Is Free/Open-Source Software a Good Substitute for SAS in Analyzing Large Datasets?
Introduction
For analyzing large datasets, many organizations and researchers find that proprietary software like SAS can be expensive and restrictive. Free and open-source software offers a compelling alternative, often with similar or better functionality. This article explores the best free and open-source software options to replace SAS for large dataset analysis, focusing on performance, ease of use, and specific features.
Altair SLC: A Compiler for SAS Language
Altair SAS Language Compiler
While Altair SLC is an interesting solution, it doesn't completely replace SAS but rather compiles SAS code to run applications without requiring third-party middleware. The Altair SAS Language Compiler supports SAS language and macro syntax, including procedures for statistics, time series analytics, operational research, machine learning, matrix manipulation, and graphing. However, its scope is more limited compared to full-scale alternatives like H2O, R, and Python.
H2O: A Free and Open-Source Alternative
H2O
H2O is a highly praised free and open-source alternative that offers robust machine learning capabilities. While it is free, it is backed by a company rather than a team of volunteers, ensuring ongoing support and development. H2O is particularly adept at scaling, often outperforming R or Python in many scenarios, and can even match or exceed SAS in terms of speed. It supports a wide array of supervised learning techniques, including Generalized Linear Models, Random Forest, Gradient Boosting Machines (GBM), Deep Learning Neural Networks, Naive Bayes, and more.
Integration with R and Python
For users familiar with these languages, integrating H2O with R through the "h2o" package can provide additional data munging capabilities. Alternatively, Python users can leverage H2O through its Python interface. This dual support allows users to leverage their existing skill sets while enjoying the benefits of H2O's advanced machine learning algorithms.
Other Tools for Large Datasets
Numpy, Pandas, and MySQl
For handling large datasets, NumPy, Pandas, and MySQL are also valuable tools. Pandas is particularly effective for data manipulation, while MySQL excels at database management, which can be crucial for large-scale data storage and retrieval.
R and Big Memory Packages
For statistical analysis, R stands out. However, it can struggle with very large datasets. To address this, newer packages like bigmemory and bigkmeans offer ways to work with larger datasets in memory and on disk.
Hadoop for Big Data Processing
Hadoop is an excellent choice for processing vast amounts of data and performing basic sort and grouping analysis. Hadoop can handle significantly more data than SAS and is particularly suitable for big data processing and storage, though it requires more expertise to use effectively.
Conclusion
Choosing a free and open-source alternative to SAS for analyzing large datasets depends on your specific needs and the skills of your team. H2O, R, and Python, along with tools like bigmemory and bigkmeans, offer robust alternatives with excellent performance. Understanding the nature of your data and the analysis you need to perform will help you determine the best tool for your requirements.