior Product Manager"},{"authorImage":{"id":"image-6cd76d19df","height":"468","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--8f743e68-f130-48b1-b75a-97f86a62db2b/anita-thomas.png?quality=85&preferwebp=true","lazyEnabled":true,"isLcpImage":false,"width":"432",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-723110bd20","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/anita-thomas/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Anita Thomas"},"authorTitle":"Staff 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