Working with Big Data in SAS: Techniques and Best Practices

In today’s fast-paced digital world, organizations collect vast amounts of data daily, from customer interactions to business operations. Managing and analyzing this massive volume of data—often referred to as “big data”—has become crucial for making data-driven decisions. SAS (Statistical Analysis System), with its robust data management and analytics capabilities, is one of the most effective tools for handling big data.

As organizations continue to generate terabytes and petabytes of data, traditional data processing methods may struggle to scale. This article explores techniques and best practices for working with big data in SAS, ensuring efficient data management, analytics, and performance optimization.

Understanding Big Data and Its Challenges

Big data refers to datasets so large and complex that traditional data processing tools cannot efficiently handle them. Big data is often characterized by the “3Vs”:

• Volume: The amount of data generated, stored, and processed, which can run into terabytes or more.
• Velocity: The speed at which data is generated and processed, often in real time.
• Variety: The types of data, including structured, unstructured, and semi-structured data (e.g., text, videos, and social media).

Managing big data poses several challenges, including:

• Scalability: The ability to handle increasing amounts of data without performance bottlenecks.
• Storage: Efficient storage solutions for large datasets.
• Processing Power: The need for computational resources to perform analysis in a timely manner.
• Data Integration: Combining data from various sources into a unified format.

Why Use SAS for Big Data?

SAS offers several advantages when working with big data, such as:

• Scalable Architecture: SAS provides solutions that can scale across multiple machines, making it suitable for distributed computing.
• Integration with Hadoop and Cloud Platforms: SAS can seamlessly connect with big data storage systems like Hadoop, as well as cloud platforms, enabling distributed data processing.
• In-Memory Processing: SAS’s in-memory analytics allow for faster computations, which is critical for big data.
• Parallel Processing: SAS supports multi-threaded and distributed processing, enabling the simultaneous handling of large datasets.

Techniques for Managing Big Data in SAS

To efficiently work with big data, SAS provides several advanced features and techniques designed for high performance.

1. Using SAS High-Performance Analytics (HPA)

One of the key components for handling big data in SAS is High-Performance Analytics (HPA). HPA allows for data processing across multiple machines and clusters, leveraging distributed computing to handle large datasets.

• In-memory computing: Data is loaded into memory and processed directly, reducing the need to read from and write to disk. This dramatically speeds up analysis.
• Parallel execution: Tasks are divided into smaller chunks and processed in parallel across multiple cores or machines.

For example, if you’re running a logistic regression on a large dataset:

PROC HPLOGISTIC DATA=big_data;
    MODEL target_var = predictor1 predictor2;
RUN;

The HPLOGISTIC procedure runs parallel computations to optimize performance.

2. Partitioning Data for Parallel Processing

When working with big data, it’s essential to partition your data to enable parallel processing. Partitioning allows SAS to divide the workload and run processes on different nodes simultaneously.

You can use PROC SORT to partition the data:

PROC SORT DATA=big_data OUT=sorted_data;
    BY partition_var;
RUN;

Partitioning the data helps in distributing the workload across different threads, enhancing efficiency, especially when running on a cluster environment.

3. Leveraging PROC FEDSQL for Efficient Queries

When dealing with massive datasets, traditional PROC SQL queries may struggle with performance. PROC FEDSQL is optimized for big data and allows for high-performance SQL queries within SAS. It can also directly access distributed databases like Hadoop.

Here’s an example of how PROC FEDSQL can be used:

PROC FEDSQL;
    CREATE TABLE results AS 
    SELECT var1, var2, COUNT(*) AS frequency
    FROM big_data
    GROUP BY var1, var2;
QUIT;

FEDSQL allows for parallel execution of queries and is faster when working with large datasets, especially in a distributed data environment.

4. Data Storage Optimization: Using SAS LASR Server

For big data, efficient storage and retrieval are critical. SAS’s LASR (Lightning Analytics Server) is designed to handle large datasets in-memory, significantly reducing the time it takes to perform data analytics.

To load data into the LASR server:

PROC LASR ADD DATA=big_data;
RUN;

Once the data is loaded into memory, the LASR server can handle lightning-fast computations across distributed clusters, making it ideal for interactive reporting and analytics.

5. Using the DATA Step in SAS for Big Data Manipulation

The DATA step is one of the most powerful tools in SAS for data manipulation. When dealing with big data, optimizing your DATA steps can significantly improve performance.

Here are a few best practices:

• Use WHERE statements instead of IF statements to filter data efficiently:

  DATA filtered_data;
      SET big_data;
      WHERE condition;
  RUN;

This filters the data during the read operation, reducing the amount of data loaded into memory.

• Indexing: For large datasets, creating an index on frequently queried columns can speed up searches.

  PROC DATASETS LIBRARY=libname;
      MODIFY big_data;
      INDEX CREATE var1;
  QUIT;

• Compressing Data: Compressing datasets can save storage space and reduce I/O operations:

  DATA compressed_data(compress=yes);
      SET big_data;
  RUN;

Best Practices for Big Data Analysis in SAS

1. Efficient Data Import and Export

Big data often resides in distributed systems or cloud-based platforms. SAS integrates with Hadoop, Amazon S3, Google Cloud Storage, and other cloud services to import and export large datasets efficiently.

• Hadoop Integration: SAS provides seamless integration with Hadoop through PROC HADOOP and SAS/ACCESS Interface to Hadoop.

  LIBNAME myHadoop HADOOP SERVER="hadoop_server" SCHEMA="hadoop_schema";

• Cloud Storage Integration: You can also connect SAS to cloud storage for easy data retrieval and storage, particularly for large datasets.

2. Data Sampling for Faster Prototyping

When working with massive datasets, it can be useful to create a sample dataset for initial prototyping and testing of your models or queries.

PROC SURVEYSELECT DATA=big_data OUT=sample_data METHOD=SRS SAMPSIZE=10000;
RUN;

Sampling can significantly speed up the development process by reducing the computational load, especially when testing different hypotheses or refining models.

3. Memory Management

Efficient memory management is crucial when working with big data. SAS allows users to control memory allocation with options like MEMSIZE and SORTSIZE, which determine the amount of memory SAS can use for data processing.

You can adjust the memory settings based on the size of your dataset:

OPTIONS MEMSIZE=8G SORTSIZE=4G;

Allocating sufficient memory helps prevent bottlenecks during data processing, particularly when working with high-performance procedures.

4. SAS Grid Computing for Distributed Workloads

For large-scale operations, SAS Grid Computing provides a scalable infrastructure for distributing jobs across multiple servers. Grid computing allows for load balancing, job scheduling, and fault tolerance, enabling organizations to handle big data more efficiently.

5. Utilizing SAS Visual Analytics for Big Data Visualization

Visualizing big data can provide valuable insights into trends and patterns. SAS Visual Analytics is a powerful tool for creating interactive visualizations, dashboards, and reports, even for large datasets.

Visualizations like heat maps, scatter plots, and geographical maps can help users identify key patterns and trends in big data, making it easier to make informed decisions.

Conclusion

SAS is an industry leader in big data analytics, offering a suite of tools and technologies to manage, process, and analyze large datasets efficiently. Whether you’re dealing with structured or unstructured data, SAS provides scalable solutions that enable high-performance data manipulation, modeling, and reporting.

By following the techniques and best practices outlined in this article, you can leverage the full power of SAS for big data analytics, driving valuable insights and making better data-driven decisions for your organization. From parallel processing with PROC FEDSQL to scalable in-memory analytics with SAS LASR Server, SAS has the tools to make working with big data seamless and effective.

Big data is here to stay, and mastering the techniques to work with it in SAS will position you at the forefront of data analytics innovation.