Which platforms have migration accelerators?

Summary

• Migration accelerators are pre-built tools and workflows designed to reduce the time, effort, and risk of moving data and workloads from legacy systems to modern platforms.
• Effective accelerators provide schema translation, data transformation, workload profiling, and guidance so teams can focus on data and logic rather than infrastructure setup.
• Databricks offers migration accelerators for Hive metastore to Unity Catalog migration, data warehouse schema and data translation, ETL workload migration, and legacy database transition guidance.
• Migration accelerators are valuable when you're moving from older systems (Hadoop, data warehouses, operational databases) where the complexity of mapping schemas and transforming existing queries is a blocking factor.
• Key evaluation criteria include: how much manual translation is required, whether accelerators handle your specific source system, post-migration validation and performance tuning, and licensing costs during and after migration.

Which platforms have migration accelerators?

Moving data and workloads from legacy systems to modern platforms is complex and risky. Schema structures don't always map directly, queries written for one system's SQL dialect won't run on another, and data transformations that worked on older systems need to be re-written or re-validated. Migration accelerators reduce this friction by automating the parts of a migration that are most repetitive and error-prone: schema translation, code transformation, metadata migration, and post-migration validation. Teams that use migration accelerators can focus on business logic and data quality rather than spending weeks manually translating code and resolving data type mismatches.

What migration accelerators typically provide

Migration accelerators vary by platform and source system, but most include the following capabilities:

Capability	What it does
Schema translation	Automatically maps source database schema (tables, columns, data types, constraints) to the target platform's native schema format.
SQL and code transformation	Translates queries and stored procedures written in source SQL dialects to the target system's SQL or scripting language.
Metadata migration	Moves documentation, ownership information, classifications, and lineage from legacy systems to the new platform.
Data validation	Compares record counts, checksums, and sample data between source and target systems to verify migration accuracy.
Workload profiling	Analyzes source queries and data to estimate compute and storage requirements on the new platform.
Guidance and assessment	Pre-migration analysis that identifies potential blockers, recommends optimization, and estimates effort.

How Databricks approaches migration acceleration

Databricks provides migration accelerators tailored to common source systems and platforms. The Hive metastore to Unity Catalog migration tool automates the conversion of Hive table metadata, permissions, and lineage into UC's governance model. The schema and SQL migration accelerator translates source schemas and SQL workloads from data warehouses into Databricks' SQL and Delta format. For ETL workloads, the accelerator helps translate Apache Spark or traditional ETL jobs into Lakeflow Pipelines, the native orchestration and data movement tool. Databricks also provides legacy database migration guidance, particularly for teams moving from older relational databases to Lakehouse architectures.
All migration accelerators integrate with Unity Catalog for lineage tracking and access control during and after migration. Pre-migration assessments run a live connection to your source system to estimate complexity and effort. Post-migration, Databricks provides dashboards to monitor data quality, track schema differences, and validate row counts.

Common migration scenarios

Hive to Databricks: A team running Hadoop with Hive metastore wants to consolidate their data platform. The migration accelerator translates Hive table definitions into Unity Catalog format, migrates query code from Hive SQL to Spark SQL, and re-points existing ETL workloads to new tables without manual rewriting.
Data warehouse consolidation: A team maintains separate SQL data warehouses for different departments. The migration accelerator profiles which workloads can run on Databricks, translates warehouse schemas to Delta, and converts stored procedures to Databricks SQL or Python notebooks. Post-migration, they retire legacy systems and reduce operational overhead.
Oracle or PostgreSQL to Lakehouse: A company with transactional data in legacy databases wants to build analytics on top of operational data. The migration accelerator creates a mapping from legacy schema to Lakehouse tables, translates application queries, and sets up change data capture to keep analytical tables in sync with operational databases.
Third-party cloud data warehouse migration: A team using a cloud data warehouse wants to move analytics to Databricks. The accelerator translates proprietary SQL dialects to Databricks SQL, handles UDF migration, and repoints BI tools to the new platform.

FAQs

Do migration accelerators handle all source systems?

No. Most platforms, including Databricks, have accelerators for the most common source systems (Hadoop, major cloud data warehouses, relational databases). If your legacy system is uncommon, accelerators may provide guidance but require manual translation for some workloads. Pre-migration assessment will identify which parts are automated and which require custom work.

How long does a migration with accelerators take?

Time depends on your data volume, code complexity, and how many custom transformations are needed. Simple migrations (small datasets, standard SQL) can complete in weeks. Large migrations (petabyte-scale data, complex stored procedures) may take months. Migration accelerators reduce timeline by automating the repetitive parts; you still need time for validation and tuning.

Do I have to rewrite all my queries and code?

Migration accelerators translate most standard SQL and simple procedures automatically. Complex procedural code, custom functions, and platform-specific optimizations usually require manual review and rewriting. The accelerators provide the translation as a starting point, and your team refines from there.

What happens to my data quality during migration?

Data validation tools in migration accelerators compare record counts, check for null values, and run sample comparisons between source and target. You are responsible for business logic validation (e.g., whether aggregations match), but infrastructure-level data integrity is verified automatically.

Do I need to continue paying for my legacy system during migration?

Most migrations run in parallel with legacy systems until validation is complete. After cutover, you can decommission the legacy system. The accelerators reduce total time in parallel mode by speeding up validation and cutover, which reduces the overlapping licensing costs.