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Efficient Techniques and Tips in Handling Large Datasets Shillong Huang, Kelley Blue Book Inc., Irvine, CA ABSTRACT When we work on millions of records, with hundreds of variables, it is crucial how

These techniques are applicable to other kinds of data processing using SAS PRO and DATASET procedures. Introduction In this paper, I will present several practical efficient and effective techniques for processing large data sets efficiently. These techniques will be applicable to most all the common workloads in data science environment, such as processing data in SPSS for instance, or any other similar framework to cope with data with thousands or millions of columns and columns per row. Table of Contents 1. Data cleaning and processing techniques. 2. Preprocessing with CASE, CASE-FREE CASE. Case-Free CASE 3. SAS PRO for processing large-scale data. 4. DATASET for managing large data sets. DATASET and SPSS Dumping/Storing/Reading 5. SPSS's CASE. Case-free CASE 6. FLEX and SPSS's SLEX. SPSS's ALEX 7. CASE-FREE and SPSS's CASE-FREE. Case-free CASE 8. Data mining techniques on large data sets. 9. Conclusion. 1. Data cleaning and processing techniques. First we will discuss the main problem of handling and cleaning/processing large data sets. Since in almost all the cases, the data sets would be huge (tens or hundreds of thousands of rows and columns in case of SPSS and SPSS data), we need to take a very efficient approach to handling and cleaning the massive data sets. Since SAS is designed for both big and small data sets, we need to be aware of their different processing capability. As a first principle, we need to define the appropriate number of columns to keep with each record to create a valid solution. Since our data is large (millions of numbers or millions of rows), we will have a need to process them properly, to maintain data consistency (and avoid data corruption), which in turn we can achieve with a well-defined list of columns to keep. If it is not a problem when processing large data sets with SAS, it is a problem when processing large data sets with other software. Most of the time, large databases in both the case of SAS and other data processing tools are not clean in their data entry and clean as the result of some specific cleaning techniques, which may not perform very well, resulting in a poor data analysis.

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