What is the Difference Between UNION and UNION ALL?
Data and databases are foundational aspects of the modern world, particularly in business. Organizing large bodies of data and rendering it intuitively retrievable has, for that reason, been an ongoing process within the programming community. No two databases are quite alike, as the data they house inherently differ, as does the data’s intended purpose. Nevertheless, managing large volumes of information amounts to complex work of an ongoing nature. Doing so requires a strong knowledge of the material being organized, a clear understanding as to how that material will eventually be employed, and a powerfully programmed language with which to realize said organization and employment.
Fostering Database Interconnection
Financial records, applicant information, polling results, demographic figures – these are but a few of the types of data which various firms and official operations are required to accurately store, safely retain, and ultimately access when necessary. The sheer diversity of information categories in play suggests that a dynamic approach to this process is plainly necessary. And what about the ways in which distinctive bodies of aggregated data interconnect with one another? As it happens, there exist decades’ old practical applications and functional theories as to how this interconnection should best be governed. These led to the rise of programming languages formulated with the express intent of streamlined database management.
Perfecting the Database Language
Refining the language of database management has, for years, been the charter of skilled programmers. Among the earliest and most enduring of results to stem from that charter was the advent of Structured Query Language (or SQL). SQL was engineered with relational data stream management systems in mind and has been indispensable within that field since its inception. Like all of the most durable and omnipresent of programming languages, SQL is characterized by intuitive usage and an accessible underlying concept. Put simply, it works…and well. Storing, manipulating, and retrieving data are vital functions in the Digital Age, all of which are highly manageable within the SQL linguistic framework.
SQL can render sizable bodies of data retrievable without requiring unique coding for that purpose. The interface allows for cross-medium engagement with various databases applications and configurations. It is an accessible language used by companies and individuals across the computing and business spectrum for the increasingly important work of database management, and it is generally well-received in the Information Technology industry.
Effective Organization of Data-Sets and Fields
HR directors, sales leaders, and finance professionals have benefited tremendously from the presence of SQL software within their IT arsenals, and for the obvious reason that the industries in which they operate require the joining of disconnected data-sets for reasons of reporting and analysis. And while SQL certainly goes a long way towards rendering such data retrievable, the user must nevertheless know what information they need, what they can do without, and how their reports should be rendered. For its part, SQL provides the end-user with various concatenation options, which is programmer-speak for ways of joining together data fields and/or sets as necessity dictates.
Which brings us to the topic of SQL UNION vs UNION ALL.
Data administrators, CIOs, and IT-focused professionals of many sorts will frequently find it necessary to merge data from separate sources. As simple as that process may sound, it is in fact rather complex. Even when the effort is undertaken via SQL, the data-sets being joined must bear some resemblance to one another in terms of coding format for the process to be successful. They must, in other words, be structurally compatible tables. From there, it is a matter determining whether the UNION or UNION ALL command is appropriate for your data merging situation.
Correctly Utilizing UNION and UNION ALL Operators
As a database management software program, SQL is specifically geared towards combining data-sets. However, data often overlaps between one information aggregate and another. That much is to be expected. Where the UNION ALL vs UNION topic comes into play is in determining whether redundant data should be de-duplicated when generating a combined report. For certain reporting functions, redundancy may present a significant problem by creating confusion of one sort or another. In others, redundant data might be easily absorbed into and accounted for in the final report.
The Fundamental UNION/UNION ALL Distinction
If the aim is to eliminate redundant data, the SQL UNION command is necessary. This essentially applies the SELECT DISTINCT statement to the combined data-sets, thereby removing duplicate data entries.
Alternately, the UNION ALL operator joins two or more data-sets without performing the work of de-duplication. The finished result is combined data which may be populated with entry duplicates. This is a less intensive merging process, as sets/tables are simply merged regardless of overlapping data.
If the data-sets being merged are mostly or entirely distinctive in terms of the information they house, the SQL UNION ALL operator is almost certainly ideal. But where significant information overlap exists, it may be necessary to apply the UNION alternative to avoid potential misinterpretations in the reporting.