What is ICETOOL in Mainframe and its usage


In the vast world of mainframes, where punch cards and green screens once ruled, tools like ICETOOL have emerged as indispensable utilities. ICETOOL may not be as well-known outside the mainframe community, but it’s a powerhouse when it comes to data manipulation and processing. In this article, we’ll take a casual stroll through the fascinating world of ICETOOL, uncovering its capabilities and the magic it brings to mainframe data management.

The Origins of ICETOOL

Imagine a time when computers filled entire rooms, and the concept of personal computing was a distant dream. It was in this era, during the early days of mainframe computing, that ICETOOL was born. Developed by IBM, ICETOOL is a versatile utility program designed to handle various data processing tasks efficiently.

Photo by Kevin Ku on Pexels.com

While the world has moved on to sleek laptops and smartphones, mainframes continue to play a pivotal role in industries like banking, healthcare, and logistics. ICETOOL remains a trusted companion for mainframe professionals, helping them sort, merge, and transform data with ease.

The Swiss Army Knife of Mainframes

ICETOOL is often affectionately referred to as the “Swiss Army Knife” of mainframes, and for good reason. It packs a multitude of functions under its hood, allowing users to perform complex data operations using simple and intuitive commands.

  1. Sorting and Merging: Sorting data is a fundamental operation in data processing, and ICETOOL excels at it. You can sort data in ascending or descending order, based on one or multiple fields. Merging data from multiple sources into a single, organized dataset is a breeze with ICETOOL.
  2. Data Selection: ICETOOL helps you filter data efficiently. You can extract specific records that meet certain criteria, making it a powerful tool for data analysis.
  3. Summarization: Aggregating data is another forte of ICETOOL. You can generate summary reports, calculate totals, and perform statistical analyses on your mainframe data.
  4. Data Transformation: ICETOOL allows you to reformat and transform data into various formats. It’s handy for tasks like converting dates, numeric values, and text fields.
  5. Duplicate Removal: Identifying and removing duplicate records from large datasets is a common data cleaning task. ICETOOL’s deduplication capabilities simplify this process.
  6. Data Set Operations: ICETOOL supports set operations like union, intersection, and difference, making it easier to work with multiple datasets.

ICETOOL in Action

Let’s walk through a practical example of how ICETOOL can be used. Imagine you’re working in a banking institution, and you need to generate a report of all customers who have made transactions exceeding $10,000 in the last month.








Sample Code

In this example, ICE TOOL is used to select records from an input dataset (‘IN’) and write the matching records to an output dataset (‘OUT’). The selection criteria are based on the fourth numeric field, which is assumed to contain transaction amounts.

Photo by Christina Morillo on Pexels.com

How to use ICETOOL to remove duplicates

ICETOOL is a data processing tool that can be used to remove duplicate records from a dataset. It is part of the IBM Data Studio suite of tools and is available for use on z/OS systems.

To remove duplicate records from a dataset using ICE TOOL, you can use the following steps:

  1. Sort the dataset: Use the DFSORT utility to sort the dataset on the fields that you want to use to identify duplicates.
  2. Use the SELECT operator: Use the SELECT operator to specify which records to keep and which records to discard. You can use the ALLDUPS, NODUPS, HIGHER, LOWER, FIRST, FIRSTDUP, LAST, EQUAL, and VSAMTYPE options to control how duplicates are handled.
  3. Save the output dataset: Save the output dataset that contains the records that you want to keep.

Here is an example of how to use ICETOOL to remove duplicate records from a dataset:

INCLUDE COND=(20,10,CH,EQ,'A',AND,21,10,CH,EQ,'B')

This JCL will sort the dataset on the fields 1-5 (character fields) and only include records where the values in fields 20 and 21 are ‘A’ and ‘B’, respectively.


This JCL will use ICE TOOL to select all duplicate records from the INDD dataset and write them to the OUTDD dataset. The SAVEDD dataset will contain the records that are not duplicates. For more information on how to use ICETOOL to remove duplicate records, please refer to the IBM z/OS Data Studio documentation.

Alternatives to ICETOOL

While ICETOOL is a powerful and versatile utility for data manipulation on mainframes, there are alternative tools and approaches available for various data processing tasks. These alternatives can be useful depending on your specific needs and preferences. Here are some alternatives to ICETOOL:

  1. SORT Utility: The standard SORT utility on mainframes, often referred to as DFSORT or SyncSort, is a powerful alternative to ICETOOL for sorting and merging datasets. It offers extensive sorting options and can handle complex data manipulation tasks.
  2. AWK: AWK is a text processing tool available on many Unix-based systems. It’s a scripting language that allows you to write custom data processing scripts. While not exclusive to mainframes, it can be a versatile choice for data manipulation when working with text-based data.
  3. Perl: Perl is a general-purpose scripting language known for its text processing capabilities. It can be used for data extraction, transformation, and reporting on mainframes if Perl is installed and configured on the system.
  4. SQL: If your mainframe environment supports Structured Query Language (SQL), you can use SQL queries to perform various data operations, including data selection, aggregation, and joining tables.
  5. COBOL Programs: COBOL is a programming language commonly used on mainframes. You can write COBOL programs to handle complex data processing tasks, making it a flexible and customizable alternative to utility programs.
  6. Rexx: Rexx is a scripting language that’s well-suited for automation and data manipulation tasks on mainframes. It’s known for its simplicity and ease of use.
  7. Mainframe-Specific Tools: Depending on the mainframe environment and software stack you’re working with, there may be specific tools and utilities designed for data processing tasks. These tools may have unique features tailored to your organization’s needs.
  8. Custom Scripts: Mainframe professionals often create custom scripts or programs in languages like JCL (Job Control Language) to perform specific data processing tasks. These scripts can be highly specialized to meet the organization’s requirements.
  9. Third-Party Software: Some organizations use third-party software solutions that offer advanced data processing and analytics capabilities on mainframes. These solutions often provide user-friendly interfaces and may integrate with other systems.

When choosing an alternative, consider factors such as your specific data processing requirements, the tools available in your mainframe environment, and your familiarity with the chosen tool or programming language. Each option has its strengths and may be more suitable for certain tasks or preferences.


ICETOOL may not have the glamour of modern data processing tools, but its enduring presence in the mainframe world is a testament to its reliability and efficiency. It continues to be the go-to utility for mainframe professionals who need to tame vast volumes of data.

So, the next time you hear someone mention ICE TOOL in the context of mainframes, you’ll have a glimpse into the versatile, dependable, and slightly nostalgic world of this Swiss Army Knife of mainframe data management.

Leave a Comment