7 Steps to Seamlessly Connect SAS with Hadoop for Big Data Processing

STATISTICS HELPDESK 7 Steps to Seamlessly Connect SAS with Hadoop for Big Data Processing • A Strategic Guide for Statisticians to Master Big Data Integration • WWW.STATISTICSHELPDESK.COM • MARCH 2025

INTRODUCTION: THE CONVERGENCE OF STATISTICAL PRECISION AND BIG DATA SCALABILITY • In the age of data driven research, statisticians and biostatisticians are faced with the daunting task of sifting through datasets that cannot be subjected to normal processing. For those of you fluently enrolled in SAS—the gold standard of statistical analysis—the addition of distributed computing power to Hadoop not only enhances your strengths alone but are a career making skill for you. This presentation explains the synergy between SAS and Hadoop, mapping out a tactical roadmap to synergize the two without relying on oversimplified tutorials. Whether you are analyzing genomic sequences or public health trends, by learning this integration you can make your work more impact, scalable and reproducible.

STEP 1: LAY THE GROUNDWORK – CONFIGURE SAS AND HADOOP INTEGRATION PREREQUISITES • Prime your environment to be able to interoperate before a single line of code is executed. • Install this interface to enable bidirectional data flow (SAS/ACCESS to Hadoop) • Hadoop Distribution Compatibility: Verify your particular Hadoop ecosystem (Cloudera or Hortonworks) is supported using that version of SAS. • Authentication Protocols: Set up Kerberos or token-based authentication for secure cluster access. • Hands-On Example: Verify connectivity using LIBNAME statement: • LIBNAME myhadoop HADOOP SERVER="hadoop-server.example.com" USER="your_id" • SCHEMA="default" DATABASE=test; • This assigns a library reference (myhadoop) to a Hadoop database, letting you query tables as if they were native SAS datasets.

STEP 2: OPTIMIZE DATA MOVEMENT WITH SAS EMBEDDED PROCESS • The second advantage of SAS Embedded Process (EP) is in-database processing, that is, reducing the data amount transferred from Hadoop to SAS. Use it to: • Evict computations (for instance, aggregation, filtering) to Hadoop nodes. • It will reduce latency by avoiding extraction of full dataset. • Illustration: Instead of importing a 10TB dataset into SAS, run a summary query in Hadoop: • PROC SQL; • CREATE TABLE summary AS • SELECT gender, AVG(age) AS avg_age • FROM myhadoop.patient_data • GROUP BY gender; • QUIT;

STEP 3: LEVERAGE PROC HADOOP FOR DIRECT CLUSTER MANAGEMENT • PROC HADOOP makes it easy to execute HDFS commands from SAS, streamlining workflows: • PROC HADOOP; • HDFS COMMAND="put /local/path/data.csv /user/hadoop/input/"; • RUN; • This prepares local data to be processed by the distributed system, by copying local data to HDFS.

STEP 4: USE DS2 IN SAS FOR PARALLELIZED DATA MANIPULATION • Big data skills are most suited for DS2, SAS’s advanced programming language that supports threading and parallel execution. • PROC DS2; • THREAD patient_thread / OVERWRITE=YES; • METHOD RUN(); • SET myhadoop.health_records; • IF cholesterol > 200 THEN OUTPUT; • END; • ENDTHREAD; • DATA high_cholesterol (keep=patient_id cholesterol); • DCL THREAD patient_thread t; • METHOD RUN(); • SET FROM t; • END; • ENDDATA; • RUN; • It processes chunks of data in parallel following MapReduce paradigm.

STEP 5: VALIDATE THE DATA INTEGRITY USING SAS QUALITY KNOWLEDGE BASE (QKB) • Quality risks magnify when it comes to volume. Use SAS QKB to: • Convert unstandardized formats (e.g., dates, addresses). • Generic function to remove outliers in clinical trial datasets.

STEP 6: AUTOMATE WORKFLOWS WITH SAS STUDIO AND HADOOP CRON JOBS • Run SAS scripts as scheduled using SAS Studio’s built-in scheduler. Combine with Hadoop’s cron jobs for ETL pipeline synchronization.

STEP 7: MONITOR PERFORMANCE WITH SAS GRID MANAGER AND HADOOP METRICS • Track bottlenecks using: • SAS Grid Manager for resource allocation analytics. • Hadoop’s YARN ResourceManager to audit memory/CPU usage.

NAVIGATING COMMON PITFALLS: WHERE EVEN ADVANCED STUDENTS STUMBLE • Despite SAS and Hadoop integration, it is easy to get off track with tiny errors which halt the research. • Underestimating Data Locality: When processing data on remote nodes rather than optimizing for distance jobs take time. Fix: Data is pre- staged using PROC HADOOP at the start near the compute nodes. • Schema Mismatches: Hadoop’s schema-on-read clashes with SAS’s rigid structure. Fix: Define explicit data types in LIBNAME statements. • Thread Contention in DS2: Over-parallelizing strains cluster resources. Fix: Limit threads to the number of available cores. • Specifically, for those who require a specific solution, working with a SAS assignment helpercan close the knowledge gap without detriment to outcome of learning.

CONCLUSION • Take Your Statistical Workflows to Enterprise-Grade Analytics • SAS and Hadoop turn students into architects of scalable solutions. By processing in-database, automating and validating you’ll meet academic requirements and be at the forefront of biostatistics innovation. The tools are here—your next breakthrough is waiting.

THANK YOU STATISTICS HELPDESK • HOMEWORK@STATISTICSHELPDESK.COM • +44-166-626-0813

7 Steps to Seamlessly Connect SAS with Hadoop for Big Data Processing