应对SAS到PySpark迁移中的关键挑战

SAS to PySpark MigrationFuture-proofing enterprise analytics © 2025 Hexaware Technologies Limited. All rights reserved•Underestimating Data Transformation Complexity•Not Accounting for Task Performance Optimization•Overlooking Data Quality and Integrity Standards•Inadequate Skill Set and PySpark Training•Neglecting Updates to Enterprise Analytical Models•Insufficient Conversion Testing and Validation•Integration with Existing Systems and Workflows•Understanding Customer Needs•Overcoming Technical Challenges•Nurturing Collaboration and Support•Amaze®for Data and AI: Your Platform to Drive Strategic Action010203Key Challenges in SAS to PySpark Migrationand Strategic ActionsWhat’s Important in Real World SAS toPySpark Migration StrategyAccelerate Your Shift to PySpark withTrusted StrategyContents © 2025 Hexaware Technologies Limited. All rights reservedIn today’s world, where AI is a must for enterprise business intelligencestrategies, it’s pivotal to transition from traditional analytics systems likeStatistical Analytics Systems (SAS) to more scalable and flexible platformssuch as PySpark.Migrating from SAS to PySpark modernizes the analytics stack and alignswith the growing enterprise trend of adopting open-source technologies. Thistrend significantly reduces licensing costs and fosters innovation throughcommunity-driven development.According to the McKinsey Global Institute, advanced analytics—includingplatforms like PySpark—can unlock between $9.5 trillion to $15.4 trillion invalue across industries globally. A swift migration can set the stage for AI andadvanced analytics, improving how enterprises expand on insights and createvalue from vast amounts of data efficiently.Though the journey from SAS to PySpark is not without its challenges. Manyenterprises encounter a variety of hurdles during this transition, ranging fromstrategic migration issues to dealing with the differences in programmingrequired to stabilize the shift.In this strategic guide, we will examine the key challenges faced during SAS toPySpark migration and consider actionable strategies to accelerate your shift.Further, dive into how Hexaware’s data and analytics services experts ace thetransition. 3 © 2025 Hexaware Technologies Limited. All rights reservedBy understanding the common pitfalls in this migration process andimplementing effective strategies to address them, enterprises can streamlinetheir transition to PySpark.A proactive approach minimizes disruptions and paves the way for increasedscalability, flexibility, and efficiency in data operations. Let’s examine some ofthese challenges and explore ways to navigate them successfully.SAS and PySpark differ significantly in their data handling approaches. SAS isrenowned for its user-friendly data manipulation capabilities, while PySparkexcels in distributed data processing. Migrating from SAS to PySpark involvestranslating procedural SAS code into PySpark’s functional programming style,which can be particularly challenging for complex SAS programs with macros.Additionally, PySpark’s reliance on RDDs and DataFrames requires a deeperunderstanding of its architecture. This shift may lead to integration challengeswith existing systems.Strategic Action1.Analyze Existing Processes:Conduct a detailed review of your currentSAS data transformation workflows. Identify how these can be replicatedor optimized in PySpark.Underestimating Data TransformationComplexityMany enterprises encounter complexities when moving away from legacySAS systems, which often have extensive codebases and establishedbusiness logic. Translating these unique functionalities into the PySparkenvironment can lead to roadblocks, such as performance issues andcompatibility concerns.Additionally, the learning curve associated with adopting new technologieslike PySpark can be significant for teams accustomed to working with SAS.It’s important to recognize these hurdles early on to prepare for the journeyahead. Key Challenges in SAS toPySpark Migration andStrategic Actions01 4 5© 2025 Hexaware Technologies Limited. All rights reservedMigrating from SAS to PySpark presents challenges with analytics taskperformance, especially when handling large datasets. SAS uses a centralizedprocessing model, which simplifies performance management. In contrast,PySpark’s distributed architecture requires careful optimization to avoidbottlenecks.For example, improper use of user-defined functions (UDFs) in PySpark canlead to inefficiencies. UDFs are executed on the driver node instead of beingdistributed across worker nodes, increasing communication overhead andslowing execution.Additionally, PySpark’s reliance on partitioning, caching, and memory tuningdemands a deeper understanding of its architecture, which can be dauntingfor teams accustomed to SAS’s straightforward approach.Not Accounting for Task PerformanceOptimizationStrategic Action1.Explore PySpark Optimization Techniques:Familiarize with keytechniques like caching, partitionin