Migrating from Azure Synapse to Databricks 

| 5 Minutes

Migrating from Azure Synapse to Databricks: Discover key challenges and solutions for optimizing PySpark code & ensure seamless migration with expert guidance

Migrating from Azure Synapse to Databricks 

Migrating from Azure Synapse to Databricks can be a complex undertaking, especially when dealing with PySpark. While both platforms leverage PySpark for data processing, subtle differences in their implementations can introduce unexpected challenges. This post dissects five critical PySpark considerations for software engineers and data professionals migrating from Azure Synapse to Databricks

Common Pitfalls in Migrating from Azure Synapse to Databricks 

1. Schema Enforcement and Evolution: “Not as Flexible as You Think!” 

Databricks adopts a more rigorous approach to schema enforcement compared to Azure Synapse. When writing data to Delta tables, Databricks enforces schema compliance by default. This means that if the schema of the incoming data doesn’t perfectly align with the target table schema, write operations will fail. This behavior differs from Azure Synapse, where schema evolution might be handled more permissively, potentially leading to unexpected data transformations or inconsistencies. 

Solution: 

  • Perform a comprehensive analysis and reconciliation of schema definitions between source data and target Delta tables in Databricks. 
  • Utilize the mergeSchema option when writing to Delta tables to accommodate schema evolution and prevent write failures due to mismatches. 
  • For non-Delta tables, employ schema enforcement techniques like defining the schema explicitly using schema() during data loading or utilizing spark.createDataFrame() with a predefined schema. 

2. Performance Optimization 

Performance characteristics can diverge significantly between Azure Synapse and Databricks due to variations in cluster configurations, resource management, and underlying Spark optimizations. Code optimized for Azure Synapse might not translate to optimal performance in Databricks, necessitating adjustments to achieve desired execution speeds and efficient resource utilization. 

While both platforms are built upon Apache Spark, their underlying architectures and optimization strategies differ, leading to varying performance profiles. These differences can manifest in various aspects of PySpark job execution, including: 

Data Serialization:

Databricks, by default, utilizes a more efficient serialization format (often Kryo) compared to Azure Synapse. This can lead to reduced data transfer overhead and improved performance, especially for large datasets. 

Issue: Code relying on Java serialization in Synapse might experience performance degradation in Databricks. 

Solution: Explicitly configure Kryo serialization in your Databricks PySpark code. 

Shuffling:

Shuffling, the process of redistributing data across the cluster, can be a major performance bottleneck in Spark applications. Databricks employs optimized shuffle mechanisms and configurations that can significantly improve performance compared to Azure Synapse. 

Issue: Inefficient shuffle operations in Synapse code can become even more pronounced in Databricks. 

Solution: Analyze and optimize shuffle operations in your PySpark code: 

  • Adjust the number of shuffle partitions (spark.sql.shuffle.partitions) based on data size and cluster configuration. 
  • Utilize broadcast joins to minimize data shuffling for smaller datasets. 
Caching:

Caching frequently accessed data in memory can drastically improve performance by reducing redundant computations. Databricks provides efficient caching mechanisms and configurations that can be fine-tuned to optimize memory utilization and data access patterns. 

Issue: Code not leveraging caching in Synapse might miss out on significant performance gains in Databricks. 

Solution: Actively cache DataFrames in your Databricks PySpark code. 

Resource Allocation:

Databricks offers more granular control over cluster resources, allowing you to fine-tune executor memory, driver size, and other configurations to match your specific workload requirements. 

Issue: Code relying on default resource allocation in Synapse might not fully utilize the available resources in Databricks. 

Solution: Configure Spark properties to optimize resource allocation. 

By carefully considering these performance optimization techniques and adapting your PySpark code to the specific characteristics of Databricks, you can ensure efficient execution and maximize the benefits of this powerful platform. 

3. Magic Command Divergence 

Azure Synapse and Databricks have distinct sets of magic commands for executing code and managing notebook workflows. Magic commands like %run in Azure Synapse might not have direct equivalents in Databricks, requiring code refactoring to ensure compatibility and prevent unexpected behavior. 

Magic commands provide convenient shortcuts for common tasks within notebooks. However, these commands are not standardized across different Spark environments. Migrating from Azure Synapse to Databricks requires understanding these differences and adapting your code accordingly. 

Issue: Code relying on Azure Synapse magic commands might not function correctly in Databricks. For example, the %run command in Synapse is used to execute external Python files or notebooks, but Databricks uses dbutils.notebook.run() for similar functionality. 

Solution: 

  • Familiarize yourself with the available magic commands in Databricks and their corresponding functionalities. 
  • Refactor code that uses Azure Synapse magic commands to utilize Databricks equivalents or alternative approaches. 
  • Leverage Databricks utilities like dbutils for managing notebook workflows and executing external code. 

Tricky Scenarios in Migrating from Azure Synapse to Databricks 

4. UDF Portability: “Don’t Assume It’ll Just Work!” 

User-defined functions (UDFs) written in Azure Synapse might require modifications to ensure compatibility and optimal performance in Databricks. Differences in Python versions, library dependencies, and execution environments can affect UDF behavior, potentially leading to errors or performance degradation. 

UDFs are essential for extending the functionality of PySpark and implementing custom logic. However, UDFs can be sensitive to the specific Spark environment in which they are executed. Migrating from Azure Synapse to Databricks requires careful consideration of potential compatibility issues. 

Issue: UDFs might depend on specific Python libraries or versions that are not available or compatible with the Databricks environment. Additionally, the way UDFs are defined and registered might differ between the two platforms. 

Solution: 

  • Thoroughly test and validate UDFs in the Databricks environment to identify any compatibility issues. This includes checking for library dependencies, Python version compatibility, and any environment-specific configurations. 
  • Ensure that UDFs are compatible with the Python version and libraries available in the Databricks cluster. If necessary, update the UDF code to use compatible libraries or versions. 
  • Consider using Pandas UDFs for improved performance in Databricks, especially for vectorized operations on Pandas Series. Pandas UDFs leverage the Pandas library for efficient data manipulation within UDFs, and Databricks provides optimized execution for these types of UDFs. 

5. Notebook Conversion 

Migrating from Azure Synapse to Databricks like notebooks might not be a straightforward process. Direct conversion can result in syntax errors, functionality discrepancies, and unexpected behavior due to differences in notebook features and supported languages. 

Notebooks are essential for interactive data exploration, analysis, and development in Spark environments. However, notebooks can contain code, visualizations, and markdown that might not be directly compatible between Azure Synapse and Databricks. This can include differences in magic commands, supported languages, and integration with other services. 

Issue: Notebooks might contain magic commands, syntax, or dependencies that are specific to Azure Synapse and not supported in Databricks. For example, Synapse notebooks might use magic commands like %%synapse or %%sql with specific syntax that is not compatible with Databricks. 

Solution: 

  • Manual Conversion: The most reliable approach is to manually review and update notebooks to ensure compatibility with Databricks syntax, magic commands, and supported libraries. This involves: 
  • Identifying and replacing any Synapse-specific magic commands with their Databricks equivalents (e.g., dbutils.notebook.run() instead of %run). 
  • Updating any code that relies on Synapse-specific libraries or APIs to use Databricks-compatible alternatives. 
  • Ensuring that the notebook uses a supported language and syntax in Databricks (e.g., PySpark, Spark SQL). 
  • Conversion Tools: Explore tools or scripts that can assist in converting notebooks between the two platforms, but be prepared for manual adjustments and potential limitations. These tools might not handle all cases perfectly and might require manual intervention to fix any remaining inconsistencies. 
  • Azure Data Factory: Consider using Azure Data Factory to orchestrate data transfer and notebook execution, providing a more robust and manageable migration process. Azure Data Factory can help automate the migration process and handle dependencies between notebooks, making it easier to manage complex migrations. 

Conclusion 

Migrating from Azure Synapse to Databricks requires a meticulous approach and a deep understanding of the nuances between the two platforms. By proactively addressing the potential pitfalls outlined in this post, data engineers and software professionals can ensure a smooth transition and unlock the full potential of Databricks for their data processing and machine learning endeavors. 

Key Takeaways for Migrating from Azure Synapse to Databricks 

  • Schema Management: Rigorous schema enforcement in Databricks necessitates careful schema design and the use of schema evolution features like mergeSchema for Delta tables. 
  • Performance Tuning: Optimize PySpark code for Databricks by leveraging efficient serialization, minimizing shuffles, utilizing caching, and fine-tuning resource allocation. 
  • Magic Command Compatibility: Adapt or replace Azure Synapse magic commands with their Databricks equivalents or alternative approaches. 
  • UDF Portability: Thoroughly test and validate UDFs in Databricks, ensuring compatibility with Python versions, libraries, and execution environments. 
  • Notebook Conversion: Manually review and update notebooks or explore conversion tools and orchestration frameworks like Azure Data Factory to streamline the migration process. 

Why Sparity 

When migrating from Azure Synapse to Databricks, Sparity stands out as a trusted partner. The deep cloud and AI expertise at Sparity enables successful transitions through addressing PySpark optimization alongside schema management and performance tuning challenges. Our team uses proven cloud migration skills to enhance Databricks workflows while enabling organizations to reach optimal performance and complete merger with existing infrastructure. By selecting Sparity you can confidently access the maximum capabilities of your Databricks environment. 

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