Data platform modernization
Modernize Snowflake data, SQL, Snowpark, and analytics workloads on the Databricks Lakehouse Platform with a plan grounded in your estate.
Assess migration patterns, move approved data into Delta Lake, modernize pipelines and code, map governance to Unity Catalog, and validate business outputs before cutover. Databricks is not automatically a better or less expensive fit for every workload.
Workload compatibility, automation coverage, cost, and timing depend on the source environment and current platform support.
Migration patterns
A migration can combine assessment tooling, federation, connector-based ingestion, staged files, and phased coexistence. Select a pattern for each workload rather than forcing one method across the estate.
Evaluate Databricks Lakebridge for profiling, analysis, supported SQL conversion, and reconciliation tasks.
Lakebridge is an accelerator, not a guarantee of automatic conversion. Check the current Databricks Labs project maturity, source coverage, target support, and remediation requirements.
Use governed query access to selected Snowflake data while workloads are assessed or migrated in phases.
Federation is access to external data, not physical migration. Query federation and catalog federation for eligible Snowflake-managed Iceberg tables have different requirements; benchmark source pushdown, connectivity, and permissions.
Use the Databricks Runtime Snowflake Spark data source connector for supported programmatic reads and writes.
Validate runtime and compute support, authentication, network paths, pushdown, partitioning, write behavior, retries, and incremental-load semantics.
Use Snowflake COPY INTO location to create staged files, then ingest approved data into Delta Lake.
Test file format, partitioning, compression, null and empty-string handling, timestamps, nested data, schema evolution, encryption, and storage access.
Keep selected Snowflake workloads in place temporarily while approved data products and consumers move to Databricks.
Define data ownership, freshness, security boundaries, consumer routing, cost monitoring, and retirement criteria.
Lakehouse Federation is for querying external data, not full physical migration. The Spark connector supports specific data movement patterns, not automatic workload conversion. Lakebridge does not eliminate the need to review and test converted code.
Workload mapping
These are assessment starting points, not guaranteed one-to-one conversions. Confirm current feature support and test target behavior for each workload.
| Snowflake component | Potential Databricks destination or pattern | Migration consideration |
|---|---|---|
| Snowflake database and schema | Unity Catalog catalog and schema | Define naming, ownership, permissions, and workspace access. |
| Managed tables | Delta Lake managed or external tables | Choose bulk or incremental ingestion and establish schema and history strategy. |
| Snowflake-managed Iceberg tables | Unity Catalog catalog federation for eligible tables, or Delta migration | Validate object-storage location, Iceberg metadata, and current federation limitations. |
| Views and secure views | Databricks SQL views or governed data products | Translate SQL and recreate access semantics; validate effective permissions. |
| Virtual warehouses | Databricks SQL warehouses and/or Spark compute | Compute and billing models differ; size from workload benchmarks, not a direct mapping. |
| Snowpipe and Snowpipe Streaming | Auto Loader, supported connectors, or a streaming ingestion design | Preserve latency, delivery, deduplication, checkpoint, and recovery requirements. |
| Streams and Tasks | Delta Change Data Feed where suitable and Lakeflow Jobs | Recreate change capture, dependencies, schedules, retries, and idempotency. |
| Dynamic Tables | Lakeflow Spark Declarative Pipelines, jobs, or managed Delta transformations | Rebuild refresh and dependency semantics; select the target by workload behavior. |
| Snowpark Python, Java, and Scala | PySpark, Spark SQL, Scala, notebooks, jobs, or supported application services | Review APIs, packages, runtime, session patterns, and distributed execution behavior. |
| Roles, grants, masking, and row access policies | Unity Catalog privileges, identities, governed views, and supported row/column controls | Recreate effective access and test with representative identities; role names alone do not migrate security. |
| BI and application consumers | Databricks SQL endpoints, Delta data products, and updated connections | Repoint consumers in waves and validate semantic models, measures, refresh, and access. |
Lakehouse target
Select platform capabilities according to governance, transformation, query, latency, cloud, and operational requirements.
Plan catalogs, schemas, ownership, identities, storage credentials, external locations, permissions, lineage, and audit according to supported capabilities.
Design governed Delta tables for batch and streaming data products, with schema, history, file layout, and maintenance requirements tested.
Assess SQL warehouses, curated views and marts, BI connectivity, query behavior, concurrency, and workload isolation.
Modernize suitable Snowpark and transformation logic using supported Spark APIs, PySpark, Spark SQL, Scala, and managed libraries.
Recreate scheduling, dependencies, retries, monitoring, and batch or streaming pipeline behavior using appropriate supported patterns.
Evaluate Auto Loader, supported connectors, the Snowflake Spark connector, Lakehouse Federation, and staged cloud files per source and workload.
Delivery approach
Inventory databases, schemas, tables, Iceberg, views, SQL, stored procedures, UDFs, Snowpark, warehouses, ingestion, orchestration, security, BI consumers, and dependencies. Baseline data volumes, changes, query patterns, SLAs, and costs.
Decide what to migrate, federate temporarily, redesign, retain, or retire. Select Lakebridge, Spark connector, staged unload, federation, or a combined approach by workload.
Define Unity Catalog, identities, storage, networking, compute, Delta Lake data products, workspace boundaries, operations, and BI connectivity.
Test representative tables, semi-structured types, SQL, Snowpark, incremental ingestion, security, performance, and data reconciliation. Use findings to refine scope and estimates.
Load historical data, implement change capture and ingestion, convert or rewrite SQL and procedures, modernize Snowpark, and rebuild orchestration with appropriate Databricks patterns.
Compare source and target data and business outputs, run parallel workloads where appropriate, obtain business-owner sign-off, redirect consumers, and retain agreed rollback options.
Estate assessment
An inventory helps establish scope, sequencing, and the proof of concept. Gather what is available; never send secrets or production data through a website form.
Common questions
No. Tools such as Lakebridge may help with assessment and supported conversion patterns, but Snowflake objects, SQL, procedures, Snowpark, orchestration, security, and downstream consumers require workload-specific review, remediation, and testing.
Databricks Lakehouse Federation supports querying external Snowflake data for appropriate workloads. This provides federated access; it does not physically move the data. Benchmark supported pushdown, permissions, connectivity, and performance.
Databricks documents catalog federation for eligible Snowflake-managed Iceberg tables. Eligibility depends on storage and metadata requirements. Verify current limitations for the specific table and cloud environment.
Lakebridge is a Databricks Labs migration toolkit with capabilities described for profiling, analysis, SQL conversion, and reconciliation. Confirm current project status, source coverage, supported targets, and support expectations before relying on it.
Inventory Snowpark APIs, packages, UDFs, procedures, and session/data-access assumptions. Rebuild suitable logic with PySpark, Spark SQL, notebooks, jobs, or supported libraries, then regression-test outputs and distributed behavior.
Assess change-capture, scheduling, dependency, retry, and idempotency behavior. Suitable target patterns may use Delta Change Data Feed, Lakeflow Jobs, pipelines, or other supported ingestion and orchestration services. Snowflake object semantics do not transfer automatically.
Assess Snowpipe and Snowpipe Streaming separately for latency and delivery requirements; potential targets include Auto Loader or supported streaming patterns. Dynamic Table logic may be rebuilt with Lakeflow Spark Declarative Pipelines, jobs, or managed Delta transformations.
No. Map identities, roles, grants, masking, row access behavior, storage permissions, and network controls to the target model. Test effective access with representative users and service identities.
Savings are not guaranteed. Compare Snowflake compute, cloud services, storage, ingestion, and transfer with Databricks compute, cloud infrastructure, object storage, networking, BI, migration, and parallel-run costs using representative workloads.
Duration depends on data volumes, object and workload counts, SQL and Snowpark complexity, dependencies, governance, testing, and cutover constraints. An estate inventory and representative pilot provide a more reliable estimate.
Office Solution AI Labs
A migration engagement can cover estate assessment, workload classification, target architecture, data and code migration, pipeline redesign, governance, BI transition, reconciliation, cutover, and enablement. Confirm delivery scope, tools, staffing, timing, and commercial terms for your environment.
No Databricks partnership, certification, marketplace listing, automated coverage, customer outcome, fixed timeline, or savings are implied by this service description.
We are an authorized implementation partner of Snowflake, Databricks, Amazon, Automation Anywhere, Denodo, DataDog, New Relic, and Elastic.
