Query: Advanced Capabilities

Widest range of data connectivity
Process unlimited file sizes
Virtual columns implement dynamic transformations
Automated metadata conversion
Stage your data in a fraction of the time
Field definition capabilities
Data Relations implements star schema modeling
Build automated processes to work for you
Maintain existing security
Combine multiple tables

Widest range of data connectivity

Unlike many alternatives, Query is not limited to accessing your warehoused data. Instead, it specializes in directly connecting you with your native mainframe legacy data. Virtually any data file, regardless of complexity or native data type can be easily accessed and queried with our Windows tools.

No matter how complex your legacy data, Query creates a virtual SQL table (rows and columns) that allows you to query it, in real time, without copying the data.

Process unlimited file sizes

Unlike most PC based applications, Query is designed to work with the large files you typically encounter in a mainframe environment. It can process files of any size supported by the host system. There is no limit on the number of rows or columns that can be accessed or processed by Query.

Since Query is highly optimized for speed, you will generally get surprisingly prompt responses, even with large data sets.

Virtual columns implement dynamic transformations

When creating table definitions, you are not limited to describing the physical data. You can also transform and present any calculation or series of calculations as “virtual” columns that fit our needs. For example, if names in your mainframe file are stored as “SMITH, JOHN P” and you need them to appear as “John P. Smith”, no problem. A simple virtual column will process this transformation dynamically, without altering the source data. You can transform or combine columns in any way you wish with Arbutus’s extensive library of operators and functions.

Best of all, you can create entirely new virtual columns that display information not even present in the source data. Add, subtract, multiply or divide two or more columns, use tests to interpret transaction codes, or deal with negative and out-of-range values. Convert a date column to a text column, or use a data mask to present phone numbers in the preferred local format. The more than 80 built-in data manipulation functions allow almost unlimited control over the presentation of data.

Arbutus’s comprehensive semantic and transformation layers allow the implementation of virtually any data interface or business rule. You are not limited to merely exposing the data, but can actually fit it to your needs.

Automated metadata conversion

Create appropriate data definitions using the Arbutus automated metadata conversion capability. Data descriptions stored in COBOL or PL/1 format are automatically converted into the appropriate format for use with Query. This conversion not only converts the physical field definitions, but also preserves the LEVEL 88 field definitions as well as any REDEFINES or OCCURS clauses. These converted definitions may then be modified as desired to remove, hide or rename any of the field definitions, as well as to add new ones, for use in the application.

Stage your data in a fraction of the time

When mainframe resource constraints make direct access solutions impractical, Arbutus enables you to easily offload your critical processing to open servers.

Unlike typical open server implementations, Arbutus’s open servers (Windows and Linux) are fully compatible with all data set structures and data types encountered on your mainframe. This means that the process of staging data is as simple as a file transfer, with no data transformation required.

The result is you can implement a data mart style query solution in a matter of days.

Field definition capabilities

Combine data from virtually any record type or field type. Query can handle more than fixed length printable files; it also supports variable length and multiple record type files. Even more, the field type support for over 20 distinct field types means any field type down to the bit level can be supported and included in table data (for example, not just ASCII and EBCDIC, but hexadecimal or half-byte characters as well). Redefined or overloaded data values in the same physical space can easily be isolated into distinct values. You can also create or recognize arrays of fields or field groups.

Data Relations implements star schema modeling

Use Arbutus’ Data Relations capability to join any number of disparate data in a star schema model, so that their data appear in a single logical view. Where the key values are structured differently between data files, expressions and virtual columns can be used to dynamically harmonize the values.

Build automated processes to work for you

When you are ready to move beyond ad hoc analyses, Query includes a powerful scripting language that gives you full access to its commands and functions. If you need to run a procedure more than once or twice, scripts will save time and ensure consistent repeatable results. The scripting language is easy to learn, and in most cases scripts can be created using the Windows interface.

In addition to the query commands, automated processes offer a range of processing optimizations, to further improve performance. As well, user interactivity can be included, so that you can specify query parameters at the time the process is run.

Maintain existing security

Query ensures that existing mainframe security is maintained and respected. All existing security protocols (such as TSO and RACF) are maintained in all interactions with mainframe data – at all times users can only access data for which they have the appropriate security rights.

Combine multiple tables

Often the most important questions are those involving more than one data source. With the Query’s Data Relations feature, a simple click and drag allows you to join tables “virtually”, creating a unified view of the data.

You can also take columns from two or more tables to create a new joined copy.

Finally, you can append rows from one table to another, extract selected rows, or create summary tables based on selected columns from one or more existing tables.

Experts in data structures will be pleased to see that the technology can handle the most complex operations involving redefined or overloaded data, multiple record-types and variable record-length files.